I install apex according this sentence:

python setup.py install --cuda_ext --cpp_ext

2.After that, using

import apex

to test, but it report warning as following: Warning: apex was installed without --cuda_ext. Fused syncbn kernels will be unavailable. Python fallbacks will be used instead. Warning: apex was installed without --cuda_ext. FusedAdam will be unavailable. Warning: apex was installed without --cuda_ext. FusedLayerNorm will be unavailable.

Is there any problem?

When using O2, data parallel does not work: RuntimeError: Expected tensor for argument #1 'input' to have the same device as tensor for argument #2 'weight'; but device 1 does not equal 0 (while checking arguments for cudnn_convolution)

however with O1, everything works just fine.

model = GeneralVae(encoder, decoder, rep_size=500).cuda()
optimizer = optim.Adam(model.parameters(), lr=LR)
model, optimizer = amp.initialize(model, optimizer, opt_level='O2')
if data_para and torch.cuda.device_count() > 1:
    print("Let's use", torch.cuda.device_count(), "GPUs!")
    model = nn.DataParallel(model)
    model = model.cuda()

loss_picture = customLoss()

val_losses = []
train_losses = []

def train(epoch):
    train_loader_food = generate_data_loader(train_root, get_batch_size(epoch), int(rampDataSize * data_size))
    print("Epoch {}: batch_size {}".format(epoch, get_batch_size(epoch)))
    model.train()
    train_loss = 0
    loss = None
    for batch_idx, (data, _, aff) in enumerate(train_loader_food):
        data = data[0].cuda(0)

After installing apex with the cuda extensions and running pytorch-pretrained-BERT, I get the following error in FusedLayerNormAffineFunction, apex/normalization/fused_layer_norm.py (line 21).

RuntimeError: a Tensor with 2482176 elements cannot be converted to Scalar (item at /pytorch/aten/src/ATen/native/Scalar.cpp:9)

Here are the shapes of my tensors:

input_ - [32, 101, 768]
bias_ - [768]
weight_ - [768]
self.normalized_shape - [768]

I'm not sure if it's a problem with pytorch-pretrained-BERT calling it incorrectly or a bug in apex. Any idea? I've also created an issue here.

I'm running Ubuntu with CUDA 9, PyTorch 0.4.1.

Full stacktrace below.

File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 710, in forward
    embedding_output = self.embeddings(input_ids, token_type_ids)
  File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/pytorch_pretrained_bert/modeling.py", line 261, in forward
    embeddings = self.LayerNorm(embeddings)
  File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/apex/normalization/fused_layer_norm.py", line 149, in forward
    input, self.weight, self.bias)
  File "/home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/apex/normalization/fused_layer_norm.py", line 21, in forward
    input_, self.normalized_shape, weight_, bias_, self.eps)

RuntimeError: a Tensor with 2482176 elements cannot be converted to Scalar (item at /pytorch/aten/src/ATen/native/Scalar.cpp:9)

frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f1aa5da3021 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f1aa5da28ea in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libc10.so)
frame #2: at::native::item(at::Tensor const&) + 0x12c3 (0x7f1aa690d5b3 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libcaffe2.so)
frame #3: at::TypeDefault::item(at::Tensor const&) const + 0x55 (0x7f1aa6b1c905 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libcaffe2.so)
frame #4: torch::autograd::VariableType::eye_out(at::Tensor&, long, long) const + 0x184 (0x7f1aa4faeec4 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libtorch.so.1)
frame #5: <unknown function> + 0x89ca (0x7f1a82e739ca in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so)
frame #6: layer_norm_affine(at::Tensor, c10::ArrayRef<long>, at::Tensor, at::Tensor, double) + 0x185 (0x7f1a82e762a5 in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/apex-0.1-py3.6-linux-x86_64.egg/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so)
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frame #15: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #16: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #17: THPFunction_do_forward(THPFunction*, _object*) + 0x15c (0x7f1ae02e21ec in /home/hyper/Documents/anaconda3/envs/allennlp/lib/python3.6/site-packages/torch/lib/libtorch_python.so)
frame #18: PyCFunction_Call + 0x5f (0x55a8f992863f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #19: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #20: <unknown function> + 0x16ba91 (0x55a8f997fa91 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #21: _PyObject_FastCallDict + 0x8b (0x55a8f992592b in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #22: <unknown function> + 0x19857e (0x55a8f99ac57e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #23: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #24: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so)
frame #25: _PyFunction_FastCallDict + 0x11b (0x55a8f99a6bab in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #26: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #27: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #28: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #29: _PyEval_EvalFrameDefault + 0x19ec (0x55a8f99d2a6c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
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frame #31: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #32: _PyFunction_FastCallDict + 0x1bc (0x55a8f99a6c4c in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #33: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #34: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #35: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #36: <unknown function> + 0x16ba91 (0x55a8f997fa91 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #37: _PyObject_FastCallDict + 0x8b (0x55a8f992592b in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #38: <unknown function> + 0x19857e (0x55a8f99ac57e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #39: _PyEval_EvalFrameDefault + 0x30a (0x55a8f99d138a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #40: <unknown function> + 0x71e1 (0x7f1af51ee1e1 in /home/hyper/.PyCharm2018.1/system/cythonExtensions/_pydevd_frame_eval_ext/pydevd_frame_evaluator.cpython-36m-x86_64-linux-gnu.so)
frame #41: <unknown function> + 0x1918e4 (0x55a8f99a58e4 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #42: _PyFunction_FastCallDict + 0x3da (0x55a8f99a6e6a in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #43: _PyObject_FastCallDict + 0x26f (0x55a8f9925b0f in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #44: _PyObject_Call_Prepend + 0x63 (0x55a8f992a6a3 in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
frame #45: PyObject_Call + 0x3e (0x55a8f992554e in /home/hyper/Documents/anaconda3/envs/allennlp/bin/python)
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Hi, thank you so much for your input so far. I have now successfully converted my models to use DDP instead of DP and time per epoch has gone down from 1400s to 1140s when using 8 GPUs. One problem that I was unable to solve so far is running more than one DDP model on the same machine:

Traceback (most recent call last):
  File "run/run_training_DPP.py", line 68, in <module>
    unpack_data=unpack, deterministic=deterministic, fp16=args.fp16)
  File "/home/fabian/PhD/meddec/meddec/model_training/distributed_training/nnUNetTrainerDPP.py", line 26, in __init__
    dist.init_process_group(backend='nccl', init_method='env://')
  File "/home/fabian/dl_venv_python3/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 354, in init_process_group
    store, rank, world_size = next(rendezvous(url))
  File "/home/fabian/dl_venv_python3/lib/python3.6/site-packages/torch/distributed/rendezvous.py", line 143, in _env_rendezvous_handler
    store = TCPStore(master_addr, master_port, world_size, start_daemon)
RuntimeError: Address already in use

(which is kind of to be expected given how DDP works). Do you know a workaround for this? We recently have gotten a dgx2 with 16 GPUs and I would like to run two different experiments in parallel, each using 8 GPUs. Best, Fabian

When running a model using apex+ddt my loss spikes dramatically after the model restarts. If i disable apex, it works fine.

Currently, I've set up apex this way:

optimizer = Adam()
schedulers= LR

torch.cuda.set_device(gpu_nb)
model.cuda(gpu_nb)

# apex
model, optimizer = amp.initialize(model, optimizer, opt_level='O2')    

# ddp
model = DDP(model)   

# restore state
ckpt = torch.load(path)
model.load_state_dict(ckpt['state_dict'])
optimizer.load_state_dict(ckpt['opt_dict'])
LR.load_state_dict(ckpt['lr_dict'])
amp.load_state_dict(ckpt['amp'])   

# continue ....

Actual code is here:

• training set up
• weight restore after the above executes

The warning message was too subtle/too easy to overlook in the output of setup.py, and it really should be a hard error.

Making it a hard error should also assist with cuda driver version errors like https://github.com/NVIDIA/apex/issues/314. https://github.com/NVIDIA/apex/issues/314 resulted because the cuda driver (libcuda.so) version was 10.0, the cuda toolkit version used to compile the Pytorch binaries was 10.0 (which was fine), but the cuda toolkit version used to compile Apex was 10.1** (which triggered a PTX JIT compilation error at runtime because the 10.0 libcuda.so couldn't handle the PTX produced by the 10.1 nvcc). The PTX JIT compilation error message was cryptic and unhelpful.

However, if the toolkit version that was used to compile Pytorch binaries is too recent for the system's cuda driver version, Pytorch will raise a much more helpful error, something like

"AssertionError: 
The NVIDIA driver on your system is too old (found version 10000)..."

If we hard-enforce that the cuda toolkit version used to compile Apex == the cuda toolkit version used to compile Pytorch, we also ensure that if the toolkit version used to compile Apex is too new for the driver, the toolkit version used to compile Pytorch must also be too new for the driver, and therefore in such cases we will receive the helpful Pytorch error instead of the bizarre PTX JIT error.

**A warning of the mismatch between torch.version.cuda and the toolkit (nvcc) had likely been issued by the setup.py while compiling apex, but this warning had likely been overlooked, so what ended up surfacing was the PTX JIT error, which was not at all a clear indication of what had gone wrong.

I created a simple GCP Deep Learning VM: https://cloud.google.com/deep-learning-vm/

I followed the install directions, and the install failed with errors:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .

...
Command "/opt/anaconda3/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-req-build-j0qgf5ds/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, 
__file__, 'exec'))" --cpp_ext --cuda_ext install --record /tmp/pip-record-1yr2fag5/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-req-build-j0qgf5ds/
Exception information:
Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/cli/base_command.py", line 143, in main
    status = self.run(options, args)
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/commands/install.py", line 366, in run
    use_user_site=options.use_user_site,
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/req/__init__.py", line 49, in install_given_reqs
    **kwargs
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/req/req_install.py", line 791, in install
    spinner=spinner,
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/utils/misc.py", line 705, in call_subprocess
    % (command_desc, proc.returncode, cwd))

The Python-only option also failed:

pip install -v --no-cache-dir .

...
Command "/opt/anaconda3/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-req-build-eedemek6/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, 
__file__, 'exec'))" install --record /tmp/pip-record-ehl5a4y7/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-req-build-eedemek6/
Exception information:
Traceback (most recent call last):
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/cli/base_command.py", line 143, in main
    status = self.run(options, args)
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/commands/install.py", line 366, in run
    use_user_site=options.use_user_site,
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/req/__init__.py", line 49, in install_given_reqs
    **kwargs
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/req/req_install.py", line 791, in install
    spinner=spinner,
  File "/opt/anaconda3/lib/python3.7/site-packages/pip/_internal/utils/misc.py", line 705, in call_subprocess
    % (command_desc, proc.returncode, cwd))

It would seem like installation on a GCP Deep Learning VM would be one of the tested use cases here no?? If it doesn't work there of all places, where is it intended to work?

I'm getting a segmentation fault when trying to train a model with amp.

torch version '1.0.1.post2' cudnn version 7.4.2

@mcarilli What could cause those issues?

Defaults for this optimization level are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Segmentation fault (core dumped)

:[System Logs]:
:Mar 29 12:23:22 server kernel: python3.6[95957]: segfault at b ip 00007f6e08bc664c sp 00007ffe63a06200 error 6 in amp_C.cpython-36m-x86_64-linux-gnu.so[7f6e08bb3000+64000]
:Mar 29 12:23:22 server abrt-hook-ccpp[96042]: Process 95957 (python3.6) of user 992434 killed by SIGSEGV - dumping core
:Mar 29 12:26:09 server kernel: python3.6[99444]: segfault at b ip 00007f502c4b764c sp 00007ffd04c510a0 error 6 in amp_C.cpython-36m-x86_64-linux-gnu.so[7f502c4a4000+64000]
:Mar 29 12:26:09 server abrt-hook-ccpp[99522]: Process 99444 (python3.6) of user 992434 killed by SIGSEGV - dumping core
:Mar 29 12:44:58 server kernel: python3.6[106167]: segfault at b ip 00007fa96640c64c sp 00007ffce94225f0 error 6 in amp_C.cpython-36m-x86_64-linux-gnu.so[7fa9663f9000+64000]
:Mar 29 12:44:58 server abrt-hook-ccpp[106244]: Process 106167 (python3.6) of user 992434 killed by SIGSEGV - dumping core

Hi, I'm facing the issue that the program crash when the input for SyncBatchNorm is two dimensions. Here's the code:

import torch
import apex

model = apex.parallel.SyncBatchNorm(4).cuda()
data = torch.rand((8,4)).cuda()
output = model(data)

When running the code, error raised like this:

Traceback (most recent call last):
  File "syncbn_test.by", line 7, in <module>
    output = model(data)
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/apex/parallel/optimized_sync_batchnorm.py", line 81, in forward
    return SyncBatchnormFunction.apply(input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, self.channel_last)
  File "/usr/local/lib/python3.5/dist-packages/apex/parallel/optimized_sync_batchnorm_kernel.py", line 27, in forward
    mean, var_biased = syncbn.welford_mean_var(input)
RuntimeError: Dimension out of range (expected to be in range of [-2, 1], but got 2) (maybe_wrap_dim at /pytorch/aten/src/ATen/core/WrapDimMinimal.h:18)

And everthing runs ok when data a 4 dims tensor.

Here is my environment:

Ubuntu 16.04
Python 3.5.2
Pytorch 1.0.1, installed with "pip install torch"
apex is installed with command:
  pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .
cuda 10.0
nvidia driver 410.72

I installed as such:

1. pytorch 1.1.

2. torchvison 2.1 (both from conda -c python).

3. built apex.

And now I'm getting this error:

  File "/home/waf/miniconda3/envs/fisherman/lib/python3.7/site-packages/apex/multi_tensor_apply/multi_tensor_
apply.py", line 30, in __call__                                                                                    
    *args)                                                                                                         
RuntimeError: CUDA error: a PTX JIT compilation failed (multi_tensor_apply at csrc/multi_tensor_apply.cuh:101)     
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f119a22c441 in /home/waf/miniconda3/en
vs/fisherman/lib/python3.7/site-packages/torch/lib/libc10.so)                                                      
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f119a22bd7a in /home/waf/mini
conda3/envs/fisherman/lib/python3.7/site-packages/torch/lib/libc10.so)                                             
frame #2: void multi_tensor_apply<2, ScaleFunctor<c10::Half, float>, float>(int, int, at::Tensor const&, std::vecto
r<std::vector<at::Tensor, std::allocator<at::Tensor> >, std::allocator<std::vector<at::Tensor, std::allocator<at::T
ensor> > > > const&, ScaleFunctor<c10::Half, float>, float) + 0x2c71 (0x7f112bd57a01 in /home/waf/miniconda3/
envs/fisherman/lib/python3.7/site-packages/amp_C.cpython-37m-x86_64-linux-gnu.so)                                  
frame #3: multi_tensor_scale_cuda(int, at::Tensor, std::vector<std::vector<at::Tensor, std::allocator<at::Tensor> >
, std::allocator<std::vector<at::Tensor, std::allocator<at::Tensor> > > >, float) + 0x3a8 (0x7f112bd4d9a8 in /home/
waf/miniconda3/envs/fisherman/lib/python3.7/site-packages/amp_C.cpython-37m-x86_64-linux-gnu.so)             
frame #4: <unknown function> + 0x15bac (0x7f112bd4cbac in /home/waf/miniconda3/envs/fisherman/lib/python3.7/s
ite-packages/amp_C.cpython-37m-x86_64-linux-gnu.so)                                                                
frame #5: <unknown function> + 0x15c6e (0x7f112bd4cc6e in /home/waf/miniconda3/envs/fisherman/lib/python3.7/s
ite-packages/amp_C.cpython-37m-x86_64-linux-gnu.so)                                                                
frame #6: <unknown function> + 0x12277 (0x7f112bd49277 in /home/waf/miniconda3/envs/fisherman/lib/python3.7/s
ite-packages/amp_C.cpython-37m-x86_64-linux-gnu.so)                                                                
<omitting python frames>                                                                                           
frame #45: __libc_start_main + 0xf1 (0x7f119ea3c2e1 in /lib/x86_64-linux-gnu/libc.so.6)      

I'd like to install apex but failed. Any suggestions? Thank you.

$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/commands/install.py:243: UserWarning: Disabling all use of wheels due to the use of --build-options / --global-options / --install-options.
  cmdoptions.check_install_build_global(options)
Created temporary directory: /tmp/pip-ephem-wheel-cache-8ib28pad
Created temporary directory: /tmp/pip-req-tracker-su9lkj72
Created requirements tracker '/tmp/pip-req-tracker-su9lkj72'
Created temporary directory: /tmp/pip-install-px208sk7
Processing /lab/cedar/home/lin/gitProjects/apex
  Created temporary directory: /tmp/pip-req-build-zfi8u8xr
  Added file:///lab/cedar/home/lin/gitProjects/apex to build tracker '/tmp/pip-req-tracker-su9lkj72'
    Running setup.py (path:/tmp/pip-req-build-zfi8u8xr/setup.py) egg_info for package from file:///lab/cedar/home/lin/gitProjects/apex
    Running command python setup.py egg_info
    torch.__version__  =  1.2.0
    running egg_info
    creating pip-egg-info/apex.egg-info
    writing pip-egg-info/apex.egg-info/PKG-INFO
    writing dependency_links to pip-egg-info/apex.egg-info/dependency_links.txt
    writing top-level names to pip-egg-info/apex.egg-info/top_level.txt
    writing manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt'
    reading manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt'
    writing manifest file 'pip-egg-info/apex.egg-info/SOURCES.txt'
    /tmp/pip-req-build-zfi8u8xr/setup.py:43: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
      warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")
  Source in /tmp/pip-req-build-zfi8u8xr has version 0.1, which satisfies requirement apex==0.1 from file:///lab/cedar/home/lin/gitProjects/apex
  Removed apex==0.1 from file:///lab/cedar/home/lin/gitProjects/apex from build tracker '/tmp/pip-req-tracker-su9lkj72'
Skipping bdist_wheel for apex, due to binaries being disabled for it.
Installing collected packages: apex
  Created temporary directory: /tmp/pip-record-mazj60_q
    Running command /lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-mazj60_q/install-record.txt --single-version-externally-managed --compile
    torch.__version__  =  1.2.0
    /tmp/pip-req-build-zfi8u8xr/setup.py:43: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
      warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")

    Compiling cuda extensions with
    nvcc: NVIDIA (R) Cuda compiler driver
    Copyright (c) 2005-2018 NVIDIA Corporation
    Built on Sat_Aug_25_21:08:01_CDT_2018
    Cuda compilation tools, release 10.0, V10.0.130
    from /usr/local/cuda/bin

    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-req-build-zfi8u8xr/setup.py", line 100, in <module>
        check_cuda_torch_binary_vs_bare_metal(torch.utils.cpp_extension.CUDA_HOME)
      File "/tmp/pip-req-build-zfi8u8xr/setup.py", line 77, in check_cuda_torch_binary_vs_bare_metal
        "https://github.com/NVIDIA/apex/pull/323#discussion_r287021798.  "
    RuntimeError: Cuda extensions are being compiled with a version of Cuda that does not match the version used to compile Pytorch binaries.  Pytorch binaries were compiled with Cuda 9.2.148.
    In some cases, a minor-version mismatch will not cause later errors:  https://github.com/NVIDIA/apex/pull/323#discussion_r287021798.  You can try commenting out this check (at your own risk).
  Running setup.py install for apex ... error
Cleaning up...
  Removing source in /tmp/pip-req-build-zfi8u8xr
Removed build tracker '/tmp/pip-req-tracker-su9lkj72'
ERROR: Command errored out with exit status 1: /lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-mazj60_q/install-record.txt --single-version-externally-managed --compile Check the logs for full command output.
Exception information:
Traceback (most recent call last):
  File "/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/cli/base_command.py", line 188, in main
    status = self.run(options, args)
  File "/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/commands/install.py", line 407, in run
    use_user_site=options.use_user_site,
  File "/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/req/__init__.py", line 58, in install_given_reqs
    **kwargs
  File "/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/req/req_install.py", line 959, in install
    spinner=spinner,
  File "/lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/lib/python3.6/site-packages/pip/_internal/utils/misc.py", line 931, in call_subprocess
    raise InstallationError(exc_msg)
pip._internal.exceptions.InstallationError: Command errored out with exit status 1: /lab/cedar/home/lin/.conda/envs/monorepoCancerCommunity/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-zfi8u8xr/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-mazj60_q/install-record.txt --single-version-externally-managed --compile Check the logs for full command output.
1 location(s) to search for versions of pip:
* https://pypi.org/simple/pip/
Getting page https://pypi.org/simple/pip/
Found index url https://pypi.org/simple
Starting new HTTPS connection (1): pypi.org:443
https://pypi.org:443 "GET /simple/pip/ HTTP/1.1" 200 11972
Analyzing links from page https://pypi.org/simple/pip/
  Found link https://files.pythonhosted.org/packages/3d/9d/1e313763bdfb6a48977b65829c6ce2a43eaae29ea2f907c8bbef024a7219/pip-0.2.tar.gz#sha256=88bb8d029e1bf4acd0e04d300104b7440086f94cc1ce1c5c3c31e3293aee1f81 (from https://pypi.org/simple/pip/), version: 0.2
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Given no hashes to check 127 links for project 'pip': discarding no candidates

Describe the Bug

Minimal Steps/Code to Reproduce the Bug

I used the origin/22.12-dev branch with commit: 7afa66e8f83cf881bcba3b859c5b283a9bf9865c

By running the following code:

from apex.normalization import InstanceNorm3dNVFuser
import torch

layer = InstanceNorm3dNVFuser(num_features=16, affine=False).to("cuda:0")
layer(torch.randn((1, 16, 8, 8, 8)).to("cuda:0"))

Error happened like:

File /usr/local/lib/python3.8/dist-packages/apex/normalization/instance_norm.py:138, in _InstanceNormNVFuser.forward(self, input)
    133     out = InstanceNormNVFuserFunction.apply(
    134             input, self.weight if self.weight is not None else self.dummy,
    135             self.bias if self.bias is not None else self.dummy, self.running_mean, self.running_var,
    136             self.training or not self.track_running_stats, self.momentum, self.eps)
    137 else:
--> 138     out = InstanceNormNVFuserFunction.apply(
    139             input, self.weight if self.weight is not None else self.dummy,
    140             self.bias if self.bias is not None else self.dummy, self.dummy, self.dummy,
    141             self.training or not self.track_running_stats, self.momentum, self.eps)
    142 return out

File /usr/local/lib/python3.8/dist-packages/apex/normalization/instance_norm.py:25, in InstanceNormNVFuserFunction.forward(ctx, input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps)
     23     _input = input
     24 assert _input.is_contiguous()
---> 25 result = instance_norm_nvfuser_cuda.forward(_input, weight, bias, running_mean, running_var,
     26                                             use_input_stats, momentum, eps, channels_last)
     27 if len(result) == 3:
     28     out, mean, invstd = result

RuntimeError: !mismatch INTERNAL ASSERT FAILED at "/opt/pytorch/pytorch/torch/csrc/jit/codegen/cuda/executor_utils.cpp":388, please report a bug to PyTorch. Found one or more invalid arguments: Argument is scalar type, but kernel parameter is not
Argument is scalar type, but kernel parameter is not

In addition, no error happen when affine=True

Expected Behavior

It should work correctly as torch.nn.InstanceNorm3d does

Environment

This PR adds a "capturable" argument to enable the Adam optimizer to be captured by CUDA graphs. This enhancement also supports non-static LR strategies.

This PR adds experimental support for the distributed Adam optimizer to overlap its parameter all-gather with the forward compute. It does this by monkey-patching the PyTorch Parameter class's __getattribute__ function. The optimizer step sets a flag in each of the parameters, and the first time a parameter is accessed in the forward pass it makes sure that its all-gather has finished.

This approach is delicate and prone to errors, hangs, or performance regressions (e.g. embeddings require special care since they pass the parameters into the C++ backend without activating our monkey-patched functions). In the long-term, it would be better to transition toward PyTorch's FSDP, which is more intrusive into the model but also more robust. That said, this functionality is disabled by default and I see some modest speedups (~2%) training GPT-3 175B on Selene.

This branch contains the work in https://github.com/NVIDIA/apex/pull/1551.

In this implementation, gamma tensor will be initialized to 0 (instead of 1) and the +1 is numerically handled. It is enabled by setting use_1p=True (default: False)

Minimal Steps/Code to Reproduce the Bug Facing lost of issues in apex installation with compatibility with pytorch, cuda. Please provide compatible pytorch versions with latest apex.