BatchNorm1d throws exception during eval with batch size of 1

[FusionCarcass]

I have a model that uses BatchNorm1d after a Linear layer that results in the exception below when using a batch size of 1 during eval. The same operation works fine in Python with a batch size of 1 during eval. I believe the correct behavior here should be to allow batch sizes of 1 during eval mode.

Unhandled exception: System.Runtime.InteropServices.ExternalException (0x80004005): Expected more than 1 value per channel when training, got input size [1, 1024]
Exception raised from batch_norm at ..\..\torch\csrc\api\include\torch/nn/functional/batchnorm.h:27 (most recent call first):
00007FFB94A5A29200007FFB94A5A230 c10.dll!c10::Error::Error [<unknown file> @ <unknown line number>]
00007FFB94A59D1E00007FFB94A59CD0 c10.dll!c10::detail::torchCheckFail [<unknown file> @ <unknown line number>]
00000296EC14AE2D00000296EC14AD40 torch_cpu.dll!torch::nn::RNNOptions::batch_first [<unknown file> @ <unknown line number>]
00000296EC19EE2A00000296EC19EC60 torch_cpu.dll!torch::nn::BatchNormImplBase<2,torch::nn::BatchNorm2dImpl>::forward [<unknown file> @ <unknown line number>]
00007FFB903790C400007FFB90379030 LibTorchSharp.DLL!THSNN_BatchNorm1d_forward [<unknown file> @ <unknown line number>]
00007FFAFBADD15D <unknown symbol address> !<unknown symbol> [<unknown file> @ <unknown line number>]

   at TorchSharp.torch.CheckForErrors()
   at TorchSharp.Modules.BatchNorm1d.forward(Tensor tensor)
   at TorchSharp.Modules.Sequential.forward(Tensor tensor)

Troubleshooting

I have verified that all BatchNorm1d modules are set to eval prior to prediction by running the following command:

InceptionResNetV4 module = new InceptionResNetV4();
module.load(fileName);
module.Eval();
module.apply((Module child) => { child.Eval(); });

Theories

I am training in python, then exporting the model to a .dat file and loading with the module.load method. I verified that the mean and var parameters of the BatchNorm1d are successfully loaded. I thought maybe the mean or var would be null, which might trigger a batching requirement during eval.

I think the issue is either related to loaded from a .dat file OR something not getting passed down to the native torch libraries correctly.

running_mean
[1024], type = Float32, device = cpu
running_var
[1024], type = Float32, device = cpu
num_batches_tracked
[], type = Int64, device = cpu

[NiklasGustafsson]

@FusionCarcass -- I'll take a look at it. If you have a smaller repro case, that would be great, otherwise I'll try to come up with one myself.

Could you check whether having a channel dimension of 1 makes any difference? BatchNorm1d is supposed to take either (N,L) or (N,C,L).

[NiklasGustafsson]

@FusionCarcass -- here's what I'm seeing in Python:

bn1 = torch.nn.BatchNorm1d(28)
p = bn1(torch.randn(16,28))
p = bn1(torch.randn(1,28))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:\Miniconda3\lib\site-packages\torch\nn\modules\module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Miniconda3\lib\site-packages\torch\nn\modules\batchnorm.py", line 168, in forward
    return F.batch_norm(
  File "D:\Miniconda3\lib\site-packages\torch\nn\functional.py", line 2280, in batch_norm
    _verify_batch_size(input.size())
  File "D:\Miniconda3\lib\site-packages\torch\nn\functional.py", line 2248, in _verify_batch_size
    raise ValueError("Expected more than 1 value per channel when training, got input size {}".format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 28])

Which seems to be the same error you're seeing with TorchSharp.

It only fails if the C dimension is missing. There's no error for this in either Python or .NET:

bn1 = torch.nn.BatchNorm1d(3)
p = bn1(torch.randn(1,3,28))

[FusionCarcass]

BatchNorm1d should work with tensor of shape (1, L) when set to eval mode. Training would require (N, L) tensors where N > 1.

This code works for me in python with input tensors (1, C, L) which gets flattened to (1, L) before the batchnorm where the exception occurs.

class InceptionResNetV4(nn.Module):
    def __init__(self):
        super().__init__()
       
        self.stack = nn.Sequential(
            Stem(1),
            InceptionResNetA(384),
            InceptionResNetA(384),
            InceptionResNetA(384),
            InceptionResNetA(384),
            InceptionResNetA(384),
            nn.AdaptiveMaxPool1d(10),
            nn.Flatten(),
            nn.Dropout(p=0.5),
            nn.Linear(3840, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(),
            nn.Linear(1024, 2)
        )
        
    def forward(self, x):
        output = self.stack(x)
        print(output.shape)
        return output

I export my model and load into the following C# model:

public class InceptionResNetV4 : Module {
	private readonly Sequential stack;

	public InceptionResNetV4() : base(string.Empty) {
		this.stack = Sequential(
			new Stem(1),
			new InceptionResNetA(384),
			new InceptionResNetA(384),
			new InceptionResNetA(384),
			new InceptionResNetA(384),
			new InceptionResNetA(384),
			AdaptiveMaxPool1d(10),
			Flatten(),
			Dropout(probability: 0.5f),
			Linear(3840, 1024),
			BatchNorm1d(1024),
			ReLU(),
			Linear(1024, 2)
		);

		this.RegisterComponents();
	}

	public override torch.Tensor forward(torch.Tensor t) {
		return this.stack.forward(t);
	}
}

[FusionCarcass]

I modified your example to make it work in python.

import torch
import torch.nn as nn
import torch.nn.functional as F


bn1 = torch.nn.BatchNorm1d(28)
bn1.eval()
p = bn1(torch.randn(16,28))
p = bn1(torch.randn(1,28))

[NiklasGustafsson]

Thanks!

[NiklasGustafsson]

Seems like the bug is in Sequential. This blows up in the second block, not the first:

            using (var pool = BatchNorm1d(28)) {
                pool.Eval();
                pool.forward(torch.ones(1, 28));
            }
            using (var pool = BatchNorm1d(28))
            using (var seq = Sequential(pool)) {
                seq.Eval();
                seq.forward(torch.ones(1, 28));
            }

[NiklasGustafsson]

Eval() does not seem to be propagated properly to all submodules, whether in a Sequential or a custom module.

I have a fix, and I'm going to push it together with the fix for #499, which is a big one.

[PointerGuide]

I hope we will see last fixes in nuget soon :)

[NiklasGustafsson]

It's coming... :-)