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Datasets, Transforms and Models specific to Computer Vision
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Compiling resize_image: function interpolate not_implemented #8389

Open treasan opened 4 months ago

treasan commented 4 months ago

🐛 Describe the bug

I am compiling a method (mode=default, fullgrph=True), which calls torchvision.transforms.v2.functional.resize_image. However, I receive an error, which indicates that the interpolate method is not implemented. I am using pytorch lightning and weirdly this only happens during validation. It works fine during training.

Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:

  - tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>

For more information, try re-running with TORCH_LOGS=not_implemented

from user code:
  File ...
  File "/some/python/file.py", line 54, in encode
    x = tv_func.resize_image(
  File ".../miniconda3/envs/deepmotion3/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py", line 260, in resize_image
    image = interpolate(

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
TypeError: Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:

  - tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>

For more information, try re-running with TORCH_LOGS=not_implemented

The above exception was the direct cause of the following exception:

RuntimeError: Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:

  - tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>

For more information, try re-running with TORCH_LOGS=not_implemented

During handling of the above exception, another exception occurred:

  File "/some/python/file.py", line 299, in validation_step
    loss = self.shared_step(step_batch, train=False)
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/some/python/file.py", line 586, in run
    trainer.fit(model, datamodule, ckpt_path=opt.resume_from_checkpoint)
  File "/some/python/file.py", line 702, in <module>
    run(opt, trainer, datamodule, model)
torch._dynamo.exc.TorchRuntimeError: Failed running call_function <function interpolate at 0x7f60c39593a0>(*(FakeTensor(..., device='cuda:0', size=(75, 3, 256, 256)),), **{'size': [224, 224], 'mode': 'bicubic', 'align_corners': False, 'antialias': True}):
Multiple dispatch failed for 'torch.ops.aten.size'; all __torch_dispatch__ handlers returned NotImplemented:

  - tensor subclass <class 'torch._subclasses.fake_tensor.FakeTensor'>

For more information, try re-running with TORCH_LOGS=not_implemented

from user code:
  File ...
  File "/some/python/file.py", line 54, in encode
    x = tv_func.resize_image(
  File ".../miniconda3/envs/deepmotion3/lib/python3.11/site-packages/torchvision/transforms/v2/functional/_geometry.py", line 260, in resize_image
    image = interpolate(

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True

Versions

Collecting environment information... PyTorch version: 2.2.1 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

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 3.22.1 Libc version: glibc-2.35

Python version: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:53:32) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-PCIE-40GB GPU 1: NVIDIA A100-PCIE-40GB GPU 2: NVIDIA A100-PCIE-40GB GPU 3: NVIDIA A100-PCIE-40GB GPU 4: NVIDIA A100-PCIE-40GB GPU 5: NVIDIA A100-PCIE-40GB GPU 6: NVIDIA A100-PCIE-40GB GPU 7: NVIDIA A100-PCIE-40GB

Nvidia driver version: 535.171.04 cuDNN version: Probably one of the following: /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.0 /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.0 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: 43 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 64 On-line CPU(s) list: 0-63 Vendor ID: AuthenticAMD Model name: AMD EPYC 7452 32-Core Processor CPU family: 23 Model: 49 Thread(s) per core: 1 Core(s) per socket: 32 Socket(s): 2 Stepping: 0 Frequency boost: enabled CPU max MHz: 2350.0000 CPU min MHz: 1500.0000 BogoMIPS: 4700.09 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 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es Virtualization: AMD-V L1d cache: 2 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 32 MiB (64 instances) L3 cache: 256 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-31 NUMA node1 CPU(s): 32-63 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: Mitigation; untrained return thunk; SMT disabled Vulnerability Spec rstack overflow: Mitigation; SMT disabled Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] pytorch-lightning==2.1.3 [pip3] torch==2.2.1 [pip3] torch-fidelity==0.3.0 [pip3] torchaudio==2.2.1 [pip3] torchdiffeq==0.2.3 [pip3] torchmetrics==1.3.2 [pip3] torchvision==0.17.1 [pip3] triton==2.2.0 [conda] blas 1.0 mkl [conda] libjpeg-turbo 2.0.0 h9bf148f_0 pytorch [conda] mkl 2023.1.0 h213fc3f_46344 [conda] mkl-service 2.4.0 py311h5eee18b_1 [conda] mkl_fft 1.3.8 py311h5eee18b_0 [conda] mkl_random 1.2.4 py311hdb19cb5_0 [conda] numpy 1.26.4 py311h08b1b3b_0 [conda] numpy-base 1.26.4 py311hf175353_0 [conda] pytorch 2.2.1 py3.11_cuda12.1_cudnn8.9.2_0 pytorch [conda] pytorch-cuda 12.1 ha16c6d3_5 pytorch [conda] pytorch-lightning 2.1.3 pyhd8ed1ab_0 conda-forge [conda] pytorch-mutex 1.0 cuda pytorch [conda] torch-fidelity 0.3.0 pypi_0 pypi [conda] torchaudio 2.2.1 py311_cu121 pytorch [conda] torchdiffeq 0.2.3 pypi_0 pypi [conda] torchmetrics 1.3.2 pyhd8ed1ab_0 conda-forge [conda] torchtriton 2.2.0 py311 pytorch [conda] torchvision 0.17.1 py311_cu121 pytorch

NicolasHug commented 4 months ago

Hi @Tomsen1410 , thanks for the report. Compiling resize_image() should work in general although there might be edge cases with the bicubic mode. Can you share the details of the call to resize_image()? (And, ideally, a minimal reproducing example)