8000 test_gradient_all Device Type test regression with Numpy >= 2.0.0 · Issue #132450 · pytorch/pytorch · GitHub
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test_gradient_all Device Type test regression with Numpy >= 2.0.0 #132450

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123epsilon opened this issue Aug 1, 2024 · 4 comments
Open

test_gradient_all Device Type test regression with Numpy >= 2.0.0 #132450

123epsilon opened this issue Aug 1, 2024 · 4 comments
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actionable module: autograd Related to torch.autograd, and the autograd engine in general module: ci Related to continuous integration module: numpy Related to numpy support, and also numpy compatibility of our operators module: tests Issues related to tests (not the torch.testing module) triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@123epsilon
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123epsilon commented Aug 1, 2024 •
edited by pytorch-bot bot
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🐛 Describe the bug

When running the device type test test_gradient_all using numpy==2.0.0 or numpy==2.0.1 I get the following failure for all dtypes:

AssertionError: The length of the sequences mismatch: 2 != 1

This is due to the np.gradient function producing fewer tensors in its output than expected in v2.0.0 - I have been able to confirm that installing numpy==1.26.4 and then running the test causes the test to pass.

To reproduce:

# ... setup pytorch ( I set it up from source)
pip install numpy==2.0.1
python test/test_torch.py -k TestTorchDeviceTypeCPU.test_gradient_all --verbose
...
# we get the failure
AssertionError: The length of the sequences mismatch: 2 != 1

pip install numpy==1.26.4
python test/test_torch.py -k TestTorchDeviceTypeCPU.test_gradient_all --verbose
...
# tests pass
OK

Honestly I haven't looked too deeply into the test logic, but I do know that it is the call to np.gradient that is causing the failure. I don't know what an appropriate solution, if any, is since this doesn't technically affect the correctness of torch.gradient - perhaps raise an issue in numpy?

Versions

PyTorch version: 2.4.0a0+gitc84e248
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.0.32831-204d35d16

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.1
Libc version: glibc-2.35

Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7713 64-Core Processor
CPU family: 25
Model: 1
Thread(s) per core: 1
Core(s) per socket: 64
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3720.7029
CPU min MHz: 1500.0000
BogoMIPS: 3992.24
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization: AMD-V
L1d cache: 4 MiB (128 instances)
L1i cache: 4 MiB (128 instances)
L2 cache: 64 MiB (128 instances)
L3 cache: 512 MiB (16 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-63
NUMA node1 CPU(s): 64-127
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==2.0.0
[pip3] optree==0.12.1
[pip3] torch==2.4.0a0+gitc84e248
[conda] numpy 2.0.0 pypi_0 pypi
[conda] optree 0.12.1 pypi_0 pypi
[conda] torch 2.4.0a0+gitc84e248 pypi_0 pypi

cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @Varal7 @xmfan @seemethere @malfet @pytorch/pytorch-dev-infra @mruberry @ZainRizvi @rgommers
@malfet malfet added module: autograd Related to torch.autograd, and the autograd engine in general module: ci Related to continuous integration labels Aug 2, 2024
@malfet malfet added module: tests Issues related to tests (not the torch.testing module) triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module labels Aug 2, 2024
@soulitzer soulitzer added the module: numpy Related to numpy support, and also numpy compatibility of our operators label Aug 2, 2024
@albanD
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albanD commented Aug 5, 2024

I think this is fixed as part of #130689 that will be merged piece by piece

@123epsilon
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Ah I see, great

@albanD albanD reopened this Aug 5, 2024
@albanD
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albanD commented Aug 5, 2024

Keeping this open until we land the fix!

@albanD albanD mentioned this issue Aug 6, 2024
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@soulitzer
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@kiukchung FYI, do you know if this one is still failing or has it been fixed?

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actionable module: autograd Related to torch.autograd, and the autograd engine in general module: ci Related to continuous integration module: numpy Related to numpy support, and also numpy compatibility of our operators module: tests Issues related to tests (not the torch.testing module) triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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