8000 [ONNX] GFPGANv1.pth to onnx conversion error · Issue #116182 · pytorch/pytorch · GitHub
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shubhamghimire opened this issue Dec 20, 2023 · 5 comments
Open

[ONNX] GFPGANv1.pth to onnx conversion error #116182

shubhamghimire opened this issue Dec 20, 2023 · 5 comments
Labels
low priority We're unlikely to get around to doing this in the near future module: onnx Related to torch.onnx triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@shubhamghimire
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shubhamghimire commented Dec 20, 2023 •
edited
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🐛 Describe the bug

Converting GFPGANv1.pth original model to onnx gave me the following errors.

Traceback (most recent call last):
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/new_from_github.py", line 45, in <module>
    torch.onnx.export(inference_model,  # model being run
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py", line 516, in export
    _export(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py", line 1596, in _export
    graph, params_dict, torch_out = _model_to_graph(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py", line 1139, in _model_to_graph
    graph = _optimize_graph(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py", line 677, in _optimize_graph
    graph = _C._jit_pass_onnx(graph, operator_export_type)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py", line 1940, in _run_symbolic_function
    return symbolic_fn(graph_context, *inputs, **attrs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py", line 306, in wrapper
    return fn(g, *args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py", line 2519, in _convolution
    raise errors.SymbolicValueError(
torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of convolution for kernel of unknown shape.  [Caused by the value '818 defined in (%818 : Float(*, *, *, *, strides=[8192, 16, 4, 1], requires_grad=1, device=cuda:0) = onnx::Reshape[allowzero=0](%770, %817), scope: gfpgan.archs.gfpganv1_arch.GFPGANv1::/gfpgan.archs.gfpganv1_arch.StyleGAN2GeneratorSFT::stylegan_decoder/basicsr.archs.stylegan2_arch.StyleConv::style_conv1/basicsr.archs.stylegan2_arch.ModulatedConv2d::modulated_conv # /home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/archs/stylegan2_arch.py:282:0
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx::Reshape'.]
    (node defined in /home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/archs/stylegan2_arch.py(282): forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1508): _slow_forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1527): _call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1518): _wrapped_call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/archs/stylegan2_arch.py(333): forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1508): _slow_forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1527): _call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1518): _wrapped_call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/gfpgan/archs/gfpganv1_arch.py(102): forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1508): _slow_forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1527): _call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1518): _wrapped_call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/gfpgan/archs/gfpganv1_arch.py(395): forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1508): _slow_forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1527): _call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1518): _wrapped_call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/jit/_trace.py(124): wrapper
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/jit/_trace.py(133): forward
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1527): _call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py(1518): _wrapped_call_impl
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/jit/_trace.py(1285): _get_trace_graph
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py(915): _trace_and_get_graph_from_model
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py(1011): _create_jit_graph
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py(1135): _model_to_graph
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py(1596): _export
/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/utils.py(516): export
/home/batman/GFPGAN-Training-Models-To-Onnx/new_from_github.py(45): <module>
)

    Inputs:
        #0: 770 defined in (%770 : Float(*, *, *, *, strides=[8192, 16, 4, 1], requires_grad=1, device=cuda:0) = onnx::Tile(%769, %766), scope: gfpgan.archs.gfpganv1_arch.GFPGANv1::/gfpgan.archs.gfpganv1_arch.StyleGAN2GeneratorSFT::stylegan_decoder/basicsr.archs.stylegan2_arch.ConstantInput::constant_input # /home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/archs/stylegan2_arch.py:399:0
    )  (type 'Tensor')
        #1: 817 defined in (%817 : int[] = prim::ListConstruct(%295, %816, %785, %790), scope: gfpgan.archs.gfpganv1_arch.GFPGANv1::/gfpgan.archs.gfpganv1_arch.StyleGAN2GeneratorSFT::stylegan_decoder/basicsr.archs.stylegan2_arch.StyleConv::style_conv1/basicsr.archs.stylegan2_arch.ModulatedConv2d::modulated_conv
    )  (type 'List[int]')
    Outputs:
        #0: 818 defined in (%818 : Float(*, *, *, *, strides=[8192, 16, 4, 1], requires_grad=1, device=cuda:0) = onnx::Reshape[allowzero=0](%770, %817), scope: gfpgan.archs.gfpganv1_arch.GFPGANv1::/gfpgan.archs.gfpganv1_arch.StyleGAN2GeneratorSFT::stylegan_decoder/basicsr.archs.stylegan2_arch.StyleConv::style_conv1/basicsr.archs.stylegan2_arch.ModulatedConv2d::modulated_conv # /home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/archs/stylegan2_arch.py:282:0
    )  (type 'Tensor')

Conversion code for converting GFPGANv1.pth to onnx.
https://github.com/TencentARC/GFPGAN

import cv2
from basicsr.utils import img2tensor
from torchvisio
8000
n.transforms.functional import normalize
import torch

from gfpgan.archs.gfpganv1_arch import GFPGANv1

model_path = "./GFPGANv1.pth"
onnx_path = "./experiments/GFPGAN_v1.onnx"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("-------------------", device)

inference_model = GFPGANv1(
        out_size=512,
        num_style_feat=512,
        channel_multiplier=1,
        decoder_load_path=None,
        fix_decoder=False,
        num_mlp=8,
        input_is_latent=True,
        different_w=True,
        narrow=1,
        sft_half=True).to(device)

loadnet = torch.load(model_path)
if 'params_ema' in loadnet:
    keyname = 'params_ema'
else:
    keyname = 'params'
inference_model.load_state_dict(loadnet[keyname], strict=False)
inference_model = inference_model.eval()
img_path = './1.png'
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(input_img, (512, 512))
cropped_face_t = img2tensor(img / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)

mat1 = torch.randn(3, 512, 512).to(device)  # moving the tensor to cpu
mat1 = mat1.unsqueeze(0).to(device)

#torch_out = torch.jit.trace(inference_model, cropped_face_t)

torch.onnx.export(inference_model,  # model being run
                    mat1,  # model input (or a tuple for multiple inputs)
                    onnx_path,  # where to save the model (can be a file or file-like object)
                    export_params=True,  # store the trained parameter weights inside the model file
                    autograd_inlining=False,
                    opset_version=16,  # the ONNX version to export the model to
                    do_constant_folding=True,  # whether to execute constant folding for optimization
                    verbose=True,
                    input_names = ['modelInput'],   # the model's input names 
                    output_names = ['modelOutput'], # the model's output names 
                    dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes 
                                'modelOutput' : {0 : 'batch_size'}}
                    )

print("export GFPGANv1 onnx done.")

I already tried with different Opset versions and different Pytorch versions but same error occurs and only Pytorch 2.1.0 gives me the error details.

Versions

Collecting environment information...
PyTorch version: 2.1.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX A2000 12GB
Nvidia driver version: 536.67
cuDNN version: Could not collect
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: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 7
BogoMIPS: 7199.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_vnni flush_l1d arch_capabilities
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 4 MiB (4 instances)
L3 cache: 8.3 MiB (1 instance)
Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Mitigation; Enhanced IBRS
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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] onnx==1.15.0
[pip3] onnxruntime==1.16.2
[pip3] onnxsim==0.4.35
[pip3] torch==2.1.0+cu118
[pip3] torchaudio==2.1.0+cu118
[pip3] torchvision==0.16.0+cu118
[pip3] triton==2.1.0
[conda] Could not collect
@shubhamghimire shubhamghimire changed the title [ONNX] Convert GFPGANv1.pth to onnx [ONNX] GFPGANv1.pth to onnx conversion error Dec 20, 2023
@drisspg drisspg added the module: onnx Related to torch.onnx label Dec 20, 2023
@github-project-automation github-project-automation bot moved this to Inbox in ONNX Dec 20, 2023
@justinchuby
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Please consider using the torch.onnx.dynamo_export api.

@justinchuby justinchuby added the low priority We're unlikely to get around to doing this in the near future label Dec 20, 2023
@drisspg drisspg added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label Dec 21, 2023
@shubhamghimire
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Author < 8000 /div>

@justinchuby Thank you for your help. I tried with torch.onnx.dynamo_export and got a lot of errors. I couldn't figure out what the error is about.

torch.onnx.dynamo_export(inference_model, cropped_face_t)

The Error

/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:130: UserWarning: torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
  warnings.warn(
[2023-12-21 10:51:58,910] [0/0] torch._dynamo.variables.higher_order_ops: [WARNING] speculate_subgraph: while introspecting the user-defined autograd.Function, we were unable to trace function `trampoline_autograd_fwd` into a single graph. This means that Dynamo was unable to prove safety for this API and will fall back to eager-mode PyTorch, which could lead to a slowdown.
[2023-12-21 10:51:58,910] [0/0] torch._dynamo.variables.higher_order_ops: [ERROR] call_method UserDefinedObjectVariable(fused_bias_act) __call__ [TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(int), ConstantVariable(int), ConstantVariable(float), ConstantVariable(float)] {}
Traceback (most recent call last):
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/higher_order_ops.py", line 152, in speculate_subgraph
    output = f.call_function(tx, args, sub_kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 261, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 598, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2179, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2286, in inline_call_
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1155, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars.items)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 594, in call_function
    return self.obj.call_method(tx, self.name, args, kwargs).add_options(self)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 413, in call_method
    return tx.inline_call(tx, forward, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2179, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2286, in inline_call_
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1115, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 383, in call_function
    return self.call_method(tx, "__call__", args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 303, in call_method
    return super().call_method(tx, name, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 329, in call_method
    raise unimplemented(f"call_method {self} {name} {args} {kwargs}")
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 172, in unimplemented
    raise Unsupported(msg)
torch._dynamo.exc.Unsupported: call_method UserDefinedObjectVariable(fused_bias_act) __call__ [TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(int), ConstantVariable(int), ConstantVariable(float), ConstantVariable(float)] {}

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

Traceback (most recent call last):
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py", line 1195, in dynamo_export
    ).export()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py", line 941, in export
    graph_module = self.options.fx_tracer.generate_fx(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py", line 199, in generate_fx
    graph_module, graph_guard = torch._dynamo.export(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1140, in inner
    result_traced = opt_f(*args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 328, in _fn
    return fn(*args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/fx/dynamo_graph_extractor.py", line 154, in wrapped
    return output_adapter.apply(model_func(*args, **kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 490, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 133, in _fn
    return fn(*args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 389, in _convert_frame_assert
    return _compile(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 569, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 189, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 491, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1028, in transform_code_object
    transformations(instructions, code_options)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 458, in transform
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2074, in run
    super().run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1115, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 266, in call_function
    tx.call_function(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py", line 331, in call_function
    return tx.inline_user_function_return(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 598, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2179, in in
8000
line_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2286, in inline_call_
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1155, in CALL_FUNCTION_EX
    self.call_function(fn, argsvars.items, kwargsvars.items)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 307, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 261, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 598, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2179, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2286, in inline_call_
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1115, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 261, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 598, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2179, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2286, in inline_call_
    tracer.run()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 724, in run
    and self.step()
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 688, in step
    getattr(self, inst.opname)(inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 392, in wrapper
    return inner_fn(self, inst)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1115, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 562, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 583, in call_function
    return self.obj.call_apply(tx, args, kwargs).add_options(self)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/misc.py", line 333, in call_apply
    speculated_fwd_result = higher_order_autograd_fn.call_function(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/higher_order_ops.py", line 966, in call_function
    ) = speculate_subgraph(
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/_dynamo/variables/higher_order_ops.py", line 211, in speculate_subgraph
    raise Unsupported(
torch._dynamo.exc.Unsupported: speculate_subgraph: while introspecting the user-defined autograd.Function, we were unable to trace function `trampoline_autograd_fwd` into a single graph. This means that Dynamo was unable to prove safety for this API and will fall back to eager-mode PyTorch, which could lead to a slowdown. Scroll up for the stack trace of the initial exception. The reason was: call_method UserDefinedObjectVariable(fused_bias_act) __call__ [TensorVariable(), TensorVariable(), TensorVariable(), ConstantVariable(int), ConstantVariable(int), ConstantVariable(float), ConstantVariable(float)] {}

from user code:
   File "/home/batman/GFPGAN-Training-Models-To-Onnx/gfpgan/archs/gfpganv1_arch.py", line 367, in forward
    feat = self.conv_body_first(x)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/ops/fused_act/fused_act.py", line 91, in forward
    return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/basicsr/ops/fused_act/fused_act.py", line 95, in fused_leaky_relu
    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)

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


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

Traceback (most recent call last):
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/new_from_github.py", line 59, in <module>
    torch.onnx.dynamo_export(inference_model, cropped_face_t)
  File "/home/batman/GFPGAN-Training-Models-To-Onnx/onnxVenv/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py", line 1206, in dynamo_export
    raise OnnxExporterError(
torch.onnx.OnnxExporterError: Failed to export the model to ONNX. Generating SARIF report at 'report_dynamo_export.sarif'. SARIF is a standard format for the output of static analysis tools. SARIF logs can be loaded in VS Code SARIF viewer extension, or SARIF web viewer (https://microsoft.github.io/sarif-web-component/). Please report a bug on PyTorch Github: https://github.com/pytorch/pytorch/issues

report_dynamo_export.sarif File

{
 "runs":[
  {
   "tool":{
    "driver":{
     "name":"torch.onnx.dynamo_export",
     "contents":[
      "localizedData",
      "nonLocalizedData"
     ],
     "language":"en-US",
     "rules":[],
     "version":"2.1.0+cu118"
    }
   },
   "language":"en-US",
   "newlineSequences":[
    "\r\n",
    "\n"
   ],
   "results":[]
  }
 ],
 "version":"2.1.0",
 "schemaUri":"https://docs.oasis-open.org/sarif/sarif/v2.1.0/cs01/schemas/sarif-schema-2.1.0.json"
}

@justinchuby
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I see there's an autograd function. cc @BowenBao

@shubhamghimire
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@justinchuby @BowenBao I am not able to fix it yet. Do you guys have any suggestions?

@shubhamghimire shubhamghimire mentioned this issue Jan 9, 2024
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@johndpope
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johndpope commented Oct 4, 2024 •
edited
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related - #107677
JaidedAI/EasyOCR#746
facebookresearch/detectron2#5143
#128501
#116182
wyhsirius/LIA#1
williamyang1991/StyleGANEX#24
hellloxiaotian/ADSRNet#3

I think chatgpt-o1 actually solves this (claude 3.5 didn't)
https://gist.github.com/johndpope/eceebd54fcf9713ebb66fd3e96503e67

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low priority We're unlikely to get around to doing this in the near future module: onnx Related to torch.onnx triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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