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RuntimeError: "SigmoidFocalLoss_forward" not implemented for 'Half' #1048

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dedoogong opened this issue Aug 16, 2019 · 1 comment · May be fixed by #1146
Open

RuntimeError: "SigmoidFocalLoss_forward" not implemented for 'Half' #1048

dedoogong opened this issue Aug 16, 2019 · 1 comment · May be fixed by #1146

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@dedoogong
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dedoogong commented Aug 16, 2019 •
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🐛 Bug

I can run training of normal faster/mask rcnn with R-50/FPN on FP16. But I failed to run it with Retinanet(sigmoid focal loss is the problem)

I guess theres couldbe 2 options.

  1. implement half based cuda code.
  2. use apex to handle fp32/fp16 conversion during for/backward pass.

I found the current Sigmoid Focal Loss CUDA code doesn't support FP 16(Half).
For example, I could train normal FasterRCNN-R50-FPN but failed for "RETINANET-FasterRCNN-R50-FPN which use the sigmoid focal loss function) on FP16(using O1 opt).

When I use Sigmoid Focal Loss CPU version, it runs OK for both the original maskrcnn-benchsmark's Retinanet models and FCOS's retinanet models.

BUT estimated training time is almost "1 month"!! and after some itteration, only sigmoid focal loss becomes "nan"

2019-08-16 17:42:57,158 maskrcnn_benchmark.trainer INFO: Start training
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32768.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32768.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8192.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8192.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2048.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2048.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
2019-08-16 17:45:22,396 maskrcnn_benchmark.trainer INFO: eta: 30 days, 6:08:34 iter: 20 loss: 4.3898 (5.7831) loss_centerness: 0.6876 (0.7018) loss_cls: 1.0846 (1.0754) loss_reg: 2.5395 (4.0059) time: 8.5586 (7.2618) data: 0.0029 (0.0810) lr: 0.003333 max mem: 6133
2019-08-16 17:46:51,064 maskrcnn_benchmark.trainer INFO: eta: 24 days, 8:41:46 iter: 40 loss: 3.3856 (4.6105) loss_centerness: 0.6687 (0.6854) loss_cls: 1.0689 (1.0852) loss_reg: 1.6177 (2.8399) time: 3.0172 (5.8476) data: 0.0025 (0.0420) lr: 0.003333 max mem: 6133
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 512.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 512.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 256.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 256.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 128.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 128.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 64.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 64.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.5
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.5
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.25
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.25
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.03125
2019-08-16 17:48:29,613 maskrcnn_benchmark.trainer INFO: eta: 23 days, 1:59:50 iter: 60 loss: nan (nan) loss_centerness: 0.6633 (0.6788) loss_cls: nan (nan) loss_reg: 1.5505 (2.3987) time: 4.5355 (5.5409) data: 0.0030 (0.0291) lr: 0.003333 max mem: 6133
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.03125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.015625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.015625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0078125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0078125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.00390625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.00390625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.001953125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.001953125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0009765625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0009765625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.00048828125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.00048828125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.000244140625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.000244140625
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0001220703125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 0.0001220703125
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.103515625e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.103515625e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.0517578125e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.0517578125e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.52587890625e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.52587890625e-05
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.62939453125e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.62939453125e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.814697265625e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.814697265625e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.9073486328125e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.9073486328125e-06
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.5367431640625e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.5367431640625e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.76837158203125e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.76837158203125e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.384185791015625e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.384185791015625e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1920928955078125e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1920928955078125e-07
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.960464477539063e-08
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.960464477539063e-08
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.9802322387695312e-08
2019-08-16 17:49:48,975 maskrcnn_benchmark.trainer INFO: eta: 21 days, 10:39:17 iter: 80 loss: nan (nan) loss_centerness: 0.6606 (0.6748) loss_cls: nan (nan) loss_reg: 1.5613 (2.1857) time: 3.1732 (5.1477) data: 0.0024 (0.0225) lr: 0.003333 max mem: 6133
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.9802322387695312e-08
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4901161193847656e-08
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4901161193847656e-08
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.450580596923828e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.450580596923828e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.725290298461914e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.725290298461914e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.862645149230957e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.862645149230957e-09
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.313225746154785e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.313225746154785e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.656612873077393e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.656612873077393e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.3283064365386963e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.3283064365386963e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1641532182693481e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1641532182693481e-10
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.820766091346741e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.820766091346741e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.9103830456733704e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.9103830456733704e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4551915228366852e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4551915228366852e-11
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.275957614183426e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.275957614183426e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.637978807091713e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.637978807091713e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.8189894035458565e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.8189894035458565e-12
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.094947017729282e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 9.094947017729282e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.547473508864641e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.547473508864641e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.2737367544323206e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.2737367544323206e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1368683772161603e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1368683772161603e-13
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.684341886080802e-14
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.684341886080802e-14
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.842170943040401e-14
2019-08-16 17:51:34,847 maskrcnn_benchmark.trainer INFO: eta: 21 days, 13:32:36 iter: 100 loss: nan (nan) loss_centerness: 0.6631 (0.6728) loss_cls: nan (nan) loss_reg: 1.5312 (2.0573) time: 7.3970 (5.1769) data: 0.0026 (0.0186) lr: 0.003333 max mem: 6133
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.842170943040401e-14
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4210854715202004e-14
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.4210854715202004e-14
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.105427357601002e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 7.105427357601002e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.552713678800501e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.552713678800501e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.7763568394002505e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.7763568394002505e-15
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.881784197001252e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.881784197001252e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.440892098500626e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.440892098500626e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.220446049250313e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.220446049250313e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1102230246251565e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.1102230246251565e-16
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.551115123125783e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.551115123125783e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.7755575615628914e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.7755575615628914e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.3877787807814457e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.3877787807814457e-17
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.938893903907228e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.938893903907228e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.469446951953614e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.469446951953614e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.734723475976807e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.734723475976807e-18
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.673617379884035e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8.673617379884035e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.336808689942018e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4.336808689942018e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.168404344971009e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.168404344971009e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.0842021724855044e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.0842021724855044e-19
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.421010862427522e-20
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 5.421010862427522e-20
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.710505431213761e-20
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2.710505431213761e-20
2019-08-16 17:52:37,750 maskrcnn_benchmark.trainer INFO: eta: 20 days, 3:39:49 iter: 120 loss: nan (nan) loss_centerness: 0.6604 (0.6710) loss_cls: nan (nan) loss_reg: 1.5168 (1.9788) time: 2.9350 (4.8383) data: 0.0030 (0.0161) lr: 0.003333 max mem: 6133
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.3552527156068805e-20
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1.3552527156068805e-20
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.776263578034403e-21
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 6.776263578034403e-21
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.3881317890172014e-21
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 3.3881317890172014e-21

To Reproduce

after installing this repo, I just run following command
python3 -m torch.distributed.launch --nproc_per_node=$NGPUS /home/ktai01/maskrcnn-benchmark/tools/train_net.py --config-file /home/ktai01/maskrcnn-benchmark/configs/retinanet/retinanet_R-50-FPN_P5_1x.yaml MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 1000 DTYPE "float16"

Traceback (most recent call last):
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 191, in
main()
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 184, in main
model = train(cfg, args.local_rank, args.distributed)
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 85, in train
arguments,
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 71, in do_train
loss_dict = model(images, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/distributed.py", line 376, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py", line 131, in forward
return self._forward_train(anchors, box_cls, box_regression, targets)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py", line 138, in _forward_train
anchors, box_cls, box_regression, targets
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/loss.py", line 77, in call
labels
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py", line 68, in forward
loss = loss_func(logits, targets, self.gamma, self.alpha)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py", line 19, in forward
logits, targets, num_classes, gamma, alpha
RuntimeError: "SigmoidFocalLoss_forward" not implemented for 'Half' (operator() at /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu:139)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7fdc14f1b441 in /usr/local/lib/python3.6/dist-packages/torch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7fdc14f1ad7a in /usr/local/lib/python3.6/dist-packages/torch/lib/libc10.so)
frame #2: + 0x4917f (0x7fdbae35c17f in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #3: SigmoidFocalLoss_forward_cuda(at::Tensor const&, at::Tensor const&, int, float, float) + 0x606 (0x7fdbae35c7f5 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #4: SigmoidFocalLoss_forward(at::Tensor const&, at::Tensor const&, int, float, float) + 0x64 (0x7fdbae32cb44 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #5: + 0x28fcf (0x7fdbae33bfcf in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #6: + 0x25291 (0x7fdbae338291 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #7: /usr/bin/python3() [0x5030d5]
frame #8: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #9: /usr/bin/python3() [0x504c28]
frame #10: /usr/bin/python3() [0x58644b]
frame #11: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #12: THPFunction_apply(_object, _object) + 0x6b1 (0x7fdc11ec5481 in /usr/local/lib/python3.6/dist-packages/torch/lib/libtorch_python.so)
frame #13: /usr/bin/python3() [0x502d6f]
frame #14: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #15: _PyFunction_FastCallDict + 0xf5 (0x501945 in /usr/bin/python3)
frame #16: /usr/bin/python3() [0x591461]
frame #17: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #18: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #19: /usr/bin/python3() [0x504c28]
frame #20: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #21: /usr/bin/python3() [0x591461]
frame #22: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #23: /usr/bin/python3() [0x54d4e2]
frame #24: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #25: /usr/bin/python3() [0x503073]
frame #26: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #27: _PyFunction_FastCallDict + 0xf5 (0x501945 in /usr/bin/python3)
frame #28: /usr/bin/python3() [0x591461]
frame #29: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #30: /usr/bin/python3() [0x54d4e2]
frame #31: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #32: /usr/bin/python3() [0x503073]
frame #33: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #34: /usr/bin/python3() [0x502209]
frame #35: /usr/bin/python3() [0x502f3d]
frame #36: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #37: /usr/bin/python3() [0x504c28]
frame #38: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #39: /usr/bin/python3() [0x591461]
frame #40: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #41: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #42: /usr/bin/python3() [0x504c28]
frame #43: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #44: /usr/bin/python3() [0x591461]
frame #45: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #46: /usr/bin/python3() [0x54d4e2]
frame #47: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #48: /usr/bin/python3() [0x503073]
frame #49: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #50: /usr/bin/python3() [0x504c28]
frame #51: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #52: /usr/bin/python3() [0x591461]
frame #53: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #54: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #55: /usr/bin/python3() [0x504c28]
frame #56: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #57: /usr/bin/python3() [0x591461]
frame #58: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #59: /usr/bin/python3() [0x54d4e2]
frame #60: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #61: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #62: /usr/bin/python3() [0x504c28]
frame #63: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)

Traceback (most recent call last):
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 191, in
main()
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 184, in main
model = train(cfg, args.local_rank, args.distributed)
File "/home/ktai01/maskrcnn-benchmark/tools/train_net.py", line 85, in train
arguments,
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 71, in do_train
loss_dict = model(images, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/distributed.py", line 376, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py", line 131, in forward
return self._forward_train(anchors, box_cls, box_regression, targets)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py", line 138, in _forward_train
anchors, box_cls, box_regression, targets
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/loss.py", line 77, in call
labels
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in call
result = self.forward(*input, kwargs)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py", line 68, in forward
loss = loss_func(logits, targets, self.gamma, self.alpha)
File "/home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py", line 19, in forward
logits, targets, num_classes, gamma, alpha
RuntimeError: "SigmoidFocalLoss_forward" not implemented for 'Half' (operator() at /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu:139)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7fd51e596441 in /usr/local/lib/python3.6/dist-packages/torch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7fd51e595d7a in /usr/local/lib/python3.6/dist-packages/torch/lib/libc10.so)
frame #2: + 0x4917f (0x7fd4b872217f in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #3: SigmoidFocalLoss_forward_cuda(at::Tensor const&, at::Tensor const&, int, float, float) + 0x606 (0x7fd4b87227f5 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #4: SigmoidFocalLoss_forward(at::Tensor const&, at::Tensor const&, int, float, float) + 0x64 (0x7fd4b86f2b44 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #5: + 0x28fcf (0x7fd4b8701fcf in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #6: + 0x25291 (0x7fd4b86fe291 in /home/ktai01/maskrcnn-benchmark/maskrcnn_benchmark/_C.cpython-36m-x86_64-linux-gnu.so)
frame #7: /usr/bin/python3() [0x5030d5]
frame #8: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #9: /usr/bin/python3() [0x504c28]
frame #10: /usr/bin/python3() [0x58644b]
frame #11: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #12: THPFunction_apply(_object, _object) + 0x6b1 (0x7fd51edb3481 in /usr/local/lib/python3.6/dist-packages/torch/lib/libtorch_python.so)
frame #13: /usr/bin/python3() [0x502d6f]
frame #14: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #15: _PyFunction_FastCallDict + 0xf5 (0x501945 in /usr/bin/python3)
frame #16: /usr/bin/python3() [0x591461]
frame #17: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #18: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #19: /usr/bin/python3() [0x504c28]
frame #20: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #21: /usr/bin/python3() [0x591461]
frame #22: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #23: /usr/bin/python3() [0x54d4e2]
frame #24: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #25: /usr/bin/python3() [0x503073]
frame #26: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #27: _PyFunction_FastCallDict + 0xf5 (0x501945 in /usr/bin/python3)
frame #28: /usr/bin/python3() [0x591461]
frame #29: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #30: /usr/bin/python3() [0x54d4e2]
frame #31: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #32: /usr/bin/python3() [0x503073]
frame #33: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #34: /usr/bin/python3() [0x502209]
frame #35: /usr/bin/python3() [0x502f3d]
frame #36: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #37: /usr/bin/python3() [0x504c28]
frame #38: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #39: /usr/bin/python3() [0x591461]
frame #40: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #41: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #42: /usr/bin/python3() [0x504c28]
frame #43: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #44: /usr/bin/python3() [0x591461]
frame #45: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #46: /usr/bin/python3() [0x54d4e2]
frame #47: _PyObject_FastCallKeywords + 0x19c (0x5a730c in /usr/bin/python3)
frame #48: /usr/bin/python3() [0x503073]
frame #49: _PyEval_EvalFrameDefault + 0x449 (0x506859 in /usr/bin/python3)
frame #50: /usr/bin/python3() [0x504c28]
frame #51: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #52: /usr/bin/python3() [0x591461]
frame #53: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #54: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #55: /usr/bin/python3() [0x504c28]
frame #56: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)
frame #57: /usr/bin/python3() [0x591461]
frame #58: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #59: /usr/bin/python3() [0x54d4e2]
frame #60: PyObject_Call + 0x3e (0x59ebbe in /usr/bin/python3)
frame #61: _PyEval_EvalFrameDefault + 0x1807 (0x507c17 in /usr/bin/python3)
frame #62: /usr/bin/python3() [0x504c28]
frame #63: _PyFunction_FastCallDict + 0x2de (0x501b2e in /usr/bin/python3)

Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 235, in
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 231, in main
cmd=process.args)
subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', '/home/ktai01/maskrcnn-benchmark/tools/train_net.py', '--local_rank=0', '--config-file', '/home/ktai01/maskrcnn-benchmark/configs/retinanet/retinanet_R-50-FPN_P5_1x.yaml', 'MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN', '1000', 'DTYPE', 'float16']' returned non-zero exit status 1.

  • PyTorch Version : 1.1.0
  • OS : ubuntu 18
  • How you installed PyTorch (pip):
  • Python version: 3.6
  • CUDA/cuDNN version: 10.0 / 7.4
  • GPU models and configuration: GTX 1080
@stanstarks
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stanstarks commented Aug 30, 2019 •
edited
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Hi, @dedoogong ,

I think you can convert the logits to float32 before the loss computation and convert back in the forward function

orig_type = logits.dtype
logits = logits.type(torch.float32)
loss = loss_func(logits, targets, self.gamma, self.alpha)
return loss.sum().type(orig_type)

maskrcnn-benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py

Lines 61 to 69 in 24c8c90

def forward(self, logits, targets):
device = logits.device
if logits.is_cuda:
loss_func = sigmoid_focal_loss_cuda
else:
loss_func = sigmoid_focal_loss_cpu
loss = loss_func(logits, targets, self.gamma, self.alpha)
return loss.sum()

@stanstarks stanstarks mentioned this issue Aug 30, 2019
Open
@huyq huyq linked a pull request Oct 23, 2019 that will close this issue
Open
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fix FP 16 training for retinanet huyq/maskrcnn-benchmark
2 participants
@stanstarks @dedoogong