[Issue]: conv2d crashes with certain out_channels in PyTorch2.5+ROCm6.2.3

Problem Description
Environment
PyTorch 2.5.0+rocm6.2
Description
When using Conv2d with certain out_channels values (e.g., 16), the following error occurs:

MIOpen(HIP): Warning [hip_mem_get_info_wrapper] hipMemGetInfo error, status: 1
:0:rocdevice.cpp            :2984:<PHONE_NUMBER>902 us: [pid:196431 tid:0x7f2301dff640] Callback: Queue 0x7f2301400000 aborting with error : HSA_STATUS_ERROR_MEMORY_APERTURE_VIOLATION: The agent attempted to access memory beyond the largest legal address. code: 0x29
Aborted (core dumped)

However, other values for out_channels (such as 32) do not trigger this issue. This problem is not present in the earlier ROCm version (PyTorch 2.5.0 + ROCm 6.1).
Operating System
Ubuntu 22.04
CPU
AMD EPYC 73F3
GPU
AMD Instinct MI250
ROCm Version
ROCm 6.2.3
ROCm Component
No response
Steps to Reproduce
Crashed
import torch
import torch.nn.functional as F
F.conv2d(torch.ones(1, 3, 32, 32).cuda(), torch.ones(16, 3, 3, 3).cuda(), padding=1, stride=1)

Normal
import torch
import torch.nn.functional as F
F.conv2d(torch.ones(1, 3, 32, 32).cuda(), torch.ones(32, 3, 3, 3).cuda(), padding=1, stride=1)

(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
ROCk module version <IP_ADDRESS>.20 is loaded
HSA System Attributes
Runtime Version:         1.14
Runtime Ext Version:     1.6
System Timestamp Freq.:  1000.000000MHz
Sig. Max Wait Duration:<PHONE_NUMBER>3709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model:           LARGE
System Endianness:       LITTLE
Mwaitx:                  DISABLED
DMAbuf Support:          NO
==========
HSA Agents

Agent 1

Name:                    AMD EPYC 73F3 16-Core Processor
Uuid:                    CPU-XX
Marketing Name:          AMD EPYC 73F3 16-Core Processor
Vendor Name:             CPU
Feature:                 None specified
Profile:                 FULL_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        0(0x0)
Queue Min Size:          0(0x0)
Queue Max Size:          0(0x0)
Queue Type:              MULTI
Node:                    0
Device Type:             CPU
Cache Info:
L1:                      32768(0x8000) KB
Chip ID:                 0(0x0)
ASIC Revision:           0(0x0)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   3500
BDFID:                   0
Internal Node ID:        0
Compute Unit:            16
SIMDs per CU:            0
Shader Engines:          0
Shader Arrs. per Eng.:   0
WatchPts on Addr. Ranges:1
Memory Properties:
Features:                None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    528221812(0x1f7c0674) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
Pool 2
Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size:                    528221812(0x1f7c0674) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
Pool 3
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    528221812(0x1f7c0674) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
ISA Info:

Agent 2

Name:                    AMD EPYC 73F3 16-Core Processor
Uuid:                    CPU-XX
Marketing Name:          AMD EPYC 73F3 16-Core Processor
Vendor Name:             CPU
Feature:                 None specified
Profile:                 FULL_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        0(0x0)
Queue Min Size:          0(0x0)
Queue Max Size:          0(0x0)
Queue Type:              MULTI
Node:                    1
Device Type:             CPU
Cache Info:
L1:                      32768(0x8000) KB
Chip ID:                 0(0x0)
ASIC Revision:           0(0x0)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   3500
BDFID:                   0
Internal Node ID:        1
Compute Unit:            16
SIMDs per CU:            0
Shader Engines:          0
Shader Arrs. per Eng.:   0
WatchPts on Addr. Ranges:1
Memory Properties:
Features:                None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    528473232(0x1f7fdc90) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
Pool 2
Segment:                 GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size:                    528473232(0x1f7fdc90) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
Pool 3
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    528473232(0x1f7fdc90) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:4KB
Alloc Alignment:         4KB
Accessible by all:       TRUE
ISA Info:

Agent 3

Name:                    gfx90a
Uuid:                    GPU-4007a1a5b32f6606
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    2
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   12544
Internal Node ID:        2
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 4

Name:                    gfx90a
Uuid:                    GPU-52502d2da1dea260
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    3
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   13312
Internal Node ID:        3
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 5

Name:                    gfx90a
Uuid:                    GPU-653e18d19b439d5a
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    4
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   4352
Internal Node ID:        4
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 6

Name:                    gfx90a
Uuid:                    GPU-9b11638d619cbb47
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    5
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   5120
Internal Node ID:        5
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 7

Name:                    gfx90a
Uuid:                    GPU-5a76590cbb8066eb
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    6
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   44544
Internal Node ID:        6
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 8

Name:                    gfx90a
Uuid:                    GPU-e8c729e8113c9eb7
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    7
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   45824
Internal Node ID:        7
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 9

Name:                    gfx90a
Uuid:                    GPU-1e9de07e91e5f097
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    8
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   36352
Internal Node ID:        8
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32

Agent 10

Name:                    gfx90a
Uuid:                    GPU-51135bae6866f251
Marketing Name:          AMD Instinct MI250X/MI250
Vendor Name:             AMD
Feature:                 KERNEL_DISPATCH
Profile:                 BASE_PROFILE
Float Round Mode:        NEAR
Max Queue Number:        128(0x80)
Queue Min Size:          64(0x40)
Queue Max Size:          131072(0x20000)
Queue Type:              MULTI
Node:                    9
Device Type:             GPU
Cache Info:
L1:                      16(0x10) KB
L2:                      8192(0x2000) KB
Chip ID:                 29708(0x740c)
ASIC Revision:           1(0x1)
Cacheline Size:          64(0x40)
Max Clock Freq. (MHz):   1700
BDFID:                   37632
Internal Node ID:        9
Compute Unit:            104
SIMDs per CU:            4
Shader Engines:          8
Shader Arrs. per Eng.:   1
WatchPts on Addr. Ranges:4
Coherent Host Access:    FALSE
Memory Properties:
Features:                KERNEL_DISPATCH
Fast F16 Operation:      TRUE
Wavefront Size:          64(0x40)
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Max Waves Per CU:        32(0x20)
Max Work-item Per CU:    2048(0x800)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
Max fbarriers/Workgrp:   32
Packet Processor uCode:: 63
SDMA engine uCode::      8
IOMMU Support::          None
Pool Info:
Pool 1
Segment:                 GLOBAL; FLAGS: COARSE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 2
Segment:                 GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 3
Segment:                 GLOBAL; FLAGS: FINE GRAINED
Size:                    67092480(0x3ffc000) KB
Allocatable:             TRUE
Alloc Granule:           4KB
Alloc Recommended Granule:2048KB
Alloc Alignment:         4KB
Accessible by all:       FALSE
Pool 4
Segment:                 GROUP
Size:                    64(0x40) KB
Allocatable:             FALSE
Alloc Granule:           0KB
Alloc Recommended Granule:0KB
Alloc Alignment:         0KB
Accessible by all:       FALSE
ISA Info:
ISA 1
Name:                    amdgcn-amd-amdhsa--gfx90a:sramecc+:xnack-
Machine Models:          HSA_MACHINE_MODEL_LARGE
Profiles:                HSA_PROFILE_BASE
Default Rounding Mode:   NEAR
Default Rounding Mode:   NEAR
Fast f16:                TRUE
Workgroup Max Size:      1024(0x400)
Workgroup Max Size per Dimension:
x                        1024(0x400)
y                        1024(0x400)
z                        1024(0x400)
Grid Max Size:<PHONE_NUMBER>(0xffffffff)
Grid Max Size per Dimension:
x<PHONE_NUMBER>(0xffffffff)
y<PHONE_NUMBER>(0xffffffff)
z<PHONE_NUMBER>(0xffffffff)
FBarrier Max Size:       32
*** Done ***
Additional Information
No response

Hi @hann-wang, I wasn't able to reproduce this on a MI210 system with ROCm 6.2.3 and torch 2.3.0/2.5.0/2.6.0. I will try getting my hands on a MI250, in the meantime, could you try to reproduce this issue on the rocm/pytorch docker image? This image has torch 2.3.0/ROCm 6.2.3 pre-installed and will rule out any configuration issues. You can launch a container with the image using the following
docker pull rocm/pytorch:latest

docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
--device=/dev/kfd --device=/dev/dri --group-add video \
--ipc=host --shm-size 8G rocm/pytorch:latest

Hi @harkgill-amd, I can confirm that rocm/pytorch:latest image runs smoothly without issues on MI250. I have got a server equipped with MI210 and this issue persists.
This might be kernel-related, as there is a known issue running hipMemGetInfo on servers using older Linux kernels. The current kernel versions are 5.4.0-173-generic with amdgpu-core=22.20.50200-1438746~20.04 (for the MI210 host) and 5.15.0-41-generic with amdgpu-core=1:6.1.60101-1769056.22.04 (for the MI250 host).
Bellow is the Dockerfile for my working environment.
FROM rocm/dev-ubuntu-22.04:6.2-complete

RUN apt-get update && \
    apt-get install -y wget && \
    wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null && \
    echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' | tee /etc/apt/sources.list.d/kitware.list >/dev/null && \
    apt-get update && \
    apt-get install -y vim build-essential cmake git git-lfs python-is-python3 python3-dev rust-all clang-format ccache

RUN pip3 install --no-cache-dir netron pyfma safetensors tabulate tqdm eliot jupyter notebook "numpy>=1.26.4,<2.0.0" \
    "regex>=2024.5.15" huggingface_hub datasets onnxoptimizer pylance yapf "esbonio>=0.12.0" \
    onnxsim rich scipy sentencepiece zstandard pytest ninja onnx

RUN pip3 install --no-cache-dir "torch>=2.5.1" torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2

RUN mkdir /workspace

WORKDIR /workspace

Upgrading rocm-core, upgrading PyTorch to 2.5.1 and installing kdb files for pytorch wheels did not resolve this issue. However, I have tested PyTorch 2.5 with the ROCm6.1 image and it works well on both MI250 and MI210 servers.

The amdgpu-core version on the MI210 machine is pointing to an older ROCm version, 5.2. It's likely that an older amdgpu-dkms is also present on the system, potentially causing the issue.

amdgpu-core=22.20.50200-1438746~20.04

Could you please try removing the host's ROCm installation with
sudo amdgpu-install --uninstall --rocmrelease=all
sudo apt purge amdgpu-install
sudo apt autoremove

Then reinstall ROCm for Ubuntu 20.04 with
wget https://repo.radeon.com/amdgpu-install/6.2.2/ubuntu/focal/amdgpu-install_6.2.60202-1_all.deb
sudo apt install ./amdgpu-install_6.2.60202-1_all.deb
sudo apt update
sudo apt install amdgpu-dkms rocm

After doing this, could you please confirm that the amdgpu-core/amdgpu-dkms version is updated and try to rerun the reproducer on the rocm/pytorch docker image?

I don't have sudo privileges, so I've reached out to IT support to upgrade one of the servers, which may take some time. The MI250 server currently has ROCm 6.1 on host, and the issue persists there as well. Although the ROCm library is stated to be compatible with kernel drivers within a ±2 version range, I suspect there may be a compatibility issue in ROCm 6.2 affecting backward compatibility.

Update: One of the MI250 servers has been upgraded to Ubuntu22.04 kernel 5.15.0-97-generic with amdgpu=1:6.2.60202-2041575.22.04, and this issue persists.

GPU core dump failed
:0:rocdevice.cpp            :2984:<PHONE_NUMBER>8 us: [pid:8     tid:0x7f53d8bff640] Callback: Queue 0x7f53d8200000 aborting with error : HSA_STATUS_ERROR_MEMORY_APERTURE_VIOLATION: The agent attempted to access memory beyond the largest legal address. code: 0x29
Aborted (core dumped)

Hi @harkgill-amd, I've traced the issue back to the permissions of the home directory. When I mount a local disk directory as the home directory in Docker, the conv2d function operates without issues. However, mounting a directory on NFS as the home directory causes conv2d to crash for certain out_channels. I suspect this is due to home directory permissions since I noticed that files are being written to ~/.cache and ~/.config, which may not have the necessary write permissions on NFS.

Thanks for the update, and I’m glad to hear the issue has been resolved. At a quick glance, it does seem that pytorch uses ~/.cache/torch/hub to store models. I do see some documentation regarding using the TORCH_HOME or XDG_CACHE_HOME environment variables to alter the caching directories, maybe these could be of some use. Will close out the ticket as the root cause is unrelated.


Sequential Monte Carlo

@petergchang and I started to implement SMC based on some existing code from my lab and his implementation of the bootstrap PF. So far, it runs! And the results aren't crazy, but we haven't tested them fully yet. We wouldn't be surprised if there are some off-by-one errors or potentially bugs in the marginal_loglik calculation around resampling.
Remaining to-do's:

[ ] Test that marginal_loglik recovers true value on LGSSM with lots of particles.
[ ] Write a bootstrap particle filter wrapper
[ ] Extend the demo a bit. Add some text and math.
[ ] Implement a particle smoother.
[ ] Add more tests for other resampling methods, etc.
[ ] Try it on a model that takes in covariates.
[ ] Optional: implement fancier resampling methods (e.g. stratified)

CC @andrewwarrington and @dieterichlawson.

This looks great!
I would rename smc to smc_ssm.
Also I think a few comments explaining some of the math may be helpful.
You could potentially refer to the equations in ch 13 of my book.
eg for propose_and_weight in https://github.com/probml/ssm-jax/pull/136/files#diff-d3b2fd6622444eb938a69d8e8070f4dc20f8d2c446a987bbec492fb4cc4f3ac9R157,
you could say
# incremental_weight(z(1:t)) = obs_model(y(t) | z(1:t)) * trans_model(z(t) | z(1:t-1)) / proposal(z(t) | z(1:t-1))
# see eqn 13.21 of probml.github.io/book2

When the code is ready, we should port these JSL demos over

https://github.com/probml/JSL/blob/main/jsl/demos/bootstrap_filter.py (fig 13.1)
https://github.com/probml/JSL/blob/main/jsl/demos/bootstrap_filter_maneuver.py (fig 13.6)
https://github.com/probml/JSL/blob/main/jsl/demos/rbpf_maneuver.py (fig 13.6, Uses mixture of Kalman filters)

It would also be great to add a likelihood tempering demo (as an alternative to HMC),
to illustrate SMC for a non-SSM, eg
https://github.com/probml/pyprobml/blob/master/notebooks/book2/13/smc_tempered_1d_bimodal.ipynb

Regarding resamplings, please have a look at what I did in Blackjax

Thanks for the pointer. This is just a start but we certainly plan to add other resampling methods.

Also note that the killing resampling has been getting some traction for "sticky" problems related to SDEs
https://arxiv.org/abs/2203.10037, https://arxiv.org/abs/2205.13898, if you are into this kind of things

I'll take a closer look at your BlackJax SMC code though — somehow I missed it!
We're already planning to use BlackJax for HMC in this library. Maybe we can use it for SMC as well, rather than having yet another SMC implementation. I do have dreams of adding FIVO/VMSC/SIXO. Do you know if BlackJax has any plans to implement such methods eventually @AdrienCorenflos?

Technically, they are implemented by default, given that the biased REINFORCE trick used in FIVO and friends is exactly the backpropagation ignoring the resampling selection step.

It's the first time I see your SIXO paper though, so not sure for this one.

By the way, any reason why you don't consider https://arxiv.org/abs/2102.07850 in your SIXO paper?

Good point! FIVO/VSMC with biased grads are pretty simple if you get reparameterization grads of the SMC log normalizer estimate. Our existing implementations also have unbiased gradient estimators with control variates, but in practice that rarely works as well.
SIXO adds a little twist (terrible pun, sorry) with learned targets and contrastive estimation. I guess in all cases there's the machinery for proposal networks and stuff, but that's pretty straightforward compared to the model usually.
R/e DPF, really cool work! We studied that in some detail and even played around with some ideas for extensions. In our hands, we found that solving OT problem in the inner loop was considerably more expensive, and we got some strange results with DPF on the Gaussian drift toy example. I'll ask Dieterich et al to follow up with you over email.

Ok, I was wondering if this has something to do with the fact you used smoothing. I caressed the idea of doing an OT based particle smoother for a while, but the fact that the concept of genealogy is destroyed by the push forward actually makes it rather hard.

FWIW I like the idea of using blackjax SMC for particle filtering, rather than rolling our own, so long as it supports the use cases of interest (eg EKF proposals). Also I'm not sure how easy/hard it would be to add RBPF for SLDS.

I'm going to close this since it's not a v1 feature. We can always reopen later if we want, but it seems like we could use other libraries for SMC support.


change timeValue cluster settings to string type

LONG is wrong as they contain the unit. For example '30m' isn't a number

ack


Cannot get schem from GraphQL-dotnet server

I am running the latest version of graphql-dotnet and trying to hit it via the cli's get-schema.  It keeps returning the following error: Error trying to resolve type.: { long response }.

Here is the error section of the response
{
"errors":[{"message":"Error trying to resolve type.","locations":[{"line":31,"column":7}]}],"status":400}
}

The issue ended up being a mutation field that was of ObjectGraphType (void mutation).

Thanks for providing the answer to this. Closing this unless someone else is also having related problems.


[SPARK-34558][SQL][FOLLOWUP] Do not fail Spark startup with a broken FileSystem

What changes were proposed in this pull request?

This is a followup of https://github.com/apache/spark/pull/31671
https://github.com/apache/spark/pull/31671 qualifies the warehouse at the beginning, which may fail Spark startup if something goes wrong, like the underlying FileSystem can't be initialized.
This PR falls back to the old behavior and leave the warehouse path unqualified if qualifying fails.
Why are the changes needed?

Fix a regression. It's important to be always able to start Spark app (e.g. spark-shell), so that we can debug.
Does this PR introduce any user-facing change?

No
How was this patch tested?

a new test case

cc @yaooqinn

Test build #138807 has started for PR 32622 at commit 82924f7.

Kubernetes integration test unable to build dist.
exiting with code: 1
URL: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder-K8s/43329/

Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder-K8s/43329/

For the thrift server, could this change lead an unhealthy server for end-users?

For the thrift server, could this change lead to creating an unhealthy server for end-users?

At least it's the same as before, not worse.

LGTM

Test build #138807 has finished for PR 32622 at commit 82924f7.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/138807/


Solution Test Plan definition

Reviewers:
@jputrino for organization and integration into RTD
@jlongstaf for content
Background:
This doc is the public version of an internal test plan that strips out specific hardware/software requirements to allow easy consumption by the the community.  A follow-on story will overhaul the README.rst to provide the "tool kit" to facilitate execution of F5 tests.
Issues:
Fixes #360
Problem:
Tests are available via github, but F5 supports multiple ways of deploying equipment and OpenStack.  Partners, customers, and contributors need guidance on how to deploy the test environment prior to executing tests.
Analysis:
Create a lightweight document laying out expectations, pre-reqs, etc. that will grow over time as we add additional qualified use cases.
Tests:
Review of documentation on fork. Generated docs using sphinx to inspect content and external links.

I added some section headers to the supported templates in the Heat repo that will make linking to them a bit easier. Opening a PR for that update shortly.

Here are the tags you can reference for the heat templates:
cluster-prep - F5 BIG-IP VE: 2-Device Clustering Prep
ve2nic - F5 BIG-IP VE: Standalone, 2-nic
ve3nic - F5 BIG-IP VE: Standalone, 3-nic
active-standby-cluster - F5 BIG-IP: Active-Standby Cluster
ve-image - F5 BIG-IP VE: Image Patch & Upload
4nic-cluster-ready - F5 BIG-IP VE: Cluster-Ready, 4-nic
deploy-lb - Deploy Basic Load Balancer

All of the section tags are now in the heat repo, on all branches.

All comments addressed.  Please re-review (the 2nd commit).


LEGO: Pull request from lego/hb_04604851-bac4-4681-9f74-73de611d6e48_20210916153335914 to main

LEGO: Pull request from lego/hb_04604851-bac4-4681-9f74-73de611d6e48_20210916153335914 to main with localized lcls

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 41.663%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
26427

Relevant Lines:
57452

💛  - Coveralls


As the porters organisation, I want to have certainty over the alignment with RaidGuid.

[x] https://github.com/porters-xyz/gateway-demo/issues/119

Create post in the Raid Guild discord of our intention to have this talk during call next thursday 3/14


Adds a patch for the previous CH vat commit

Ok I applied the previous commit as a patch now. Hope I did everything correctly.

I meant apply the patch to your composer.json, there's nothing I can do with this.

Ok I will do that. Sorry, I'm not too familiar with contributing to public projects. This was my first pull request actually ;). Cheers!


Another attempt at xds proxy log cleanup

Results, assuming https://github.com/envoyproxy/envoy/pull/14616#pullrequestreview-565567086 removes envoy logs for status code=0
# Initial connection
2021-01-11T18:05:53.451244Z     info    xdsproxy        connected to upstream XDS server: localhost:15012

# Keep alive triggered (if envoy pr merges, the StreamAggregatedResources log will be hiddden)
2021-01-11T10:06:14.309738Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 0,
2021-01-11T18:06:14.759961Z     info    xdsproxy        connected to upstream XDS server: localhost:15012

# Failures (until eventual recovery)
2021-01-11T10:06:17.576631Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 14, connection error: desc = "transport: Error while dialing dial tcp [::1]:15012: connect: connection refused"
2021-01-11T10:06:18.262982Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 14, connection error: desc = "transport: Error while dialing dial tcp [::1]:15012: connect: connection refused"
2021-01-11T10:06:18.284543Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 14, connection error: desc = "transport: Error while dialing dial tcp [::1]:15012: connect: connection refused"
2021-01-11T10:06:19.577431Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 14, connection error: desc = "transport: Error while dialing dial tcp [::1]:15012: connect: connection refused"
2021-01-11T10:06:23.110776Z     warning envoy config    StreamAggregatedResources gRPC config stream closed: 14, connection error: desc = "transport: Error while dialing dial tcp [::1]:15012: connect: connection refused"
2021-01-11T18:06:27.502452Z     info    xdsproxy        connected to upstream XDS server: localhost:15012

@howardjohn: The following test failed, say /retest to rerun all failed tests:

Test name
Commit
Details
Rerun command

release-notes_istio
821ced1b0f530da817ccb3f4b573fe6a55930933
link
/test release-notes_istio

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. I understand the commands that are listed here.

@howardjohn: The following test failed, say /retest to rerun all failed tests:

Test name
Commit
Details
Rerun command

release-notes_istio
821ced1b0f530da817ccb3f4b573fe6a55930933
link
/test release-notes_istio

Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. I understand the commands that are listed here.

/ok-to-test

/ok-to-test

even for real failures, there is no reason to say connection refused 5 times. Is this PR and @ramaraochavali 's PR both going in?

even for real failures, there is no reason to say connection refused 5 times. Is this PR and @ramaraochavali 's PR both going in?

@mandarjog I have changed it so xdsproxy never reports the error, and it assumes Envoy will report the error instead. Currently this means we get 1 msg per error, if your envoy PR merges as-is we would get 1 message every X errors.
With ramas PR, envoy reports every error and xds proxy reports 1/10, so we get 1.1 messages per error. If your envoy PR also lands, then xdsproxy will report 1/10 errors and envoy will report 1/x errors, which I think is even more confusing?
Rama's PR already merged so this would be on top of it (need to rebase though)

@mandarjog I have changed it so xdsproxy never reports the error, and it assumes Envoy will report the error instead. Currently this means we get 1 msg per error, if your envoy PR merges as-is we would get 1 message every X errors.
With ramas PR, envoy reports every error and xds proxy reports 1/10, so we get 1.1 messages per error. If your envoy PR also lands, then xdsproxy will report 1/10 errors and envoy will report 1/x errors, which I think is even more confusing?
Rama's PR already merged so this would be on top of it (need to rebase though)

@mandarjog I switched it around to log the istiod successful connection, I think its less spammy and more clear. updated original description

@mandarjog I switched it around to log the istiod successful connection, I think its less spammy and more clear. updated original description


feat(convert): improve conversion handling

Signed-off-by: Ramkumar Chinchani<EMAIL_ADDRESS>
What type of PR is this?

Which issue does this PR fix:
What does this PR do / Why do we need it:
If an issue # is not available please add repro steps and logs showing the issue:
Testing done on this change:

Automation added to e2e:

Will this break upgrades or downgrades?
Does this PR introduce any user-facing change?:

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

I'm sorry, but could you please add a bit of explanation to patch "[PATCH 2/5] feat: support file-to-file import copy" ?

I'm sorry, but could you please add a bit of explanation to patch "[PATCH 2/5] feat: support file-to-file import copy" ?

import /dir1/file1 -> /dir2/file2 means that we need to setup a overlaydir dest=dir2 but we also need to copy file1 as file2 there first.


(styles): update tailwind to v3

In addition to updating the latest version and plugins, the following changes have been made:

[x] Update some specific colors (green, yellow, purple and gray)
[x] Update flex-grow and flex-shrink to new naming
[x] Remove theme and variants by default as there are no longer needed (on V3 everything is enabled by default)
[x] Remove purge (is no longer needed to remove unused styles)

Pakages: Don't use latest as the version to use
Colors: Instead of changing all the classnames in the components it would be better if you add the colors inside the config
const colors = require('tailwindcss/colors')

module.exports = {
  theme: {
    extend: {
      colors: {
        gray: colors.slate,
        green: colors.emerald,
        yellow: colors.amber,
        purple: colors.violet,
      }
    },
  },
  // ...
}

Why rename the colors and not adapt to the new names if we are going to use the scaffold for new projects?

Soved in https://github.com/Vizzuality/front-end-scaffold/pull/53


Option to make topnav smooth scroll?

I noticed the Magellan top nav will smooth scroll, but the regular topbar won't. Is there an easy way to make it do so?

This is something that is very prescriptive and can be added to top-bar easily with jQuery. We won't be adding it to the core framework however a code snippet example or tutorial would be better.


Reorder Rows: LongPressGestureRecognizer

Tap & Hold to reorder rows
https://github.com/xmartlabs/Eureka/issues/17#issuecomment-267990550

Please, do not spend time on this, at least in this first iteration. Let's add support to most common UIKit user interaction for now.


[BUG] fixing constructor non-compliance in various estimators

The following estimators fail the extended test_constructor from https://github.com/alan-turing-institute/sktime/pull/2732, along the lines of the previously undetected bug #2730 and need to be fixed.

[ ] ARIMA https://github.com/alan-turing-institute/sktime/pull/2731
[ ] AutoARIMA
[ ] AutoETS
[ ] KNeighborsTimeSeriesClassifier
[ ] KNeighborsTimeSeriesRegressor
[ ] PCATransformer
[ ] ShapeDTW
[ ] TimeSeriesKMeans
[ ] UnobservedComponents

FYI @juanitorduz re UnobservedComponents
FYI @chrisholder, @MatthewMiddlehurst re KNeighbors, ShapeDTW, TimeSeriesKMeans

Good first issue = "follow the pattern in https://github.com/alan-turing-institute/sktime/pull/2731"

remaining estimators fixed by https://github.com/alan-turing-institute/sktime/pull/2773


Examples of usages for IOptions

I introduces some easy scenarios for usages of IOptions.
I agree that it's a bit more code than the other 3 examples by @siroky.
But I really think that if anybody wants to use our fiscalization libraries, we need to give them some examples of how to work with and get values out of options and ITries. Not just nice examples of what it allows. I'd like to have those examples here rather than in the readme of the fiscal repository. Although, I could put it there, if you see this as a pollution of code.
Maybe we can discuss how deep I go into showcasing the usages. I didn't want to spend too much time on it.

@KaliCZ also added link to homepage: https://github.com/siroky/FuncSharp/blob/master/readme.md#option


Set network status to 3 with cache-and-network fetch policy

Intended outcome:
I want to show my users that the app is loading fresh data in the background when they get a render from the cache. (i.e. fetchPolicy is set to cache-and-network) In our app, this mostly happens when users navigate back to a page they've been on previously and we render the data from the cache first, while using cache-and-network to load fresh data from the server.
Actual outcome:
I can't do that because networkStatus does not expose that information.
Idea:
Rather than networkStatus 3 being specific to this.props.data.refetch(), why not also set the networkStatus to 3 when the data is refetched by the cache-and-network fetch policy? The data is being refetched (even if not by calling refetch()) so it makes sense for the networkStatus to reflect that reality.

➤ stale[bot] commented:
This issue has been automatically labled because it has not had recent activity. If you have not received a response from anyone, please mention the repository maintainer (most likely @jbaxleyiii). It will be closed if no further activity occurs. Thank you for your contributions to React Apollo!

➤ Max Stoiber commented:
Hah @stale take that, now I can label it myself! 🖕😆 (thanks @jbaxleyiii)
This might've been fixed with the Apollo Client 2.0 rewrite which would be awesome, but I can't confirm that as we haven't had time to upgrade yet.

➤ taikn commented:
Using Apollo Client 2.0,  I can confirm that networkStatus is still 1 while using cache-and-network fetch policy.


LL-9068 - Create how to fund and sell flow doc

Add a How to guide for the fund and sell flow
cf. https://diataxis.fr/how-to-guides/

Doc in dev portal https://developers.ledger.com/docs/non-dapp/howto/sell/


Homework11: Vladislav Savchuk

Decisions of tasks Homework 11

Library
Bank deposit

@Stml89, hello) Made a review, please)


Local Timezone

Display the date and times in the current Timezone of the browser

I'll pick this issue up.


After upgrading to angular 1.4.12 to 1.6.1 to cropper not working

1.it was worked fine previously when i am using angular 1.4.12 and when i upgrade it is not working .
2.unable to increase the the cropper (the increasing button not working).
3.The image is not placing in exactly in the canvas.
Am attaching the screens
(http://imgur.com/a/7fHQx)
(http://imgur.com/a/qECkU)

working fine bro you can close the issue.
i did mistake in css,other canvas css width :100% over writting .


Optionally Ignore unknown TrackTypes

Added an explicit typed exception to be caught in order to ignore unknown track types. Since you didn't have the Other (or Menu) section types implemented these will just throw exceptions making the library almost useless.
I also implemented the Other type as I wanted to use it. However Menu is still unimplemented, and any other strange sections MediaInfo might have in the future. My suggestion, make it ignore unknown track types by default rather than throw an exception. As you'll see I made the parameters to ignore optional and set to false, so it copies the current behavior.

Thanks for PR, LGTM, before merging could you:

Fix code style issue (travis-build)
Add some unit test
Add documentation

Ok added documentation for the parameters, some unit tests, and any other minor style fixes.

Thanks @Nicholi. Merge in master (relased in 1.3.0)


Add "show n most recent" feature

Adds an optional list_most_recent argument to list the most recent TILs below the description

Codecov Report

Merging #2 into master will increase coverage by 1.93%.
The diff coverage is 92.85%.

@@            Coverage Diff             @@
##           master       #2      +/-   ##
==========================================
+ Coverage   75.34%   77.27%   +1.93%
==========================================
  Files           1        1
  Lines          73       88      +15
==========================================
+ Hits           55       68      +13
- Misses         10       11       +1
- Partials        8        9       +1

Impacted Files
Coverage Δ

main.go
77.27% <92.85%> (+1.93%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Add PROXY_PERCENTAGE_OF_WHITEHALL_ASSET_REQUESTS_TO_S3_VIA_NGINX env var

This complements the change made to Asset Manager in PR 263.

Thanks @chrislo!


Q: Persisting models to Django ORM?

I am developing a library that handles data that is exchanged in JSON format. It shall be designed to be easily usable with Django, but not require it.
jsonmodels seems like a good fit to start the modelling process. Has anyone thought about how to link it to Django ORM, as in, optionally persist data in real Django models?

Hey @Natureshadow
If you are creating lib, of which jsonmodels would be a transport layer with optional Django support - then I suggest to put code (in the beginning at least) responsible for that in that very library (as jsonmodels does not have any integration with any framework for now).
As the lib grows - you could make this generic - and extract this as a plugin to your library (or jsonmodels?)
And diving into more details about

Has anyone thought about how to link it to Django ORM, as in, optionally persist data in real Django models?
I'd keep jsonmodels and Django agnostic - and build around them layer that translates/autogenerates models (field by field) - although this is complicated as you can have few approaches like keeping only one-level models (so they are easily translated) or cast deeper json structures as json field or as related model... Plus there is problem how to handle django migrations (especially that Django has to be optional).

So yeah, problem is complicated - you should start with as many constraints and assumptions as possible to make this easier :)


Translated all pages

Translated all pages
Missing the translation of some lines in developers.html

Okay! There is only a mistake in a description of developers page!


Drop dependecy pkg_resources required by kaitaistruct autogenerated code

Problem Description
pkg_resources is used here only to check kaitaistruct installed version. Kaitaistruct by itself doesn't use this package.
But this package also on import do a lot of other stuff, that slows down the code startup time and causes a lot of other problems in my project. So I've dropped it from mitmproxy at my local version.
Also, as I know, you build executable files. This would help to increase startup speed and decrease exe file size.
I know that there were already a lot of topics about this problem. But I don't see anything that stops us from this step (drop pkg_resources).
Starting from the kaitaistruct version 0.10, they've already dropped pkg_resources dependency.
You can read the related issue here:
https://github.com/kaitai-io/kaitai_struct/issues/804
This issue also solved your issue
https://github.com/kaitai-io/kaitai_struct_python_runtime/issues/62
Proposal
Drop pkg_resources dependency.
Alternatives
Because it's used only to check an installed kaitaistruct version there are some proposals:

Drop this checks and don't check kaitaistruct version at all. In the wheel it said: kaitaistruct (<0.10,>=0.7). So it would be strange if you try to use mitmproxy with some other kaitaistruct versions. For me it's a bit strange why do we check kaitaistruct package version, but don't check other dependencies versions.

Use our own version checker.
It would be better to check the version only once by moving it to the nearest init file. I'm ready to write a simple version checker based on regexp.

I'm ready to create a PR for any chosen alternative.
Additional context
There is only one minor issue. If you decide to recompile these files with an old version kaitaistruct (<0.10), this patch should be applied again. But if we're going to recompile them, shouldn't we use a newer version.
So let's finally solve this problem :)

Thanks for raising this! Ideally we'd just wait for a kaitai 0.10 release and then recompile, but not sure when this happens. For now I'd be happy to merge a PR that just removes the checks. As you say, we check the version anyway. We can add a
# manually removed version check, see https://github.com/mitmproxy/mitmproxy/issues/5401

comment instead to make sure it's not inadvertently reintroduced when someone recompiles with 0.9.

Closed by #5403


FIX?: CTF ref meg channels not handled properly in LCMV

Reconstructing CTF (271 mag + ref meg) using LCMV seems buggy. If I don't explicitly exclude the ref meg, the reconstruction gets dramatically influenced by the ref channels, and lead to seemingly absurd estimates.
I think this is an issue with the way the forward model is used by lcmv.
@romquentin, didn't you have a similar issue at some point?
cc @britta-wstnr @avkasdan
To reproduce the issue: data and code:
import mne
import numpy as np
import matplotlib.pyplot as plt
from mne.minimum_norm import apply_inverse_epochs, make_inverse_operator
from mne.cov import compute_covariance

# Setup paths and subject
subject = 'ESA28'

# Read in epochs and apply baseline
epochs = mne.read_epochs('%s_audio_None-30.0Hz-epo.fif' % subject)
epochs.apply_baseline((-1.000, -.900))

# Setup Forward model
bem_fname = '%s-5120-bem-sol.fif' % subject
src = mne.setup_volume_source_space(subject, pos=10, mri='T1.mgz',
                                    bem=bem_fname, subjects_dir='./')
fwd = mne.make_forward_solution(info=epochs.info, trans='%s_01-trans.fif' % subject,
                                src=src, ignore_ref=True, bem=bem_fname)

# Compute the noise and data covariance
# BUG don't exclude ref channel here, and you'll get crazy sources
epochs.pick_types(meg='mag', ref_meg=False)
noise_cov = compute_covariance(epochs, tmin=-1., tmax=-.900)
data_cov = compute_covariance(epochs, tmin=-.800)
epochs.decimate(3)

# Setup inverse operator
# FIXME loose='auto' doesn't work as it should
inv = make_inverse_operator(info=epochs.info, forward=fwd,
                            noise_cov=noise_cov, loose=1, depth=None)

stc = mne.beamformer.lcmv_epochs(epochs, fwd, noise_cov, data_cov,
                                 reg=0.05, pick_ori=None)

# Retrieve data to plot average
data = np.zeros_like(stc[0]._data)
for src in stc:
    data += src.data

plt.matshow(data)

The same code without epochs.pick_types(meg='mag', ref_meg=False)python, seems to follow the ref channels.

I did not see this bug. I usually exclude ref_meg.

This is indeed a bug within the lcmv code, thanks @kingjr . Working on the fix for it.


sometimes it will not cash when I use the watchdog

I use the watchdog in my app, why sometime it will crash when anr, but sometimes the android os anr dialog will appear(then it will not crash)?
I just code it by the default way:   new ANRWatchDog().start();

That's very weird.
do you start the watchdog conditionally ?

Actually no, sometimes it just doesn't crash the app.  I try it again today, use new code like this:
in the Application I init the watchdog:
new ANRWatchDog().setANRListener(
new ANRListener() {
@Override
public void onAppNotResponding(ANRError error) {
if (!DebugUtil.isDebuggerConnected()) {
throw error;
}
}
}).start();
Then I simulate an ANR in my MainActivity without debugger connected by "Thread.sleep(10000)", I still found it doesn't crash utils the ANR dialog appear, but just one time. I don't know the reason.

That should not happen.
I will investigate as soon as I get the time ;)

@HolenZhou If you wrap the code snippet in backticks (three before, three after) it will be formatted nicely and much more readable.

when I use suggested :
    new ANRWatchDog().start();
}``` I don't get a report to Acralyzer
but if I use just :
``` new ANRWatchDog().start();```

I get the report.
??

How do you launch the application ?

Can you test with v1.2.0 ?

Closing, after 8 month without reply


Modifying logic behind dhcp_options netbios validation

netbios node type = '2' is not a default value , rather it is the recommended value. Additionally, not all dhcp_options require the netbios_node_type to be specified. After initial provisioning subsequent runs puppet attempts to add the default value 2 for netbios_node_type, only to fail as it is a read-only (creation time only) value.
This change improves the description of the netbios_node_type property, and enforces the presence of said property when the netbios_name_servers property is in use. netbios_node_type is a requirement when netbios_name_servers is in use. Will fail when both are not specified.

adding to unit test to test for new behavior

+1

Updated the validation to be a bit more elegant, using self with attribute symbol as key.

If someone previously relied on the default of 2 then this would be backwards incompatible for them. We can roll it in as part of the next major bump release when we drop puppet 3.


DPDK: cleanup sriov enabled status in after-case

Make sure to clean up and re-enable SRIOV on the node in after_case. Defensive coding in the event the test fails while SRIOV is disabled.

I am interested in the root cause for when the AN disabled during testing, and why it was not set back after testing.

I am interested in the root cause for when the AN disabled during testing, and why it was not set back after testing.

This is more of a defensive step, if there's an unexpected error during the test when sriov is disabled it would be good to check and re-enable it after the test.
I've also started making sure to check and enable if needed at test start, since some of the SRIOV tests disable it and can potentially fail and leave it disabled. This is the second half to make sure my test doesn't unintentionally leave it disabled for other tests.
Since you pointed it out, I made a change to move the re-enable before the driver reloads, since re-enabling should be a 'safer' operation. Removed node.close call since the SRIOV re-enable will do this before the driver reloads are called.


NUM_FEW_SHOT does not correspond with the leaderboard

There seems to be a discrepancy between the leaderboard and this repository, which may end up meaning that models were benchmarked with different settings then reported.
Specifically, m_hellaswag seems to have specified to use 0 few shot examples even though the leaderboard says 10.
https://github.com/laiviet/lm-evaluation-harness/blob/10cb5292748e882c22db7eed49a380089645c4c2/lm_eval/tasks/multilingual_hellaswag.py#L48-L56
Similarly, MMLU has 5 in the leaderboard but 25 in the code.
https://github.com/laiviet/lm-evaluation-harness/blob/10cb5292748e882c22db7eed49a380089645c4c2/lm_eval/tasks/multilingual_mmlu.py#L44-L48
Where should it be corrected - on the leaderboard or in the code? And what are the consequences for the models that you report?

In the same way as @BramVanroy, the number of few-shot examples doesn't match, and I can't reproduce the results of the leaderboard.


token field removed, register api endpoint, register page

Closes #32
Closes #82

remove token field from manager doc
activated field added
registration html page
registration bot endpoint for creating account
manager should be activated before he will be able to use account
pulling manager photo and full name from actual data

replace options endpoints declaration by main single one with method as route param


[16795] Remove warning no longer applying

Since Fast DDS v2.1.0 DataReaderListener::on_requested_deadline_missed and DataWriterListener::on_offered_deadline_missed are implemented.
Signed-off-by: JLBuenoLopez-eProsima<EMAIL_ADDRESS>

@Mergifyion backport 2.1.x 2.6.x 2.7.x 2.8.x
(2.9.x I think it is mirrored automatically)


Deprecate random.shuffle() and implement random.permutation() for multi-dim inputs

Addresses #2066.
Motivation for deprecating shuffle:

the numpy implementation shuffles in-place, which JAX cannot emulate
numpy's permutation provides all the functionality of shuffle, but returns a copy rather than shuffling in-place.

Once a few releases have passed, we should make shuffle() raise a NotImplementedError that points to permutation as a viable replacement.

The core team's convention is that we merge our own PRs (after approval). I think you have commit rights now, so feel free to go ahead!

Thanks!


[AMBARI-23602] NN Federation Lower priority changes

What changes were proposed in this pull request?

The add metric in the HDFS Metrics needs more padding around it
Create metric - because of menu depth the name of the metric is hard to see, we should wrap them and do anything to make sure the metric names are easily readable
In the move master wizard we need to add the namespace before the word "NameNode", like "ns1 NameNode"

How was this patch tested?
21516 passing (28s)
48 pending

Refer to this link for build results (access rights to CI server needed):
https://builds.apache.org/job/Ambari-Github-PullRequest-Builder/1892/
Test FAILed.
Test FAILured.

retest this please

Refer to this link for build results (access rights to CI server needed):
https://builds.apache.org/job/Ambari-Github-PullRequest-Builder/1895/
Test PASSed.


Update to refactored tact-docs structure

The recent tact-docs refactoring https://github.com/tact-lang/tact-docs/pull/81 broke links in tact-by-example

This issue will be fully resolved in https://github.com/tact-lang/tact-docs/issues/86, although it may be nice to update the links to the newer ones here as well :)


Node-red crashes if port is already in use

It seems like the osc node makes node-red crash if you attempt to use a port that is already in use, see this error log:
15 Feb 13:05:34 - [red] Uncaught Exception:
15 Feb 13:05:34 - Error: bind EADDRINUSE
    at exports._errnoException (util.js:746:11)
    at dgram.js:224:28
    at dns.js:85:18
    at process._tickCallback (node.js:355:11)

I was getting very frustrated with this until I realized what was happening. For anyone wanting to fix this for the short-term, you need to edit your node-red flows file and remove the conflicting OSC node manually. Hopefully there's an eventual fix for this.

I applied your Pull Request and published a new version of the node.
Note that I renamed it to be consistent with the Node-RED packaging guide:
https://www.npmjs.com/package/node-red-contrib-osc
Thanks for reporting and fixing :)

Many thanks !


Bug: Lambda context access in batch processing record handler

Expected Behaviour
Based on the Python implementation, the record handler should be defined to accept a Context object in order to access the Lambda context.
Current Behaviour
Currently, the processor will pass the entire BatchProcessingOptions object to the handler, so if a handler is defined matching a Python handler, expecting a context parameter of type Context, the Context attributes and methods will be inaccessible.
Code snippet
const recordHandler = (record: SQSRecord, lambdaContext?: Context): void => {
  const payload = record.body;
  if (payload) {
    const item = JSON.parse(payload);
    logger.info('Processed item', { item });
  }
  if (lambdaContext) {
    logger.info('Remaining time', {
      time: lambdaContext.getRemainingTimeInMillis(),
    });
  }
};

Steps to Reproduce
This code is from the documentation example. When running the example, there will be an error that getRemainingTimeInMillis() is not a function.
Possible Solution
In order to have the code work by redefining the handler, it has to be something like this:
const recordHandler = (record: SQSRecord, options?: BatchProcessingOptions): void => {
    const payload = record.body;
    if (payload) {
        const item = JSON.parse(payload);
        logger.info('Processed item', { item });
    }
    if (options) {
        logger.info('Remaining time', {
            time: options.context.getRemainingTimeInMillis(),
        });
    }
};

Otherwise, the processor should be redefined to pass a Context object to the handler instead of a BatchProcessingOptions type object.
Powertools for AWS Lambda (TypeScript) version
latest
AWS Lambda function runtime
16.x
Packaging format used
npm
Execution logs
"msg": "All records failed processing. 1 individual errors logged separately below.",
    "childErrors": [
        {
            "errorType": "TypeError",
            "errorMessage": "e.getRemainingTimeInMillis is not a function",
            "stack": [
                "TypeError: e.getRemainingTimeInMillis is not a function",
                "    at xe.Ys [as handler] (/var/task/index.js:7:24441)",
                "    at xe.processRecord (/var/task/index.js:4:3142)",
                "    at xe.process (/var/task/index.js:4:978)",
                "    at kr (/var/task/index.js:5:88)",
                "    at Runtime.$s [as handler] (/var/task/index.js:7:24514)",
                "    at Runtime.handleOnceNonStreaming (file:///var/runtime/index.mjs:1083:29)"
            ]
        }
    ],

Hi Erika, thank you for opening this issue. From a first look in the codebase it seems that the issue is valid.
If you or anyone bumping into this is interested in proposing a PR, I'd be happy to help you get it merged. Otherwise we'll work on a fix before the next release later next week.


Compendia Does Not Include Submitter Processed Samples

When the changes in https://github.com/AlexsLemonade/refinebio/pull/2148 are merged will be no longer including submitter processed data in any of the compendia. It is my opinion that the docs already convey this but perhaps it could make it more explicit.

refine.bio normalized compendia are comprised of all the samples from a species that we were able to process, aggregate, and normalize. Normalized compendia provide a snapshot of the most complete collection of gene expression that refine.bio can produce for each supported organism. We process these compendia in a manner that is different from the options that are available via the web user interface.

Would it make sense to add a line that says something to the effect of:

Submitter processed samples that are available through the web user interface are omitted from normalized compendia.

Maybe also add a line saying why or a link to the issue that brought it to our attention.

I agree that we should make it explicit when we make that change. My read of what is currently there is not that submitter processed data is excluded because I would consider what we do to submitter processed data to fall under

process, aggregate, and normalize.

where "process" is pretty much limited to identifier mapping. This is why explicit is good!


[logging] Add support for display/debug structured log fields

Many times the identifier based one off structured logs require
conversion of objects to debug or strings before using, and cannot be
displayed with the serializer.  Debug can now be used by putting a ?
before the identifier, and a ! before the identifier for display.
Examples
        error!(?name = Vec::new(), "Debug");
        error!(!name = DisplayOnlyStruct {}, "Display");

I would probably prefer to have the sigil on the value itself, instead of the key. At least that is how both tracing and slog handle it. And it makes it clearer that the value is what needs to use Display or Debug, not the key.

This is actually handled through where an inner macro puts the sigil before the key.  The issue is having the sigil in front of an expression causes the compiler to complain.
https://docs.rs/tracing/0.1.19/src/tracing/macros.rs.html#637-644

I would probably prefer to have the sigil on the value itself, instead of the key. At least that is how both tracing and slog handle it. And it makes it clearer that the value is what needs to use Display or Debug, not the key.

Done.


unrestrict remove-missing from configmap and secret

Is your feature request related to a problem? Please describe.
Is there a reason that the remove-missing only works on ConfigMaps and Secrets? I have a CustomResource I would love to use it on. Right now, I have jinja template a file with conditionals that then gets kustomized. If i can use remove-missing, I can have all the fields in there and remove the conditional. The lack of keys in vault would act as the conditional.
Describe the solution you'd like
Remove missing should work on all resources
Describe alternatives you've considered
I'm not sure how to work around this issue otherwise.
Additional context
I have removed the check in util.go and can open a PR if it makes sense; it seems to do what I expect.

I actually managed to work around this by using a nested secret, but still curious if there's actually a reason this is restricted

Glad you found a workaround. This was the reason at the time https://github.com/argoproj-labs/argocd-vault-plugin/pull/177#issuecomment-898016968.


Update network-plugins.md

update zh-trans to match master branch, the orig seems to be based on release-1.14

@jiaj12 Can you squash the commits?

ping @jiaj12

still got problem on squash. After I squash recent 3 commit to 1 and commit to local patch-31, I got rejected when pushing to origin/patch-31. How to  overwrite the remote history? push to another branch?

After you have squashed your local commits, do a force push to  your fork: git push -f.

After you have squashed your local commits, do a force push to your fork: git push -f.

How about this time?

Worked!
/lgtm
/approve


Allow ignoring invalid time values

I have the following issue:
https://github.com/IsraelHikingMap/Site/issues/957
In it you will find a gpx file generated by compeGPS.
Unfortunately, the time in the metadata tag is 0000-00-00T00:00:00Z which is obviously an invalid time.
I would like to still be able to open this file using this library.
Just a side not, this package is used in production and is doing a great job! :-)

Thanks!!


PHPRedis::delete() is deprecated. Replaced with PHPRedis::del()

Coverage remained the same at 84.917% when pulling 315e9581fdde4d1bf91f6faf11f169ffd9e3abca on byron-lotto:Patch-1 into 8f1d28c765b257b05e932c28ad75c809e34fe12c on tedious:master.


[Darwin] Retrieve data stored into ~/Documents/chip.store

Problem
Values saved into local database are not retrieved properly, and so while the correct informations are stored, they are not read and reused after reboot.
Change overview

Get the data to be extracted from the local database directly
Update the test suites to makes sure the accessory is commissioned again

Testing
I have updated the test suite to make sure the device is commissioned again, otherwise the test suite just hangs.

Fast tracking, given platform change by platform maintainer.


`py2pack generate khal -f python-khal' leads to AttributeError

generating spec for python-khal produce AttributeError
$ py2pack generate khal -f python-khal
generating spec file for khal...
Traceback (most recent call last):
  File "/usr/bin/py2pack", line 22, in <module>
    main()
  File "/usr/lib/python2.7/site-packages/py2pack/__init__.py", line 354, in main
    args.func(args)
  File "/usr/lib/python2.7/site-packages/py2pack/__init__.py", line 268, in generate
    _augment_data_from_tarball(args, tarball_file[0], data)
  File "/usr/lib/python2.7/site-packages/py2pack/__init__.py", line 214, in _augment_data_from_tarball
    _canonicalize_setup_data(data)
  File "/usr/lib/python2.7/site-packages/py2pack/__init__.py", line 199, in _canonicalize_setup_data
    data["console_scripts"] = data["entry_points"]["console_scripts"].keys()
AttributeError: 'list' object has no attribute 'keys'

This is almost certainly the same issue as #41; please take a look there and see if the proposed pull request #42 helps you understand what's going wrong.


CAN bus functionality

Is your feature request related to a problem? Please describe.
Ubuntu normally has CAN bus functionality built in. Doesn't seem to be the case here (PEAK CAN adapter driver and other CAN functionality). The problem is mainly I would love to use WSL2 for work; But CAN support is required.
Describe the solution you'd like
I would like the WSL2 Kernel to be built with CAN support enabled to work alongside the USB/IP functionality.
Describe alternatives you've considered
I have delved into the depths of the internet and have almost successfully compiled my own Kernel version with these functionalities built in. And though I am fairly technically minded I didn't have the in-depth knowledge to fully work out the hiccups I was having.
Additional context
None

Duplicate of #5533?


Map support with higher pitch angles

Hello,
Will you consider upgrading to Mapbox version 2.0.0 (https://github.com/mapbox/mapbox-gl-js/releases/tag/v2.0.0) where the camera pitch angle is unlocked?
Thanks

The new version has a new license right.  I need to see what our dependency lib does, react-map-gl.
https://github.com/visgl/react-map-gl/blob/master/docs/get-started/mapbox-tokens.md
I'm not sure just yet how to best approach this.  Ideally it's a user choice with sensible defaults.  If I can find a way to have the mapbox v1 as the default, with the ability to pass in or configure the v2 (all via react-map-gl so really v5 & v6 of that project), then that would make the choice moot.

The new version has a new license right.  I need to see what our dependency lib does, react-map-gl.
https://github.com/visgl/react-map-gl/blob/master/docs/get-started/mapbox-tokens.md
I'm not sure just yet how to best approach this.  Ideally it's a user choice with sensible defaults.  If I can find a way to have the mapbox v1 as the default, with the ability to pass in or configure the v2 (all via react-map-gl so really v5 & v6 of that project), then that would make the choice moot.

We actually use streetscape as a dependency for now (no access to its internal code). So yes if it would be possible to have the Map version configurable at the application top-level (user's side), then this would meet our need (we can decide then whether to go with the commercial v2 Mapbox license or just keep using the older version)
I see this discussion, however, (https://github.com/visgl/deck.gl/discussions/5237) and wonder whether the Mapbox support as a base map will be dropped in the future!
Thanks for your response and support.

We actually use streetscape as a dependency for now (no access to its internal code). So yes if it would be possible to have the Map version configurable at the application top-level (user's side), then this would meet our need (we can decide then whether to go with the commercial v2 Mapbox license or just keep using the older version)
I see this discussion, however, (https://github.com/visgl/deck.gl/discussions/5237) and wonder whether the Mapbox support as a base map will be dropped in the future!
Thanks for your response and support.

I see this discussion, however, (visgl/deck.gl#5237) and wonder whether the Mapbox support as a base map will be dropped in the future!

No plans to drop mapbox support. The goal of deck.gl to support the best commercial base map integrations. Mapbox is likely to remain a great base map option for users who need very high quality base maps and are willing to pay for that.
That said, deck.gl would like to offer a free, lower quality base map option in parallel. I don't want to go ahead of things for deck.gl itself, but for now, if you follow the updates in react-map-gl docs http://visgl.github.io/react-map-gl/docs you can see work that is being done there to support alternative basemaps.

I see this discussion, however, (visgl/deck.gl#5237) and wonder whether the Mapbox support as a base map will be dropped in the future!

No plans to drop mapbox support. The goal of deck.gl to support the best commercial base map integrations. Mapbox is likely to remain a great base map option for users who need very high quality base maps and are willing to pay for that.
That said, deck.gl would like to offer a free, lower quality base map option in parallel. I don't want to go ahead of things for deck.gl itself, but for now, if you follow the updates in react-map-gl docs http://visgl.github.io/react-map-gl/docs you can see work that is being done there to support alternative basemaps.

Hmm, sounds good, thanks for the illustration.

Hmm, sounds good, thanks for the illustration.


XSLT transformation not correct in JRuby (because of XPath?)

In the gem xml-focus (https://github.com/gioele/xml-focus), Nokogiri::XSLT is used to transform an XML document using a dynamically created XSLT. Everything is fine when MRI Nokogiri is used. Under JRuby, the transformation returns an incorrect result caused by the fact that some XPath selects element that should not be selected.
An example file and a failing transformation can be seen running rspec inside the xml-focus source tree.

I'm sorry I haven't responded to this issue for such an embarrassingly long time.
Unfortunately, I couldn't get the xml-focus spec suite to run (I'm seeing some errors related to, likely, now-out-of-date RSpec API usage). If you can help me understand and reproduce what you're seeing, I might be able to help. Feel free to comment on this issue or open a new one.


registry update ... fails

When the registry does not have an access id and or secret it fails with an update.
registry update self
ERRO[0000] failed to parse registry id: strconv.ParseInt: parsing "self": invalid syntax
ERRO[0007] failed to update registry: [PUT /registries/{id}][404] updateRegistryNotFound  &{Errors:[0x14000218040]}

Hi @Vad1mo, I wasn't able to reproduce the issue – the command seems to be working smoothly.


continue not working in while loop

It looks like #196 is not fully working. The following example fails to lax.
fn while_continue() {
    let mut attempt = 0;
    let mut value = None;

    while attempt < 5 {
        attempt += 1;

        if attempt < 3 {
            continue;
        }

        value = Some(1);
    }
}

Open this code snippet in the playground

* Error 12 at Playground.fst(40,10-45,19):
  - Expected type
        o:
        (Rust_primitives.Integers.i32 &
          Core.Option.t_Option Rust_primitives.Integers.i32){o << temp_0_}
    but
        Core.Ops.Control_flow.ControlFlow_Continue (attempt, value)
        <:
        Core.Ops.Control_flow.t_ControlFlow (Prims.unit &
            (Rust_primitives.Integers.i32 &
            Core.Option.t_Option Rust_primitives.Integers.i32))
          (Rust_primitives.Integers.i32 &
            Core.Option.t_Option Rust_primitives.Integers.i32)
    has type
        Core.Ops.Control_flow.t_ControlFlow (Prims.unit &
            (Rust_primitives.Integers.i32 &
            Core.Option.t_Option Rust_primitives.Integers.i32))
          (Rust_primitives.Integers.i32 &
            Core.Option.t_Option Rust_primitives.Integers.i32)
1 error was reported (see above)
make[1]: *** [Makefile:79: Playground.fst.checked] Error 1
make: *** [Makefile:12: all] Error 2

Not sure how well we support while-loops in general, so this may be part of a larger issue.

If this is not supported for while loops, the description of the other issues has been incomplete.

If this is not supported for while loops, the description of the other issues has been incomplete.

While loops should be supported but it seems like the issue described here is a bug in the translation (I am not sure it is specific to while loops by the way). #1204 is also needed to make the translation usable but it is different.


Fix object / document comparison

Oh wow, well spotted. It even says a few lines above:

If one of the objects / documents does not have an attribute with the sought name, its attribute value is considered to be null.

I can confirm that { } == { "a": null } // true in version 2.5 and 3.9.devel.

This happens when you create unit tests out of the documentation ;-) #699 and #702 showed there as well.

Thank you for reporting and fixing the docs!


Questions about using accelerate library for training

Hi,
Thanks a lot for the repo! I was curious that if I use
CUDA_VISIBLE_DEVICES=0,1 python train.py --config configs/makelongvideo.yaml, it seems that the training time will be roughly 700 hours for 20k steps on 2xA100, however, if I use accelerate launch --config_file ./configs/multigpu.yaml train.py --config configs/makelongvideo.yaml on the same 2xA100, it will be >1000 hours, and I was curious that whether you know why this is the case?
Thank you!

Maybe it's due to default configuration difference with ./configs/multigpu.yaml. You can compare accelerate output information in console log. Mine(./configs/multigpu.yaml) is
/home/ubuntu/torch19/lib/python3.10/site-packages/accelerate/accelerator.py:231: FutureWarning: logging_dir is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use project_dir instead.
warnings.warn(
/home/ubuntu/torch19/lib/python3.10/site-packages/accelerate/accelerator.py:231: FutureWarning: logging_dir is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use project_dir instead.
warnings.warn(
06/27/2023 18:12:14 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 2
Process index: 1
Local process index: 1
Device: cuda:1
Mixed precision type: fp16
06/27/2023 18:12:14 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 2
Process index: 0
Local process index: 0
Device: cuda:0
Mixed precision type: fp16
{'prediction_type', 'variance_type'} was not found in config. Values will be initialized to default values.
{'norm_num_groups'} was not found in config. Values will be initialized to default values.
{'use_linear_projection', 'num_class_embeds', 'upcast_attention', 'only_cross_attention', 'resnet_time_scale_shift', 'dual_cross_attention', 'class_embed_type', 'mid_block_type'} was not found in config. Values will be initialized to default values.
{'prediction_type'} was not found in config. Values will be initialized to default values.
{'prediction_type'} was not found in config. Values will be initialized to default values.
06/27/2023 18:12:33 - INFO - main - ***** Running training *****
06/27/2023 18:12:33 - INFO - main -   Num examples = 4243251
06/27/2023 18:12:33 - INFO - main -   Num Epochs = 4
06/27/2023 18:12:33 - INFO - main -   Instantaneous batch size per device = 4
06/27/2023 18:12:33 - INFO - main -   Total train batch size (w. parallel, distributed & accumulation) = 800
06/27/2023 18:12:33 - INFO - main -   Gradient Accumulation steps = 100
06/27/2023 18:12:33 - INFO - main -   Total optimization steps = 20000
Resuming from checkpoint checkpoint-12080
06/27/2023 18:12:33 - INFO - accelerate.accelerator - Loading states from ./outputs/makelongvideo/checkpoint-12080

Thank you for the reply!! So here's what it printed:
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 4
Local process index: 4
Device: cuda:4
Mixed precision type: fp16
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 6
Local process index: 6
Device: cuda:6
Mixed precision type: fp16
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 7
Local process index: 7
Device: cuda:7
Mixed precision type: fp16
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 1
Local process index: 1
Device: cuda:1
Mixed precision type: fp16
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 2
Local process index: 2
Device: cuda:2
Mixed precision type: fp16
06/30/2023 22:31:30 - INFO - main - Distributed environment: MULTI_GPU  Backend: nccl
Num processes: 8
Process index: 5
Local process index: 5
Device: cuda:5
Mixed precision type: fp16
...........
06/30/2023 22:33:46 - INFO - main - ***** Running training *****
06/30/2023 22:33:46 - INFO - main -   Num examples = 2327084
06/30/2023 22:33:46 - INFO - main -   Num Epochs = 55
06/30/2023 22:33:46 - INFO - main -   Instantaneous batch size per device = 16
06/30/2023 22:33:46 - INFO - main -   Total train batch size (w. parallel, distributed & accumulation) = 6400
06/30/2023 22:33:46 - INFO - main -   Gradient Accumulation steps = 50
06/30/2023 22:33:46 - INFO - main -   Total optimization steps = 20000
Steps:   0%|                                                                                | 0/20000 [00:00<5517, ?it/s$
............
Steps:   0%|▎                                                                                | 80/20000 [20:50:17<5090:20:55, 919.94s/it, lr=0.0001, step_loss=
and my config is:
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: MULTI_GPU
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config: {}
gpu_ids: all
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: fp16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
use_cpu: false
any thoughts on why it will take >5000 hours on 8xA100 to train? Thank you!!

The config file seems fine. You can check CPU and GPU's load by nvidia-smi, htop, free -h. I guess your A100 is not fully running due to data feeding speed. Change num_workers=8 to num_workers=32 or bigger according to your machine ability.
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
    train_dataset, batch_size=train_batch_size, num_workers=8
)

Below is my machine running state, GPU's is 97% loaded.
nvidia-smi

free -h

htop

Refer to https://github.com/m-bain/webvid @Revanthraja

Thanks sir

The config file seems fine. You can check CPU and GPU's load by nvidia-smi, htop, free -h. I guess your A100 is not fully running due to data feeding speed. Change num_workers=8 to num_workers=32 or bigger according to your machine ability.
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
    train_dataset, batch_size=train_batch_size, num_workers=8
)

Below is my machine running state, GPU's is 97% loaded.
nvidia-smi
free -h
htop

Awesome thank you!!

Awesome thank you!!


Can vllm become faster?

I find an artice Accelerating Generative AI with PyTorch II: GPT, Fast
The optimization used in this article is as shown below

I simply tried gpt-fast, the improvement is huge
codellama-python-7b, 2xA10(24G)

infer
speed(token/s)

vllm fp16
45.2

gpt-fast fp16
66.5

gpt-fast int8
105.1

gpt-fast int4
145.9

ps: results of int4 is terrible
I'm curious, can these optimizations be used on vllm?
I can see some discussion about these optimizations, but it doesn't look like they will be possible in the short term (because of some problems about vllm?)
torch.compile
+34% higher throughput?
Compiled model with torch.compile, unfortunately without performance improvements
quantization
Add GPTQ support (I tried a version before but it didn't work well
Speculative Decoding
Speculative Decoding
vllm is a great project!! I really hope to see these optimizations in vllm. I also want to know the difficulties that still exist :)

hi, I want to run THUDM/chatglm-6b-int4 by vllm, but raising cuda oom error. Based on the log, it requires at least 10G gpu memory. Actually when I use huggingface transformers directly run this model, it only use 5G memory. Do you know why?

hi, I want to run THUDM/chatglm-6b-int4 by vllm, but raising cuda oom error. Based on the log, it requires at least 10G gpu memory. Actually when I use huggingface transformers directly run this model, it only use 5G memory. Do you know why?嗨，我想通过 vllm 运行 THUDM/chatglm-6b-int4，但引发 cuda oom 错误。根据日志，它至少需要 10G GPU 内存。其实我用 huggingface transformers 直接运行这个模型的时候，它只用了5G内存。你知道为什么吗？

https://github.com/vllm-project/vllm/issues/2176 maybe you can look at this issue

hi, I want to run THUDM/chatglm-6b-int4 by vllm, but raising cuda oom error. Based on the log, it requires at least 10G gpu memory. Actually when I use huggingface transformers directly run this model, it only use 5G memory. Do you know why?嗨，我想通过 vllm 运行 THUDM/chatglm-6b-int4，但引发 cuda oom 错误。根据日志，它至少需要 10G GPU 内存。其实我用 huggingface transformers 直接运行这个模型的时候，它只用了5G内存。你知道为什么吗？

#2176 maybe you can look at this issue

THX, but it not works. I open a new issue, could you please have a look?
https://github.com/vllm-project/vllm/issues/2338

Closing this as all the mentioned optimisations are either in progress or merged.


⚠ Bofrost.at has degraded performance

In 09572c0, Bofrost.at (https://www.bofrost.at) experienced degraded performance:

HTTP code: 200
Response time: 1204 ms

Resolved: Bofrost.at performance has improved in ea0c07f after 7 minutes.