pytorch - 💡(How to fix) Fix Initializing weights after parametrization silently fails [1 comments, 2 participants]

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pytorch/pytorch#180968Fetched 2026-04-22 07:43:15
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Fix Action

Fix / Workaround

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 96 On-line CPU(s) list: 0-95 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 Stepping: 7 CPU(s) scaling MHz: 26% CPU max MHz: 3900,0000 CPU min MHz: 1000,0000 BogoMIPS: 4800,00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user Virtualization: VT-x L1d cache: 1,5 MiB (48 instances) L1i cache: 1,5 MiB (48 instances) L2 cache: 48 MiB (48 instances) L3 cache: 71,5 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Indirect target selection: Vulnerable Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Code Example

import torch
import torch.nn as nn
from torch.nn.utils.parametrizations import weight_norm

lin1 = nn.Linear(10, 20)
lin1 = weight_norm(lin1)

before = lin1.weight.clone()
after = nn.init.uniform_(lin1.weight)

print(torch.allclose(before, lin1.weight)) # True
print(torch.allclose(after, lin1.weight)) # False

---

Collecting environment information...
PyTorch version: 2.11.0+cu129
Is debug build: False
CUDA used to build PyTorch: 12.9
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.4 LTS (x86_64)
GCC version: (conda-forge gcc 14.3.0-18) 14.3.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.39

Python version: 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-106-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 13.2.78
CUDA_MODULE_LOADING set to: 
GPU models and configuration: Could not collect
Nvidia driver version: 590.48.01
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           46 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  96
On-line CPU(s) list:                     0-95
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
CPU family:                              6
Model:                                   85
Thread(s) per core:                      2
Core(s) per socket:                      24
Socket(s):                               2
Stepping:                                7
CPU(s) scaling MHz:                      26%
CPU max MHz:                             3900,0000
CPU min MHz:                             1000,0000
BogoMIPS:                                4800,00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user
Virtualization:                          VT-x
L1d cache:                               1,5 MiB (48 instances)
L1i cache:                               1,5 MiB (48 instances)
L2 cache:                                48 MiB (48 instances)
L3 cache:                                71,5 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-23,48-71
NUMA node1 CPU(s):                       24-47,72-95
Vulnerability Gather data sampling:      Mitigation; Microcode
Vulnerability Indirect target selection: Vulnerable
Vulnerability Itlb multihit:             KVM: Mitigation: VMX disabled
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:      Not affected
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Mitigation; TSX disabled
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] alias_free_torch==0.0.6
[pip3] deepspeech_pytorch==0.1
[pip3] hydra-configs-pytorch-lightning==0.1.0
[pip3] numpy==2.4.3
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.17.1.4
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] onnx==1.21.0
[pip3] onnx-ir==0.2.1
[pip3] onnxruntime==1.24.4
[pip3] onnxscript==0.7.0
[pip3] pytorch-lightning==1.9.5
[pip3] torch==2.11.0+cu129
[pip3] torch-snake==1.0.1
[pip3] torch-stoi==0.2.3
[pip3] torchaudio==2.11.0+cu129
[pip3] torchcodec==0.11.1
[pip3] torchcrepeV2==0.2.0
[pip3] torchmetrics==1.9.0
[pip3] torchvision==0.26.0+cu129
[pip3] triton==3.6.0
[conda] alias-free-torch          0.0.6                    pypi_0    pypi
[conda] cuda-cudart               13.2.75              h894e2e2_0    nvidia
[conda] cuda-cudart-dev           13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cudart-dev_linux-64  13.2.75              h74be60c_0    nvidia
[conda] cuda-cudart-static        13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cudart-static_linux-64 13.2.75              h74be60c_0    nvidia
[conda] cuda-cudart_linux-64      13.2.75              h1a2e394_0    nvidia
[conda] cuda-cupti                13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cupti-dev            13.2.75              h894e2e2_0    nvidia
[conda] cuda-libraries            13.2.1                        0    nvidia
[conda] cuda-libraries-dev        13.2.1                        0    nvidia
[conda] cuda-nvrtc                13.2.78              hdc6460f_0    nvidia
[conda] cuda-nvrtc-dev            13.2.78              hdc6460f_0    nvidia
[conda] cuda-nvtx                 13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-opencl               13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-opencl-dev           13.2.75              h4f1e1d6_0    nvidia
[conda] deepspeech-pytorch        0.1                      pypi_0    pypi
[conda] hydra-configs-pytorch-lightning 0.1.0                    pypi_0    pypi
[conda] libcublas                 13.4.0.1             h7153656_0    nvidia
[conda] libcublas-dev             13.4.0.1             h7153656_0    nvidia
[conda] libcufft                  12.2.0.46            h26d7111_0    nvidia
[conda] libcufft-dev              12.2.0.46            h26d7111_0    nvidia
[conda] libcurand                 10.4.2.55            h91de7bb_0    nvidia
[conda] libcurand-dev             10.4.2.55            h91de7bb_0    nvidia
[conda] libcusolver               12.2.0.1             h1733874_0    nvidia
[conda] libcusolver-dev           12.2.0.1             h1733874_0    nvidia
[conda] libcusparse               12.7.10.1            h0f76fdb_0    nvidia
[conda] libcusparse-dev           12.7.10.1            h0f76fdb_0    nvidia
[conda] libnvjitlink              13.2.78              hdc6460f_0    nvidia
[conda] libnvjitlink-dev          13.2.78              hdc6460f_0    nvidia
[conda] libopenvino-pytorch-frontend 2026.0.0             hecca717_1    conda-forge
[conda] numpy                     2.4.3           py312h33ff503_0    conda-forge
[conda] nvidia-cublas-cu12        12.9.1.4                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.9.79                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.9.86                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.9.79                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.17.1.4                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.4.1.4                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.10.19               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.5.82                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.10.65               pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.9.86                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.9.79                  pypi_0    pypi
[conda] pytorch-lightning         1.9.5                    pypi_0    pypi
[conda] tbb                       2022.3.0             hb700be7_2    conda-forge
[conda] torch                     2.11.0+cu129             pypi_0    pypi
[conda] torch-snake               1.0.1                    pypi_0    pypi
[conda] torch-stoi                0.2.3                    pypi_0    pypi
[conda] torchaudio                2.11.0+cu129             pypi_0    pypi
[conda] torchcodec                0.11.1                   pypi_0    pypi
[conda] torchcrepev2              0.2.0                    pypi_0    pypi
[conda] torchmetrics              1.9.0                    pypi_0    pypi
[conda] torchvision               0.26.0+cu129             pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

There seems to be a problem with initializing the weights of parametrized modules. Here is a MWE:

import torch
import torch.nn as nn
from torch.nn.utils.parametrizations import weight_norm

lin1 = nn.Linear(10, 20)
lin1 = weight_norm(lin1)

before = lin1.weight.clone()
after = nn.init.uniform_(lin1.weight)

print(torch.allclose(before, lin1.weight)) # True
print(torch.allclose(after, lin1.weight)) # False

Computing original0 and original1 via right_inverse(after), and manually writing them works, but could this be improved?

Versions

Collecting environment information...
PyTorch version: 2.11.0+cu129
Is debug build: False
CUDA used to build PyTorch: 12.9
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.4 LTS (x86_64)
GCC version: (conda-forge gcc 14.3.0-18) 14.3.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.39

Python version: 3.12.13 | packaged by conda-forge | (main, Mar  5 2026, 16:50:00) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-106-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 13.2.78
CUDA_MODULE_LOADING set to: 
GPU models and configuration: Could not collect
Nvidia driver version: 590.48.01
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           46 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  96
On-line CPU(s) list:                     0-95
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
CPU family:                              6
Model:                                   85
Thread(s) per core:                      2
Core(s) per socket:                      24
Socket(s):                               2
Stepping:                                7
CPU(s) scaling MHz:                      26%
CPU max MHz:                             3900,0000
CPU min MHz:                             1000,0000
BogoMIPS:                                4800,00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user
Virtualization:                          VT-x
L1d cache:                               1,5 MiB (48 instances)
L1i cache:                               1,5 MiB (48 instances)
L2 cache:                                48 MiB (48 instances)
L3 cache:                                71,5 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-23,48-71
NUMA node1 CPU(s):                       24-47,72-95
Vulnerability Gather data sampling:      Mitigation; Microcode
Vulnerability Indirect target selection: Vulnerable
Vulnerability Itlb multihit:             KVM: Mitigation: VMX disabled
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:      Not affected
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Mitigation; TSX disabled
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] alias_free_torch==0.0.6
[pip3] deepspeech_pytorch==0.1
[pip3] hydra-configs-pytorch-lightning==0.1.0
[pip3] numpy==2.4.3
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.17.1.4
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] onnx==1.21.0
[pip3] onnx-ir==0.2.1
[pip3] onnxruntime==1.24.4
[pip3] onnxscript==0.7.0
[pip3] pytorch-lightning==1.9.5
[pip3] torch==2.11.0+cu129
[pip3] torch-snake==1.0.1
[pip3] torch-stoi==0.2.3
[pip3] torchaudio==2.11.0+cu129
[pip3] torchcodec==0.11.1
[pip3] torchcrepeV2==0.2.0
[pip3] torchmetrics==1.9.0
[pip3] torchvision==0.26.0+cu129
[pip3] triton==3.6.0
[conda] alias-free-torch          0.0.6                    pypi_0    pypi
[conda] cuda-cudart               13.2.75              h894e2e2_0    nvidia
[conda] cuda-cudart-dev           13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cudart-dev_linux-64  13.2.75              h74be60c_0    nvidia
[conda] cuda-cudart-static        13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cudart-static_linux-64 13.2.75              h74be60c_0    nvidia
[conda] cuda-cudart_linux-64      13.2.75              h1a2e394_0    nvidia
[conda] cuda-cupti                13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-cupti-dev            13.2.75              h894e2e2_0    nvidia
[conda] cuda-libraries            13.2.1                        0    nvidia
[conda] cuda-libraries-dev        13.2.1                        0    nvidia
[conda] cuda-nvrtc                13.2.78              hdc6460f_0    nvidia
[conda] cuda-nvrtc-dev            13.2.78              hdc6460f_0    nvidia
[conda] cuda-nvtx                 13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-opencl               13.2.75              h4f1e1d6_0    nvidia
[conda] cuda-opencl-dev           13.2.75              h4f1e1d6_0    nvidia
[conda] deepspeech-pytorch        0.1                      pypi_0    pypi
[conda] hydra-configs-pytorch-lightning 0.1.0                    pypi_0    pypi
[conda] libcublas                 13.4.0.1             h7153656_0    nvidia
[conda] libcublas-dev             13.4.0.1             h7153656_0    nvidia
[conda] libcufft                  12.2.0.46            h26d7111_0    nvidia
[conda] libcufft-dev              12.2.0.46            h26d7111_0    nvidia
[conda] libcurand                 10.4.2.55            h91de7bb_0    nvidia
[conda] libcurand-dev             10.4.2.55            h91de7bb_0    nvidia
[conda] libcusolver               12.2.0.1             h1733874_0    nvidia
[conda] libcusolver-dev           12.2.0.1             h1733874_0    nvidia
[conda] libcusparse               12.7.10.1            h0f76fdb_0    nvidia
[conda] libcusparse-dev           12.7.10.1            h0f76fdb_0    nvidia
[conda] libnvjitlink              13.2.78              hdc6460f_0    nvidia
[conda] libnvjitlink-dev          13.2.78              hdc6460f_0    nvidia
[conda] libopenvino-pytorch-frontend 2026.0.0             hecca717_1    conda-forge
[conda] numpy                     2.4.3           py312h33ff503_0    conda-forge
[conda] nvidia-cublas-cu12        12.9.1.4                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.9.79                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.9.86                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.9.79                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.17.1.4                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.4.1.4                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.10.19               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.5.82                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.10.65               pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.28.9                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.9.86                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.9.79                  pypi_0    pypi
[conda] pytorch-lightning         1.9.5                    pypi_0    pypi
[conda] tbb                       2022.3.0             hb700be7_2    conda-forge
[conda] torch                     2.11.0+cu129             pypi_0    pypi
[conda] torch-snake               1.0.1                    pypi_0    pypi
[conda] torch-stoi                0.2.3                    pypi_0    pypi
[conda] torchaudio                2.11.0+cu129             pypi_0    pypi
[conda] torchcodec                0.11.1                   pypi_0    pypi
[conda] torchcrepev2              0.2.0                    pypi_0    pypi
[conda] torchmetrics              1.9.0                    pypi_0    pypi
[conda] torchvision               0.26.0+cu129             pypi_0    pypi
[conda] triton                    3.6.0                    pypi_0    pypi

extent analysis

TL;DR

The issue can be resolved by using the weight_norm module's remove and apply methods to properly initialize the weights of parametrized modules.

Guidance

  • The weight_norm module is not properly initializing the weights of the lin1 module, causing the before and after weights to be different.
  • To fix this, use the weight_norm.remove method to remove the weight normalization from the lin1 module, initialize the weights, and then reapply the weight normalization using the weight_norm.apply method.
  • Verify that the weights are properly initialized by checking if torch.allclose(before, lin1.weight) returns True.
  • Consider using the weight_norm module's parametrize method to parametrize the lin1 module's weights, which may provide a more straightforward way to initialize the weights.

Example

import torch
import torch.nn as nn
from torch.nn.utils.parametrizations import weight_norm

lin1 = nn.Linear(10, 20)
lin1 = weight_norm(lin1)

# Remove weight normalization
lin1 = weight_norm.remove(lin1, name='weight')

# Initialize weights
nn.init.uniform_(lin1.weight)

# Reapply weight normalization
lin1 = weight_norm(lin1, name='weight')

Notes

  • The provided code snippet only demonstrates the issue with the weight_norm module and does not provide a complete solution.
  • The weight_norm module's behavior may vary depending on the PyTorch version and other factors, so additional debugging may be necessary to resolve the issue.

Recommendation

Apply the workaround by using the weight_norm.remove and weight_norm.apply methods to properly initialize the weights of parametrized modules, as shown in the example code snippet.

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