aten::ScalarImplicit is not Supported

Describe the issue:
Environment:

NNI version:2.10
Training service (local|remote|pai|aml|etc):
Client OS:
Server OS (for remote mode only):
Python version:
PyTorch/TensorFlow version:
Is conda/virtualenv/venv used?:
Is running in Docker?:

Configuration:

Experiment config (remember to remove secrets!):
Search space:

Log message:

nnimanager.log:
dispatcher.log:
nnictl stdout and stderr:

How to reproduce it?:
I have an model:
class get_model(nn.Module):

    def __init__(self, args, num_channel=3, num_class=40, **kwargs):
        super(get_model, self).__init__()
        self.args = args
        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(128)
        self.bn4 = nn.BatchNorm2d(256)
        self.bn5 = nn.BatchNorm1d(args.emb_dims)

        self.conv1 = nn.Sequential(nn.Conv2d(num_channel*2, 64, kernel_size=1, bias=False),
                                   self.bn1,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv2 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
                                   self.bn2,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv3 = nn.Sequential(nn.Conv2d(64*2, 128, kernel_size=1, bias=False),
                                   self.bn3,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv4 = nn.Sequential(nn.Conv2d(128*2, 256, kernel_size=1, bias=False),
                                   self.bn4,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv5 = nn.Sequential(nn.Conv1d(512, args.emb_dims, kernel_size=1, bias=False),
                                   self.bn5,
                                   nn.LeakyReLU(negative_slope=0.2))

        self.linear1 = nn.Linear(args.emb_dims*2, 512, bias=False)
        self.bn6 = nn.BatchNorm1d(512)
        self.dp1 = nn.Dropout(p=args.dropout)
        self.linear2 = nn.Linear(512, 256)
        self.bn7 = nn.BatchNorm1d(256)
        self.dp2 = nn.Dropout(p=args.dropout)
        self.linear3 = nn.Linear(256, num_class)

    def forward(self, x):
        batch_size = x.size()[0]
        x = get_graph_feature(x, k=self.args.k)
        x = self.conv1(x)
        x1 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x1, k=self.args.k)
        x = self.conv2(x)
        x2 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x2, k=self.args.k)
        x = self.conv3(x)
        x3 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x3, k=self.args.k)
        x = self.conv4(x)
        x4 = x.max(dim=-1, keepdim=False)[0]

        x = torch.cat((x1, x2, x3, x4), dim=1)

        x = self.conv5(x)
        x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
        x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1)
        x = torch.cat((x1, x2), 1)

        x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
        x = self.dp1(x)
        x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2)
        x = self.dp2(x)
        x = self.linear3(x)
        return x

And try prune it and speedup:
prune_config_list = [{
    'sparsity_per_layer': 0.5,
    'op_types': ['Linear', 'Conv2d']
}, {
    'exclude': True,
    'op_names': ['conv1.0', 'linear3']
}]

from nni.algorithms.compression.v2.pytorch.pruning import L2NormPruner
from nni.compression.pytorch.speedup import ModelSpeedup

pruner = L2NormPruner(classifier, prune_config_list)
masked_model, masks = pruner.compress()
pruner.show_pruned_weights()

# need to unwrap the model, if the model is wrapped before speedup
pruner._unwrap_model()

# speedup the model, for more information about speedup, please refer :doc:`pruning_speedup`.

ModelSpeedup(classifier, torch.randn(1, 3, 1024, device='cuda'), masks).speedup_model()
print(classifier)

But get the error:
[2023-03-13 10:06:07] simulated prune conv2.0 remain/total: 32/64
[2023-03-13 10:06:07] simulated prune conv3.0 remain/total: 64/128
[2023-03-13 10:06:07] simulated prune conv4.0 remain/total: 128/256
[2023-03-13 10:06:07] simulated prune linear1 remain/total: 256/512
[2023-03-13 10:06:07] simulated prune linear2 remain/total: 128/256
[2023-03-13 10:06:08] start to speedup the model
[2023-03-13 10:06:08] infer module masks...
[2023-03-13 10:06:08] Update mask for .aten::size.22
[2023-03-13 10:06:08] Update mask for .aten::size.25
[2023-03-13 10:06:08] Update mask for .aten::size.30
[2023-03-13 10:06:08] Update mask for .aten::size.33
[2023-03-13 10:06:08] Update mask for .aten::Int.23
[2023-03-13 10:06:08] Update mask for .aten::Int.24
[2023-03-13 10:06:08] Update mask for .aten::Int.26
[2023-03-13 10:06:08] Update mask for .aten::Int.27
[2023-03-13 10:06:08] Update mask for .aten::ScalarImplicit.28
[2023-03-13 10:06:08] ERROR: aten::ScalarImplicit is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~
[2023-03-13 10:06:08] Update mask for .aten::Int.29
[2023-03-13 10:06:08] Update mask for .aten::Int.31
[2023-03-13 10:06:08] Update mask for .aten::Int.32
[2023-03-13 10:06:08] Update mask for .aten::Int.34
[2023-03-13 10:06:08] Update mask for .aten::Int.35
[2023-03-13 10:06:08] Update mask for .aten::Int.36
[2023-03-13 10:06:08] Update mask for .aten::mul.57
[2023-03-13 10:06:08] Update mask for .aten::arange.50
Traceback (most recent call last):
  File "nni_optim.py", line 246, in <module>
    ModelSpeedup(classifier, torch.randn(1, 3, 1024, device='cuda'), masks).speedup_model()
  File "/home/zyfra-devbox/.conda/envs/nni/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 546, in speedup_model
    self.infer_modules_masks()
  File "/home/zyfra-devbox/.conda/envs/nni/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 383, in infer_modules_masks
    self.update_direct_sparsity(curnode)
  File "/home/zyfra-devbox/.conda/envs/nni/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 237, in update_direct_sparsity
    _auto_infer = AutoMaskInference(
  File "/home/zyfra-devbox/.conda/envs/nni/lib/python3.8/site-packages/nni/compression/pytorch/speedup/infer_mask.py", line 80, in __init__
    self.output = self.module(*dummy_input)
  File "/home/zyfra-devbox/.conda/envs/nni/lib/python3.8/site-packages/nni/compression/pytorch/speedup/jit_translate.py", line 227, in __call__
    assert len(args) >= len(self.undetermined)
AssertionError

I've checked several related issues:
https://github.com/microsoft/nni/issues/5097
https://github.com/microsoft/nni/issues/5090
But not find the workaround there was mentioned that  torch.full can cause the aten::ScalarImplicit. But I have no such operations.
What the workaround can be?
In addiction I've tried to prune with v2 api. I cloned and build the code from repo and tried:
from nni.compression.pytorch.speedup.v2 import ModelSpeedup
ModelSpeedup(classifier, torch.randn(1, 3, 1024, device='cuda'), masks).speedup_model()

But got:
[2023-03-13 08:12:57] simulated prune conv2.0 remain/total: 32/64
[2023-03-13 08:12:57] simulated prune conv3.0 remain/total: 64/128
[2023-03-13 08:12:57] simulated prune conv4.0 remain/total: 128/256
[2023-03-13 08:12:57] simulated prune linear1 remain/total: 256/512
[2023-03-13 08:12:57] simulated prune linear2 remain/total: 128/256
Traceback (most recent call last):
  File "nni_optim.py", line 248, in <module>
    ModelSpeedup(classifier, torch.randn(1, 3, 1024, device='cuda'), masks).speedup_model()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 93, in __init__
    self.graph_module = graph_module if isinstance(graph_module, GraphModule) else concrete_trace(model, self.dummy_input)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 1378, in concrete_trace
    graph = tracer.trace(root,
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 911, in trace
    (self.create_arg(OperatorPatcherContext.patch_run(fn, *args, *more_args, **kwargs)),),
  File "/root/devel/nni/nni/common/concrete_trace_utils/operator_patcher.py", line 270, in patch_run
    return new_func(*args, **kwargs)
  File "/root/devel/IAE/downstream_tasks/classification/models/dgcnn_clsft.py", line 98, in new_func
    x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
  File "/root/devel/nni/nni/common/concrete_trace_utils/operator_patcher.py", line 270, in patch_run
    return new_func(*args, **kwargs)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 659, in module_call_wrapper
    return self.create_proxy('call_module', module_qualified_name, args, kwargs)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 283, in create_proxy
    value_unwrapped = self.run_target(kind, target, args_unwrapped, kwargs_unwrapped)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 250, in run_target
    return OperatorPatcherContext.patch_run(mod, *args, **kwargs)
  File "/root/devel/nni/nni/common/concrete_trace_utils/operator_patcher.py", line 270, in patch_run
    return new_func(*args, **kwargs)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 639, in module_call_wrapper
    return _orig_module_call(mod, *args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/batchnorm.py", line 168, in forward
    return F.batch_norm(
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 1122, in func_wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py", line 2280, in batch_norm
    _verify_batch_size(input.size())
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 1122, in func_wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-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, 512])

So I've tried to increase batch size. Tried different numbers but get cuda OOM even if I have batch size of 2:
******************************
******************************
[2023-03-13 08:14:57] simulated prune conv2.0 remain/total: 32/64
[2023-03-13 08:14:57] simulated prune conv3.0 remain/total: 64/128
[2023-03-13 08:14:57] simulated prune conv4.0 remain/total: 128/256
[2023-03-13 08:14:57] simulated prune linear1 remain/total: 256/512
[2023-03-13 08:14:57] simulated prune linear2 remain/total: 128/256
[2023-03-13 08:14:57] Start to speedup the model...
[2023-03-13 08:14:57] Resolve the mask conflict before mask propagate...
[2023-03-13 08:14:57] Infer module masks...
[2023-03-13 08:14:57] Propagate original variables
[2023-03-13 08:14:57] Propagate variables for placeholder: x
[2023-03-13 08:14:57] Propagate variables for call_method: size
[2023-03-13 08:14:57] Propagate variables for call_function: getitem
[2023-03-13 08:14:57] Propagate variables for call_method: size_1
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_1
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_2
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_3
[2023-03-13 08:14:57] Propagate variables for call_method: view
[2023-03-13 08:14:57] Propagate variables for call_method: transpose
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous
[2023-03-13 08:14:57] Propagate variables for call_function: matmul
[2023-03-13 08:14:57] Propagate variables for call_function: mul
[2023-03-13 08:14:57] Propagate variables for call_function: pow_1
[2023-03-13 08:14:57] Propagate variables for call_function: sum_1
[2023-03-13 08:14:57] Propagate variables for call_function: neg
[2023-03-13 08:14:57] Propagate variables for call_function: sub
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_1
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_1
[2023-03-13 08:14:57] Propagate variables for call_function: sub_1
[2023-03-13 08:14:57] Propagate variables for call_method: topk
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_4
[2023-03-13 08:14:57] Propagate variables for call_function: arange
[2023-03-13 08:14:57] Propagate variables for call_method: view_1
[2023-03-13 08:14:57] Propagate variables for call_function: mul_1
[2023-03-13 08:14:57] Propagate variables for call_function: iadd
[2023-03-13 08:14:57] Propagate variables for call_method: view_2
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_2
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_2
[2023-03-13 08:14:57] Propagate variables for call_function: mul_2
[2023-03-13 08:14:57] Propagate variables for call_method: view_3
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_5
[2023-03-13 08:14:57] Propagate variables for call_method: view_4
[2023-03-13 08:14:57] Propagate variables for call_method: view_5
[2023-03-13 08:14:57] Propagate variables for call_method: repeat
[2023-03-13 08:14:57] Propagate variables for call_function: sub_2
[2023-03-13 08:14:57] Propagate variables for call_function: cat
[2023-03-13 08:14:57] Propagate variables for call_method: permute
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_3
[2023-03-13 08:14:57] Propagate variables for call_module: conv1_0
[2023-03-13 08:14:57] Propagate variables for call_module: bn1
[2023-03-13 08:14:57] Propagate variables for call_module: conv1_2
[2023-03-13 08:14:57] Propagate variables for call_method: max_1
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_6
[2023-03-13 08:14:57] Propagate variables for call_method: size_2
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_7
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_8
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_9
[2023-03-13 08:14:57] Propagate variables for call_method: view_6
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_3
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_4
[2023-03-13 08:14:57] Propagate variables for call_function: matmul_1
[2023-03-13 08:14:57] Propagate variables for call_function: mul_3
[2023-03-13 08:14:57] Propagate variables for call_function: pow_2
[2023-03-13 08:14:57] Propagate variables for call_function: sum_2
[2023-03-13 08:14:57] Propagate variables for call_function: neg_1
[2023-03-13 08:14:57] Propagate variables for call_function: sub_3
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_4
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_5
[2023-03-13 08:14:57] Propagate variables for call_function: sub_4
[2023-03-13 08:14:57] Propagate variables for call_method: topk_1
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_10
[2023-03-13 08:14:57] Propagate variables for call_function: arange_1
[2023-03-13 08:14:57] Propagate variables for call_method: view_7
[2023-03-13 08:14:57] Propagate variables for call_function: mul_4
[2023-03-13 08:14:57] Propagate variables for call_function: iadd_1
[2023-03-13 08:14:57] Propagate variables for call_method: view_8
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_5
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_6
[2023-03-13 08:14:57] Propagate variables for call_function: mul_5
[2023-03-13 08:14:57] Propagate variables for call_method: view_9
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_11
[2023-03-13 08:14:57] Propagate variables for call_method: view_10
[2023-03-13 08:14:57] Propagate variables for call_method: view_11
[2023-03-13 08:14:57] Propagate variables for call_method: repeat_1
[2023-03-13 08:14:57] Propagate variables for call_function: sub_5
[2023-03-13 08:14:57] Propagate variables for call_function: cat_1
[2023-03-13 08:14:57] Propagate variables for call_method: permute_1
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_7
[2023-03-13 08:14:57] Propagate variables for call_module: conv2_0
[2023-03-13 08:14:57] Propagate variables for call_module: bn2
[2023-03-13 08:14:57] Propagate variables for call_module: conv2_2
[2023-03-13 08:14:57] Propagate variables for call_method: max_2
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_12
[2023-03-13 08:14:57] Propagate variables for call_method: size_3
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_13
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_14
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_15
[2023-03-13 08:14:57] Propagate variables for call_method: view_12
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_6
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_8
[2023-03-13 08:14:57] Propagate variables for call_function: matmul_2
[2023-03-13 08:14:57] Propagate variables for call_function: mul_6
[2023-03-13 08:14:57] Propagate variables for call_function: pow_3
[2023-03-13 08:14:57] Propagate variables for call_function: sum_3
[2023-03-13 08:14:57] Propagate variables for call_function: neg_2
[2023-03-13 08:14:57] Propagate variables for call_function: sub_6
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_7
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_9
[2023-03-13 08:14:57] Propagate variables for call_function: sub_7
[2023-03-13 08:14:57] Propagate variables for call_method: topk_2
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_16
[2023-03-13 08:14:57] Propagate variables for call_function: arange_2
[2023-03-13 08:14:57] Propagate variables for call_method: view_13
[2023-03-13 08:14:57] Propagate variables for call_function: mul_7
[2023-03-13 08:14:57] Propagate variables for call_function: iadd_2
[2023-03-13 08:14:57] Propagate variables for call_method: view_14
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_8
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_10
[2023-03-13 08:14:57] Propagate variables for call_function: mul_8
[2023-03-13 08:14:57] Propagate variables for call_method: view_15
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_17
[2023-03-13 08:14:57] Propagate variables for call_method: view_16
[2023-03-13 08:14:57] Propagate variables for call_method: view_17
[2023-03-13 08:14:57] Propagate variables for call_method: repeat_2
[2023-03-13 08:14:57] Propagate variables for call_function: sub_8
[2023-03-13 08:14:57] Propagate variables for call_function: cat_2
[2023-03-13 08:14:57] Propagate variables for call_method: permute_2
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_11
[2023-03-13 08:14:57] Propagate variables for call_module: conv3_0
[2023-03-13 08:14:57] Propagate variables for call_module: bn3
[2023-03-13 08:14:57] Propagate variables for call_module: conv3_2
[2023-03-13 08:14:57] Propagate variables for call_method: max_3
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_18
[2023-03-13 08:14:57] Propagate variables for call_method: size_4
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_19
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_20
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_21
[2023-03-13 08:14:57] Propagate variables for call_method: view_18
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_9
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_12
[2023-03-13 08:14:57] Propagate variables for call_function: matmul_3
[2023-03-13 08:14:57] Propagate variables for call_function: mul_9
[2023-03-13 08:14:57] Propagate variables for call_function: pow_4
[2023-03-13 08:14:57] Propagate variables for call_function: sum_4
[2023-03-13 08:14:57] Propagate variables for call_function: neg_3
[2023-03-13 08:14:57] Propagate variables for call_function: sub_9
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_10
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_13
[2023-03-13 08:14:57] Propagate variables for call_function: sub_10
[2023-03-13 08:14:57] Propagate variables for call_method: topk_3
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_22
[2023-03-13 08:14:57] Propagate variables for call_function: arange_3
[2023-03-13 08:14:57] Propagate variables for call_method: view_19
[2023-03-13 08:14:57] Propagate variables for call_function: mul_10
[2023-03-13 08:14:57] Propagate variables for call_function: iadd_3
[2023-03-13 08:14:57] Propagate variables for call_method: view_20
[2023-03-13 08:14:57] Propagate variables for call_method: transpose_11
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_14
[2023-03-13 08:14:57] Propagate variables for call_function: mul_11
[2023-03-13 08:14:57] Propagate variables for call_method: view_21
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_23
[2023-03-13 08:14:57] Propagate variables for call_method: view_22
[2023-03-13 08:14:57] Propagate variables for call_method: view_23
[2023-03-13 08:14:57] Propagate variables for call_method: repeat_3
[2023-03-13 08:14:57] Propagate variables for call_function: sub_11
[2023-03-13 08:14:57] Propagate variables for call_function: cat_3
[2023-03-13 08:14:57] Propagate variables for call_method: permute_3
[2023-03-13 08:14:57] Propagate variables for call_method: contiguous_15
[2023-03-13 08:14:57] Propagate variables for call_module: conv4_0
[2023-03-13 08:14:57] Propagate variables for call_module: bn4
[2023-03-13 08:14:57] Propagate variables for call_module: conv4_2
[2023-03-13 08:14:57] Propagate variables for call_method: max_4
[2023-03-13 08:14:57] Propagate variables for call_function: getitem_24
[2023-03-13 08:14:57] Propagate variables for call_function: cat_4
[2023-03-13 08:14:57] Propagate variables for call_module: conv5_0
[2023-03-13 08:14:57] Propagate variables for call_module: bn5
[2023-03-13 08:14:57] Propagate variables for call_module: conv5_2
[2023-03-13 08:14:57] Propagate variables for call_function: adaptive_max_pool1d
[2023-03-13 08:14:57] Propagate variables for call_method: view_24
[2023-03-13 08:14:57] Propagate variables for call_function: adaptive_avg_pool1d
[2023-03-13 08:14:57] Propagate variables for call_method: view_25
[2023-03-13 08:14:57] Propagate variables for call_function: cat_5
[2023-03-13 08:14:57] Propagate variables for call_module: linear1
[2023-03-13 08:14:57] Propagate variables for call_module: bn6
[2023-03-13 08:14:57] Propagate variables for call_function: leaky_relu
[2023-03-13 08:14:57] Propagate variables for call_module: dp1
[2023-03-13 08:14:57] Propagate variables for call_module: linear2
[2023-03-13 08:14:57] Propagate variables for call_module: bn7
[2023-03-13 08:14:57] Propagate variables for call_function: leaky_relu_1
[2023-03-13 08:14:57] Propagate variables for call_module: dp2
[2023-03-13 08:14:57] Propagate variables for call_module: linear3
[2023-03-13 08:14:57] Propagate variables for output: output
[2023-03-13 08:14:57] Update direct sparsity...
Traceback (most recent call last):
  File "nni_optim.py", line 248, in <module>
    ModelSpeedup(classifier, torch.randn(2, 3, 1024, device='cuda'), masks).speedup_model()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 383, in speedup_model
    self.update_direct_sparsity()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 237, in update_direct_sparsity
    self.node_infos[node].mask_updater.direct_update_preprocess(self, node)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/mask_updater.py", line 95, in direct_update_preprocess
    node_info.output_randomize = tree_map_zip(lambda t: randomize_if_tensor(t, batch_dim, batch_size), node_info.output_origin)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/utils.py", line 73, in tree_map_zip
    return tree_map(fn, pytrees[0])
  File "/usr/local/lib/python3.8/dist-packages/torch/utils/_pytree.py", line 179, in tree_map
    return tree_unflatten([fn(i) for i in flat_args], spec)
  File "/usr/local/lib/python3.8/dist-packages/torch/utils/_pytree.py", line 179, in <listcomp>
    return tree_unflatten([fn(i) for i in flat_args], spec)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/mask_updater.py", line 95, in <lambda>
    node_info.output_randomize = tree_map_zip(lambda t: randomize_if_tensor(t, batch_dim, batch_size), node_info.output_origin)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/utils.py", line 55, in randomize_if_tensor
    new_obj = obj.clone().detach().contiguous()
RuntimeError: CUDA out of memory. Tried to allocate 80.00 MiB (GPU 0; 10.76 GiB total capacity; 9.16 GiB already allocated; 69.31 MiB free; 9.22 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

It's too strange because I have the NVIDIA RTX2080 TI with 12 gb of memorry and I'm able to train the model with batch size 32.
As I have 2 gpus I tried to run on parallel:
from nni.compression.pytorch.speedup.v2 import ModelSpeedup
classifier = torch.nn.DataParallel(classifier, device_ids=[0, 1])
ModelSpeedup(classifier, torch.randn(1, 3, 1024, device='cuda'), masks).speedup_model()

But got the next error:
  File "nni_optim.py", line 248, in <module>
    ModelSpeedup(classifier, torch.randn(2, 3, 1024, device='cuda'), masks).speedup_model()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 93, in __init__
    self.graph_module = graph_module if isinstance(graph_module, GraphModule) else concrete_trace(model, self.dummy_input)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 1378, in concrete_trace
    graph = tracer.trace(root,
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 589, in trace
    fn, args, more_args, kwargs = self.create_args_for_root(fn, isinstance(root, torch.nn.Module), concrete_args)
  File "/root/devel/nni/nni/common/concrete_trace_utils/concrete_tracer.py", line 464, in create_args_for_root
    raise RuntimeError(f"Tracing expected {len(arg_names)} arguments but got {len(concrete_args)} concrete arguments")
RuntimeError: Tracing expected 0 arguments but got 1 concrete arguments

What is the best way to deal with this problem?
UPD:
I've run the code on cpu with code from built library from source and got the next error:
[2023-03-13 09:55:49] simulated prune conv1.0 remain/total: 32/64
[2023-03-13 09:55:49] simulated prune conv2.0 remain/total: 32/64
[2023-03-13 09:55:49] simulated prune conv3.0 remain/total: 64/128
[2023-03-13 09:55:49] simulated prune conv4.0 remain/total: 128/256
[2023-03-13 09:55:49] simulated prune linear1 remain/total: 256/512
[2023-03-13 09:55:49] simulated prune linear2 remain/total: 128/256
[2023-03-13 09:55:50] Start to speedup the model...
[2023-03-13 09:55:50] Resolve the mask conflict before mask propagate...
[2023-03-13 09:55:50] Infer module masks...
[2023-03-13 09:55:50] Propagate original variables
[2023-03-13 09:55:50] Propagate variables for placeholder: x
[2023-03-13 09:55:50] Propagate variables for call_method: size
[2023-03-13 09:55:50] Propagate variables for call_function: getitem
[2023-03-13 09:55:50] Propagate variables for call_method: size_1
[2023-03-13 09:55:50] Propagate variables for call_function: getitem_1
[2023-03-13 09:55:50] Propagate variables for call_function: getitem_2
[2023-03-13 09:55:50] Propagate variables for call_function: getitem_3
[2023-03-13 09:55:50] Propagate variables for call_method: view
[2023-03-13 09:55:50] Propagate variables for call_method: transpose
[2023-03-13 09:55:50] Propagate variables for call_method: contiguous
[2023-03-13 09:55:50] Propagate variables for call_function: matmul
[2023-03-13 09:55:50] Propagate variables for call_function: mul
[2023-03-13 09:55:50] Propagate variables for call_function: pow_1
[2023-03-13 09:55:50] Propagate variables for call_function: sum_1
[2023-03-13 09:55:50] Propagate variables for call_function: neg
[2023-03-13 09:55:50] Propagate variables for call_function: sub
[2023-03-13 09:55:50] Propagate variables for call_method: transpose_1
[2023-03-13 09:55:50] Propagate variables for call_method: contiguous_1
[2023-03-13 09:55:50] Propagate variables for call_function: sub_1
[2023-03-13 09:55:50] Propagate variables for call_method: topk
[2023-03-13 09:55:50] Propagate variables for call_function: getitem_4
[2023-03-13 09:55:50] Propagate variables for call_function: arange
[2023-03-13 09:55:50] Propagate variables for call_method: view_1
[2023-03-13 09:55:50] Propagate variables for call_function: mul_1
[2023-03-13 09:55:50] Propagate variables for call_function: iadd
[2023-03-13 09:55:50] Propagate variables for call_method: view_2
[2023-03-13 09:55:50] Propagate variables for call_method: transpose_2
[2023-03-13 09:55:50] Propagate variables for call_method: contiguous_2
[2023-03-13 09:55:50] Propagate variables for call_function: mul_2
[2023-03-13 09:55:50] Propagate variables for call_method: view_3
[2023-03-13 09:55:50] Propagate variables for call_function: getitem_5
[2023-03-13 09:55:50] Propagate variables for call_method: view_4
[2023-03-13 09:55:50] Propagate variables for call_method: view_5
[2023-03-13 09:55:50] Propagate variables for call_method: repeat
[2023-03-13 09:55:50] Propagate variables for call_function: sub_2
[2023-03-13 09:55:50] Propagate variables for call_function: cat
[2023-03-13 09:55:50] Propagate variables for call_method: permute
[2023-03-13 09:55:50] Propagate variables for call_method: contiguous_3
[2023-03-13 09:55:50] Propagate variables for call_module: conv1_0
[2023-03-13 09:55:51] Propagate variables for call_module: bn1
[2023-03-13 09:55:51] Propagate variables for call_module: conv1_2
[2023-03-13 09:55:51] Propagate variables for call_method: max_1
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_6
[2023-03-13 09:55:51] Propagate variables for call_method: size_2
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_7
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_8
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_9
[2023-03-13 09:55:51] Propagate variables for call_method: view_6
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_3
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_4
[2023-03-13 09:55:51] Propagate variables for call_function: matmul_1
[2023-03-13 09:55:51] Propagate variables for call_function: mul_3
[2023-03-13 09:55:51] Propagate variables for call_function: pow_2
[2023-03-13 09:55:51] Propagate variables for call_function: sum_2
[2023-03-13 09:55:51] Propagate variables for call_function: neg_1
[2023-03-13 09:55:51] Propagate variables for call_function: sub_3
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_4
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_5
[2023-03-13 09:55:51] Propagate variables for call_function: sub_4
[2023-03-13 09:55:51] Propagate variables for call_method: topk_1
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_10
[2023-03-13 09:55:51] Propagate variables for call_function: arange_1
[2023-03-13 09:55:51] Propagate variables for call_method: view_7
[2023-03-13 09:55:51] Propagate variables for call_function: mul_4
[2023-03-13 09:55:51] Propagate variables for call_function: iadd_1
[2023-03-13 09:55:51] Propagate variables for call_method: view_8
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_5
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_6
[2023-03-13 09:55:51] Propagate variables for call_function: mul_5
[2023-03-13 09:55:51] Propagate variables for call_method: view_9
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_11
[2023-03-13 09:55:51] Propagate variables for call_method: view_10
[2023-03-13 09:55:51] Propagate variables for call_method: view_11
[2023-03-13 09:55:51] Propagate variables for call_method: repeat_1
[2023-03-13 09:55:51] Propagate variables for call_function: sub_5
[2023-03-13 09:55:51] Propagate variables for call_function: cat_1
[2023-03-13 09:55:51] Propagate variables for call_method: permute_1
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_7
[2023-03-13 09:55:51] Propagate variables for call_module: conv2_0
[2023-03-13 09:55:51] Propagate variables for call_module: bn2
[2023-03-13 09:55:51] Propagate variables for call_module: conv2_2
[2023-03-13 09:55:51] Propagate variables for call_method: max_2
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_12
[2023-03-13 09:55:51] Propagate variables for call_method: size_3
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_13
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_14
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_15
[2023-03-13 09:55:51] Propagate variables for call_method: view_12
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_6
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_8
[2023-03-13 09:55:51] Propagate variables for call_function: matmul_2
[2023-03-13 09:55:51] Propagate variables for call_function: mul_6
[2023-03-13 09:55:51] Propagate variables for call_function: pow_3
[2023-03-13 09:55:51] Propagate variables for call_function: sum_3
[2023-03-13 09:55:51] Propagate variables for call_function: neg_2
[2023-03-13 09:55:51] Propagate variables for call_function: sub_6
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_7
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_9
[2023-03-13 09:55:51] Propagate variables for call_function: sub_7
[2023-03-13 09:55:51] Propagate variables for call_method: topk_2
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_16
[2023-03-13 09:55:51] Propagate variables for call_function: arange_2
[2023-03-13 09:55:51] Propagate variables for call_method: view_13
[2023-03-13 09:55:51] Propagate variables for call_function: mul_7
[2023-03-13 09:55:51] Propagate variables for call_function: iadd_2
[2023-03-13 09:55:51] Propagate variables for call_method: view_14
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_8
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_10
[2023-03-13 09:55:51] Propagate variables for call_function: mul_8
[2023-03-13 09:55:51] Propagate variables for call_method: view_15
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_17
[2023-03-13 09:55:51] Propagate variables for call_method: view_16
[2023-03-13 09:55:51] Propagate variables for call_method: view_17
[2023-03-13 09:55:51] Propagate variables for call_method: repeat_2
[2023-03-13 09:55:51] Propagate variables for call_function: sub_8
[2023-03-13 09:55:51] Propagate variables for call_function: cat_2
[2023-03-13 09:55:51] Propagate variables for call_method: permute_2
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_11
[2023-03-13 09:55:51] Propagate variables for call_module: conv3_0
[2023-03-13 09:55:51] Propagate variables for call_module: bn3
[2023-03-13 09:55:51] Propagate variables for call_module: conv3_2
[2023-03-13 09:55:51] Propagate variables for call_method: max_3
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_18
[2023-03-13 09:55:51] Propagate variables for call_method: size_4
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_19
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_20
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_21
[2023-03-13 09:55:51] Propagate variables for call_method: view_18
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_9
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_12
[2023-03-13 09:55:51] Propagate variables for call_function: matmul_3
[2023-03-13 09:55:51] Propagate variables for call_function: mul_9
[2023-03-13 09:55:51] Propagate variables for call_function: pow_4
[2023-03-13 09:55:51] Propagate variables for call_function: sum_4
[2023-03-13 09:55:51] Propagate variables for call_function: neg_3
[2023-03-13 09:55:51] Propagate variables for call_function: sub_9
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_10
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_13
[2023-03-13 09:55:51] Propagate variables for call_function: sub_10
[2023-03-13 09:55:51] Propagate variables for call_method: topk_3
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_22
[2023-03-13 09:55:51] Propagate variables for call_function: arange_3
[2023-03-13 09:55:51] Propagate variables for call_method: view_19
[2023-03-13 09:55:51] Propagate variables for call_function: mul_10
[2023-03-13 09:55:51] Propagate variables for call_function: iadd_3
[2023-03-13 09:55:51] Propagate variables for call_method: view_20
[2023-03-13 09:55:51] Propagate variables for call_method: transpose_11
[2023-03-13 09:55:51] Propagate variables for call_method: contiguous_14
[2023-03-13 09:55:51] Propagate variables for call_function: mul_11
[2023-03-13 09:55:51] Propagate variables for call_method: view_21
[2023-03-13 09:55:51] Propagate variables for call_function: getitem_23
[2023-03-13 09:55:51] Propagate variables for call_method: view_22
[2023-03-13 09:55:51] Propagate variables for call_method: view_23
[2023-03-13 09:55:51] Propagate variables for call_method: repeat_3
[2023-03-13 09:55:52] Propagate variables for call_function: sub_11
[2023-03-13 09:55:52] Propagate variables for call_function: cat_3
[2023-03-13 09:55:52] Propagate variables for call_method: permute_3
[2023-03-13 09:55:52] Propagate variables for call_method: contiguous_15
[2023-03-13 09:55:52] Propagate variables for call_module: conv4_0
[2023-03-13 09:55:52] Propagate variables for call_module: bn4
[2023-03-13 09:55:52] Propagate variables for call_module: conv4_2
[2023-03-13 09:55:52] Propagate variables for call_method: max_4
[2023-03-13 09:55:52] Propagate variables for call_function: getitem_24
[2023-03-13 09:55:52] Propagate variables for call_function: cat_4
[2023-03-13 09:55:52] Propagate variables for call_module: conv5_0
[2023-03-13 09:55:52] Propagate variables for call_module: bn5
[2023-03-13 09:55:52] Propagate variables for call_module: conv5_2
[2023-03-13 09:55:52] Propagate variables for call_function: adaptive_max_pool1d
[2023-03-13 09:55:52] Propagate variables for call_method: view_24
[2023-03-13 09:55:52] Propagate variables for call_function: adaptive_avg_pool1d
[2023-03-13 09:55:52] Propagate variables for call_method: view_25
[2023-03-13 09:55:52] Propagate variables for call_function: cat_5
[2023-03-13 09:55:52] Propagate variables for call_module: linear1
[2023-03-13 09:55:52] Propagate variables for call_module: bn6
[2023-03-13 09:55:52] Propagate variables for call_function: leaky_relu
[2023-03-13 09:55:52] Propagate variables for call_module: dp1
[2023-03-13 09:55:52] Propagate variables for call_module: linear2
[2023-03-13 09:55:52] Propagate variables for call_module: bn7
[2023-03-13 09:55:52] Propagate variables for call_function: leaky_relu_1
[2023-03-13 09:55:52] Propagate variables for call_module: dp2
[2023-03-13 09:55:52] Propagate variables for call_module: linear3
[2023-03-13 09:55:52] Propagate variables for output: output
[2023-03-13 09:55:52] Update direct sparsity...
[2023-03-13 09:55:56] Update direct mask for placeholder: x
[2023-03-13 09:55:56] Update direct mask for call_method: size
[2023-03-13 09:55:56] Update direct mask for call_function: getitem
[2023-03-13 09:55:56] Update direct mask for call_method: size_1
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_1
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_2
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_3
[2023-03-13 09:55:56] Update direct mask for call_method: view
[2023-03-13 09:55:56] Update direct mask for call_method: transpose
[2023-03-13 09:55:56] Update direct mask for call_method: contiguous
[2023-03-13 09:55:56] Update direct mask for call_function: matmul
[2023-03-13 09:55:56] Update direct mask for call_function: mul
[2023-03-13 09:55:56] Update direct mask for call_function: pow_1
[2023-03-13 09:55:56] Update direct mask for call_function: sum_1
[2023-03-13 09:55:56] Update direct mask for call_function: neg
[2023-03-13 09:55:56] Update direct mask for call_function: sub
[2023-03-13 09:55:56] Update direct mask for call_method: transpose_1
[2023-03-13 09:55:56] Update direct mask for call_method: contiguous_1
[2023-03-13 09:55:56] Update direct mask for call_function: sub_1
[2023-03-13 09:55:56] Update direct mask for call_method: topk
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_4
[2023-03-13 09:55:56] Update direct mask for call_function: arange
[2023-03-13 09:55:56] Update direct mask for call_method: view_1
[2023-03-13 09:55:56] Update direct mask for call_function: mul_1
[2023-03-13 09:55:56] Update direct mask for call_function: iadd
[2023-03-13 09:55:56] Update direct mask for call_method: view_2
[2023-03-13 09:55:56] Update direct mask for call_method: transpose_2
[2023-03-13 09:55:56] Update direct mask for call_method: contiguous_2
[2023-03-13 09:55:56] Update direct mask for call_function: mul_2
[2023-03-13 09:55:56] Update direct mask for call_method: view_3
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_5
[2023-03-13 09:55:56] Update direct mask for call_method: view_4
[2023-03-13 09:55:56] Update direct mask for call_method: view_5
[2023-03-13 09:55:56] Update direct mask for call_method: repeat
[2023-03-13 09:55:56] Update direct mask for call_function: sub_2
[2023-03-13 09:55:56] Update direct mask for call_function: cat
[2023-03-13 09:55:56] Update direct mask for call_method: permute
[2023-03-13 09:55:56] Update direct mask for call_method: contiguous_3
[2023-03-13 09:55:56] Update direct mask for call_module: conv1_0
[2023-03-13 09:55:56] Update direct mask for call_module: bn1
[2023-03-13 09:55:56] Update direct mask for call_module: conv1_2
[2023-03-13 09:55:56] Update direct mask for call_method: max_1
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_6
[2023-03-13 09:55:56] Update direct mask for call_method: size_2
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_7
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_8
[2023-03-13 09:55:56] Update direct mask for call_function: getitem_9
[2023-03-13 09:55:56] Update direct mask for call_method: view_6
[2023-03-13 09:55:56] Update direct mask for call_method: transpose_3
[2023-03-13 09:55:56] Update direct mask for call_method: contiguous_4
[2023-03-13 09:55:57] Update direct mask for call_function: matmul_1
[2023-03-13 09:55:57] Update direct mask for call_function: mul_3
[2023-03-13 09:55:57] Update direct mask for call_function: pow_2
[2023-03-13 09:55:57] Update direct mask for call_function: sum_2
[2023-03-13 09:55:57] Update direct mask for call_function: neg_1
[2023-03-13 09:55:57] Update direct mask for call_function: sub_3
[2023-03-13 09:55:57] Update direct mask for call_method: transpose_4
[2023-03-13 09:55:57] Update direct mask for call_method: contiguous_5
[2023-03-13 09:55:57] Update direct mask for call_function: sub_4
[2023-03-13 09:55:57] Update direct mask for call_method: topk_1
[2023-03-13 09:55:57] Update direct mask for call_function: getitem_10
[2023-03-13 09:55:57] Update direct mask for call_function: arange_1
[2023-03-13 09:55:57] Update direct mask for call_method: view_7
[2023-03-13 09:55:57] Update direct mask for call_function: mul_4
[2023-03-13 09:55:57] Update direct mask for call_function: iadd_1
[2023-03-13 09:55:57] Update direct mask for call_method: view_8
[2023-03-13 09:55:57] Update direct mask for call_method: transpose_5
[2023-03-13 09:55:57] Update direct mask for call_method: contiguous_6
[2023-03-13 09:55:57] Update direct mask for call_function: mul_5
[2023-03-13 09:55:57] Update direct mask for call_method: view_9
[2023-03-13 09:55:57] Update direct mask for call_function: getitem_11
[2023-03-13 09:55:57] Update direct mask for call_method: view_10
[2023-03-13 09:55:57] Update direct mask for call_method: view_11
[2023-03-13 09:55:57] Update direct mask for call_method: repeat_1
[2023-03-13 09:55:57] Update direct mask for call_function: sub_5
[2023-03-13 09:55:57] Update direct mask for call_function: cat_1
[2023-03-13 09:55:58] Update direct mask for call_method: permute_1
[2023-03-13 09:55:58] Update direct mask for call_method: contiguous_7
[2023-03-13 09:55:58] Update direct mask for call_module: conv2_0
[2023-03-13 09:55:58] Update direct mask for call_module: bn2
[2023-03-13 09:55:58] Update direct mask for call_module: conv2_2
[2023-03-13 09:55:58] Update direct mask for call_method: max_2
[2023-03-13 09:55:58] Update direct mask for call_function: getitem_12
[2023-03-13 09:55:58] Update direct mask for call_method: size_3
[2023-03-13 09:55:58] Update direct mask for call_function: getitem_13
[2023-03-13 09:55:58] Update direct mask for call_function: getitem_14
[2023-03-13 09:55:58] Update direct mask for call_function: getitem_15
[2023-03-13 09:55:58] Update direct mask for call_method: view_12
[2023-03-13 09:55:58] Update direct mask for call_method: transpose_6
[2023-03-13 09:55:58] Update direct mask for call_method: contiguous_8
[2023-03-13 09:55:58] Update direct mask for call_function: matmul_2
[2023-03-13 09:55:59] Update direct mask for call_function: mul_6
[2023-03-13 09:55:59] Update direct mask for call_function: pow_3
[2023-03-13 09:55:59] Update direct mask for call_function: sum_3
[2023-03-13 09:55:59] Update direct mask for call_function: neg_2
[2023-03-13 09:55:59] Update direct mask for call_function: sub_6
[2023-03-13 09:55:59] Update direct mask for call_method: transpose_7
[2023-03-13 09:55:59] Update direct mask for call_method: contiguous_9
[2023-03-13 09:55:59] Update direct mask for call_function: sub_7
[2023-03-13 09:55:59] Update direct mask for call_method: topk_2
[2023-03-13 09:55:59] Update direct mask for call_function: getitem_16
[2023-03-13 09:55:59] Update direct mask for call_function: arange_2
[2023-03-13 09:55:59] Update direct mask for call_method: view_13
[2023-03-13 09:55:59] Update direct mask for call_function: mul_7
[2023-03-13 09:55:59] Update direct mask for call_function: iadd_2
[2023-03-13 09:55:59] Update direct mask for call_method: view_14
[2023-03-13 09:55:59] Update direct mask for call_method: transpose_8
[2023-03-13 09:55:59] Update direct mask for call_method: contiguous_10
[2023-03-13 09:55:59] Update direct mask for call_function: mul_8
[2023-03-13 09:55:59] Update direct mask for call_method: view_15
[2023-03-13 09:55:59] Update direct mask for call_function: getitem_17
[2023-03-13 09:55:59] Update direct mask for call_method: view_16
[2023-03-13 09:55:59] Update direct mask for call_method: view_17
[2023-03-13 09:55:59] Update direct mask for call_method: repeat_2
[2023-03-13 09:55:59] Update direct mask for call_function: sub_8
[2023-03-13 09:55:59] Update direct mask for call_function: cat_2
[2023-03-13 09:56:00] Update direct mask for call_method: permute_2
[2023-03-13 09:56:00] Update direct mask for call_method: contiguous_11
[2023-03-13 09:56:00] Update direct mask for call_module: conv3_0
[2023-03-13 09:56:00] Update direct mask for call_module: bn3
[2023-03-13 09:56:00] Update direct mask for call_module: conv3_2
[2023-03-13 09:56:00] Update direct mask for call_method: max_3
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_18
[2023-03-13 09:56:01] Update direct mask for call_method: size_4
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_19
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_20
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_21
[2023-03-13 09:56:01] Update direct mask for call_method: view_18
[2023-03-13 09:56:01] Update direct mask for call_method: transpose_9
[2023-03-13 09:56:01] Update direct mask for call_method: contiguous_12
[2023-03-13 09:56:01] Update direct mask for call_function: matmul_3
[2023-03-13 09:56:01] Update direct mask for call_function: mul_9
[2023-03-13 09:56:01] Update direct mask for call_function: pow_4
[2023-03-13 09:56:01] Update direct mask for call_function: sum_4
[2023-03-13 09:56:01] Update direct mask for call_function: neg_3
[2023-03-13 09:56:01] Update direct mask for call_function: sub_9
[2023-03-13 09:56:01] Update direct mask for call_method: transpose_10
[2023-03-13 09:56:01] Update direct mask for call_method: contiguous_13
[2023-03-13 09:56:01] Update direct mask for call_function: sub_10
[2023-03-13 09:56:01] Update direct mask for call_method: topk_3
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_22
[2023-03-13 09:56:01] Update direct mask for call_function: arange_3
[2023-03-13 09:56:01] Update direct mask for call_method: view_19
[2023-03-13 09:56:01] Update direct mask for call_function: mul_10
[2023-03-13 09:56:01] Update direct mask for call_function: iadd_3
[2023-03-13 09:56:01] Update direct mask for call_method: view_20
[2023-03-13 09:56:01] Update direct mask for call_method: transpose_11
[2023-03-13 09:56:01] Update direct mask for call_method: contiguous_14
[2023-03-13 09:56:01] Update direct mask for call_function: mul_11
[2023-03-13 09:56:01] Update direct mask for call_method: view_21
[2023-03-13 09:56:01] Update direct mask for call_function: getitem_23
[2023-03-13 09:56:01] Update direct mask for call_method: view_22
[2023-03-13 09:56:01] Update direct mask for call_method: view_23
[2023-03-13 09:56:01] Update direct mask for call_method: repeat_3
[2023-03-13 09:56:02] Update direct mask for call_function: sub_11
[2023-03-13 09:56:02] Update direct mask for call_function: cat_3
[2023-03-13 09:56:02] Update direct mask for call_method: permute_3
[2023-03-13 09:56:03] Update direct mask for call_method: contiguous_15
[2023-03-13 09:56:04] Update direct mask for call_module: conv4_0
[2023-03-13 09:56:04] Update direct mask for call_module: bn4
[2023-03-13 09:56:04] Update direct mask for call_module: conv4_2
[2023-03-13 09:56:04] Update direct mask for call_method: max_4
[2023-03-13 09:56:04] Update direct mask for call_function: getitem_24
[2023-03-13 09:56:04] Update direct mask for call_function: cat_4
[2023-03-13 09:56:04] Update direct mask for call_module: conv5_0
[2023-03-13 09:56:05] Update direct mask for call_module: bn5
[2023-03-13 09:56:05] Update direct mask for call_module: conv5_2
[2023-03-13 09:56:05] Update direct mask for call_function: adaptive_max_pool1d
[2023-03-13 09:56:05] Update direct mask for call_method: view_24
[2023-03-13 09:56:05] Update direct mask for call_function: adaptive_avg_pool1d
[2023-03-13 09:56:05] Update direct mask for call_method: view_25
[2023-03-13 09:56:05] Update direct mask for call_function: cat_5
[2023-03-13 09:56:05] Update direct mask for call_module: linear1
[2023-03-13 09:56:05] Update direct mask for call_module: bn6
[2023-03-13 09:56:05] Update direct mask for call_function: leaky_relu
[2023-03-13 09:56:05] Update direct mask for call_module: dp1
[2023-03-13 09:56:05] Update direct mask for call_module: linear2
[2023-03-13 09:56:05] Update direct mask for call_module: bn7
[2023-03-13 09:56:05] Update direct mask for call_function: leaky_relu_1
[2023-03-13 09:56:05] Update direct mask for call_module: dp2
[2023-03-13 09:56:05] Update direct mask for call_module: linear3
[2023-03-13 09:56:05] Update direct mask for output: output
[2023-03-13 09:56:05] Update indirect sparsity...
[2023-03-13 09:56:05] Update indirect mask for output: output
[2023-03-13 09:56:05] Update indirect mask for call_module: linear3
[2023-03-13 09:56:05] Update indirect mask for call_module: dp2
[2023-03-13 09:56:05] Update indirect mask for call_function: leaky_relu_1
[2023-03-13 09:56:05] Update indirect mask for call_module: bn7
[2023-03-13 09:56:05] Update indirect mask for call_module: linear2
[2023-03-13 09:56:05] Update indirect mask for call_module: dp1
[2023-03-13 09:56:05] Update indirect mask for call_function: leaky_relu
[2023-03-13 09:56:05] Update indirect mask for call_module: bn6
[2023-03-13 09:56:05] Update indirect mask for call_module: linear1
[2023-03-13 09:56:05] Update indirect mask for call_function: cat_5
[2023-03-13 09:56:05] Update indirect mask for call_method: view_25
[2023-03-13 09:56:05] Update indirect mask for call_function: adaptive_avg_pool1d
[2023-03-13 09:56:05] Update indirect mask for call_method: view_24
[2023-03-13 09:56:05] Update indirect mask for call_function: adaptive_max_pool1d
[2023-03-13 09:56:05] Update indirect mask for call_module: conv5_2
[2023-03-13 09:56:05] Update indirect mask for call_module: bn5
[2023-03-13 09:56:05] Update indirect mask for call_module: conv5_0
[2023-03-13 09:56:05] Update indirect mask for call_function: cat_4
[2023-03-13 09:56:05] Update indirect mask for call_function: getitem_24
[2023-03-13 09:56:05] Update indirect mask for call_method: max_4
[2023-03-13 09:56:05] Update indirect mask for call_module: conv4_2
Traceback (most recent call last):
  File "nni_optim.py", line 246, in <module>
    ModelSpeedup(classifier, torch.randn(2, 3, 1024, device='cpu'), masks).speedup_model()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 384, in speedup_model
    self.update_indirect_sparsity()
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/model_speedup.py", line 259, in update_indirect_sparsity
    self.node_infos[node].mask_updater.indirect_update_process(self, node)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/mask_updater.py", line 438, in indirect_update_process
    indirect_fn(model_speedup, node)
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/mask_updater.py", line 382, in indirect_activation
    input_grad = tree_map_zip(lambda t, m: (t * m).type_as(t) if isinstance(m, torch.Tensor) else t, \
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/utils.py", line 81, in tree_map_zip
    return tree_unflatten([fn(*args) for args in zip(*flat_args_list)], spec_list[0])
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/utils.py", line 81, in <listcomp>
    return tree_unflatten([fn(*args) for args in zip(*flat_args_list)], spec_list[0])
  File "/root/devel/nni/nni/compression/pytorch/speedup/v2/mask_updater.py", line 382, in <lambda>
    input_grad = tree_map_zip(lambda t, m: (t * m).type_as(t) if isinstance(m, torch.Tensor) else t, \
TypeError: unsupported operand type(s) for *: 'NoneType' and 'Tensor'

I ran into the previous error as well, but the last one seems to be solvable by replacing the lambda with a regular function

lambda should not be in the code of the model

Thanks for tips. Do you mean that lambda should not be in the code of the model? Or one should change lambda to function in code of library in module mask_updater.py":
        input_grad = tree_map_zip(lambda t, m: (t * m).type_as(t) if isinstance(m, torch.Tensor) else t, \
            model_speedup.node_infos[node].output_grad, model_speedup.node_infos[node].output_masks)

I think that lambda should not be in the code of the model.

Ok. thanks. Double checked and I have no lambdas in my code, so the issue is in another place

Hi, may I know what is the function get_graph_feature()?

Sure, I forgot to include:
def knn(x, k):
    inner = -2 * torch.matmul(x.transpose(2, 1).contiguous(), x)
    xx = torch.sum(x ** 2, dim=1, keepdim=True)
    pairwise_distance = -xx - inner - xx.transpose(2, 1).contiguous()
    idx = pairwise_distance.topk(k=k, dim=-1)[1]
    return idx

def get_graph_feature(x, k=20, idx=None, extra_dim=False):

    batch_size, num_dims, num_points = x.size()
    x = x.view(batch_size, -1, num_points)
    if idx is None:
        if extra_dim is False:
            idx = knn(x, k=k)
        else:
            idx = knn(x[:, 6:], k=k)  # idx = knn(x[:, :3], k=k)

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points
    idx += idx_base
    idx = idx.view(-1)

    x = x.transpose(2, 1).contiguous()
    feature = x.view(batch_size*num_points, -1)[idx, :]
    feature = feature.view(batch_size, num_points, k, num_dims)
    x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
    feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous()

    return feature  # (batch_size, 2 * num_dims, num_points, k)

I tried this model recently. The latest nni speedup works well on CPU. However, the output model was still unable to run because of incorrect shapes.
For the 5 conv_bn blocks, you should write it this way because it helps nni detect the bn module better.
self.conv1 = nn.Sequential(nn.Conv2d(num_channel*2, 64, kernel_size=1, bias=False),
                           nn.BatchNorm2d(64),
                           nn.LeakyReLU(negative_slope=0.2))
self.conv2 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
                           nn.BatchNorm2d(64),
                           nn.LeakyReLU(negative_slope=0.2))
self.conv3 = nn.Sequential(nn.Conv2d(64*2, 128, kernel_size=1, bias=False),
                           nn.BatchNorm2d(128),
                           nn.LeakyReLU(negative_slope=0.2))
self.conv4 = nn.Sequential(nn.Conv2d(128*2, 256, kernel_size=1, bias=False),
                           nn.BatchNorm2d(256),
                           nn.LeakyReLU(negative_slope=0.2))
self.conv5 = nn.Sequential(nn.Conv1d(512, args.emb_dims, kernel_size=1, bias=False),
                           nn.BatchNorm1d(args.emb_dims),
                           nn.LeakyReLU(negative_slope=0.2))

However, since we haven't got a replacer for Conv1d, it is hard to prune this structure.
x = torch.cat((x1, x2, x3, x4), dim=1)
x = self.conv5(x)

Let me discuss this with @J-shang.


kola/test/ignition/fs: add swap_activation test

This test aims to reproduce swap activation behavior on FCL through CLC
Signed-off-by: Mathieu Tortuyaux<EMAIL_ADDRESS>
Related to:

https://github.com/flatcar-linux/Flatcar/issues/133
https://www.flatcar.org/docs/latest/setup/storage/adding-swap/#adding-swap-with-a-container-linux-config

Testing done
Tested locally with Stable-3033.2.1 and Alpha-3127.0.0

[x] Changelog entries added in the respective changelog/ directory (user-facing change, bug fix, security fix, update)

@krnowak @dongsupark thanks for your reviews. Suggestion have been addressed in a fixup! commit.


feature request : texture coordinates for sphere and cylinder primitives

I'd like the ability to specify texture colors maps and texture coordinates for sphere and cylinder primitives as can already be done for trianglemeshes.
For ospNewGeometry("trianglemesh") just supply a vertex.textcoord and a material with an osp:Texture2d to get a color for each pixel come from a lookup into the texture.
See: https://gitlab.kitware.com/vtk/vtk/blob/master/Rendering/OSPRay/vtkOSPRayPolyDataMapperNode.cxx#L420 for example.
With ospNewGeometry("sphere"|"cylinders") there could be something like an "offset_textcoord" to define the same.
For spheres each sphere should be entirely the same color determined by its single u/v lookup result. For cylinders the u/v should interpolate from one end to the other and then the texture lookup for each fragment will be different down the cylinder.
This will resolve longstanding VTK/PV issues where ospray results look notably different for point clouds, stream lines and wireframe representations.

Re spheres: if each sphere still has a constant color, then how about adding a color parameter instead? Rendering would be much faster (texture fetches are expensive). The downside is probably more work in the ParaView->OSPRay adapter, and more memory consumption (when using vec3f colors).
Re cylinders: so you have a u/v pair for both vertices (v0 and v1), which are linearly interpolated before fetching the texel (as opposed to interpolating the fetched colors)? The texcoords are 2D, not just u (they are used like for a 1D line segment)?

For points I could use direct colors as you suggest and it would be optimal. For other rendering modes I already use "color"+ "offset_colorID" and "materialList"+"offset_materialID" to do something similar so it isn't that much work on my end. I still recommend that OSPRay support texture lookup for spheres though. It will let you support real texture maps, more advanced materials, and just for the aesthetic of symmetry with triangle mesh's capability.
For cylinders yes, we pass in both u and v, but v is 1 wide, constant and effectively a don't care. Interpolating R,G,B along the line gives the wrong results. Consider a color map that says 0.0=255,0,0; 0.5=0,255,0; 1.0=0,0,255. The midpoint of a line with 0.0 and 1.0 at the ends should be 0.5=0,255,0 (green) and is if you interpolate first and color after. Interpolating RGB directly will give 128,0,128 (purple) which is wrong. This one is a high priority for me.

Should work now, please give it a try.

Looking good so far. Will take me a day or five to get the rest of the modes checked out but this is a big improvement. From vtk's TestOSPRayRenderMesh, the point cloud with texture map in GL (squares) and OSP (spheres). Before OSP side had always just been white.

"Will take me a day or five..." or less. ;)
https://gitlab.kitware.com/vtk/vtk/merge_requests/2789

Wonderful :-)


Update forum to newest version

https://mybb.com/download/
Newest is 1.8.8. We are using 1.6.x.
Take backup before.

Maybe this isn't that important since we might want to do #31 instead?

#31 is an easy temporary issue, this one will take substantial amount of time.

@Deleetdk I think you mean the other way around

In retrospect, this was a great idea. New forum is much better.


(QENG-780) request sets of VMs instead of one VM at a time

This PR facilitates use of vmpooler's batch-request POST API
(https://github.com/puppetlabs/vmpooler#post-vm) within beaker.

Refer to this link for build results (access rights to CI server needed):
http://jenkins-beaker.delivery.puppetlabs.net//job/Beaker Combined Smoketest/514/
:red_circle: Test failed.

@puppetlabs-jenkins retest this please

Refer to this link for build results (access rights to CI server needed):
http://jenkins-beaker.delivery.puppetlabs.net//job/Beaker Combined Smoketest/515/
:red_circle: Test failed.

Well that last commit apparently wasn't the right fix... @kevpl @anodelman halp!

@sschneid, I took a look at this locally, and adding a mock body= virtual attribute to spec/mocks.rb solves this problem for me and gets your tests passing (inside MockNet::HTTP::Post):
def body= *args
  hash
end

@puppetlabs-jenkins retest this please

Refer to this link for build results (access rights to CI server needed):
http://jenkins-beaker.delivery.puppetlabs.net//job/Beaker Combined Smoketest/518/
:green_heart: Test passed.

@sschneid sorry to nit, but would you put a tag on the Post method commit, so we can track it better?


Serializers to sort collections

In serialized formats, it's often nice for a better overview to have collection entries sorted naturally / alphabetically / by some comparator. So what do you guys think about adding convenience serializers to do that?

I'm not sure if it would be more convenient than simply sorting your data before serializing. I guess for bigger structures it could be?
How do you propose it would work?

I guess for bigger structures it could be?

Yes, and for immutable data class hierarchies you don't want to create copies just to sort a deeply nested collection for serialization. In memory, the order does not matter. This is just for readability and canonization in the serialized format.

How do you propose it would work?

Similar to these, but more generically implemented.

Okay,  sure! Would you like to contribute it?

Maybe, yes, eventually 😀 I doesn't have high priority for me currently, though.


improvement use new way for decimal arithmetic precision promotion

Proposed changes
Issue Number: close #xxx
FE changes from #27492

decimalv3: use 38, min(decimalOverflowScale, ret_scale) as result precision if overflow

decimalv2 with largeint or bigint will promotion to decimalv3

decimalOverflowScale could be config by session variable

BE:

throw exception if it overflows for decimal arithmetics
throw exception if it overflows when casting among number types

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

run buildall

run pipelinex_p0

run buildall

run buildall

run buildall

run buildall

run feut


add Slack link to README

This adds a link to the azure-sdk-for-go channel in the Gophers Slack to the README.

[x] The purpose of this PR is explained in this or a referenced issue.
[x] The PR does not update generated files.

These files are managed by the codegen framework at [Azure/autorest.go][].

[x] The PR targets the latest branch.
[ ] Tests are included and/or updated for code changes.
[ ] Updates to [CHANGELOG.md][] are included.
[ ] Apache v2 license headers are included in each file.

Yes, I do and @devigned drops by sometimes too.

I'm lurking most everyday.


audio_common: 0.3.8-1 in 'noetic/distribution.yaml' [bloom]

Increasing version of package(s) in repository audio_common to 0.3.8-1:

upstream repository: https://github.com/ros-drivers/audio_common.git
release repository: https://github.com/ros-gbp/audio_common-release.git
distro file: noetic/distribution.yaml
bloom version: 0.9.7
previous version for package: 0.3.7-1

audio_capture

No changes

audio_common

No changes

audio_common_msgs

No changes

audio_play
* Merge pull request #151 <https://github.com/ros-drivers/audio_common/issues/151> from knorth55/do-timestamp-false
  [audio_play] set do_timestamp false
* set do_timestamp false
* Contributors: Shingo Kitagawa

sound_play
* Merge pull request #155 <https://github.com/ros-drivers/audio_common/issues/155> from garaemon/use-myargv
  Use rospy.myargv() instead of sys.argv to support remapping
* Use rospy.myargv() instead of sys.argv to support remapping
* Merge pull request #154 <https://github.com/ros-drivers/audio_common/issues/154> from mikaelarguedas/fix_say_python3
* update to support no iso-8859-15 language (#1 <https://github.com/ros-drivers/audio_common/issues/1>)
  * support non iso-8859-15 language
  * encode only for python2
* convert items to an iterator
* make cleanup compatible with Python 3
* catch AttributeError to handle python3 strings
* Contributors: Mikael Arguedas, Ryohei Ueda, Shingo Kitagawa

holding for the upcoming Noetic sync


Honeywell round connected thermostat

Component to support the honeywell round connected thermostat.
I could use some guidance on creating the documentation for this. The guidelines for contributing are not very obvious to me (Don't know whether it is me or the docs ;-)  ).
Really great platform ! Keep up the great work !
Sander.

I could use some guidance on creating the documentation for this. The guidelines for contributing are not very obvious to me (Don't know whether it is me or the docs ;-) ).

It's OK to add some details as a docstring to the header of the file if you don't want to deal with https://home-assistant.io/. A sample entry for the configuration.yaml file is a good start.
Would be nice if you could point out where we can improve the CONTRIBUTING file. Thanks.

Below a part of the contributing.md file with my comments (bold italic. Sorry about that. Didn't know how to do it in another way..). I am python hobbyist really interested in contributing. To me your project feels really professional up to a level I am not used to. So some of the below explanation is a bit too dense for me unfortunately. Any help greatly appreciated !
Adding support for a new device
For help on building your component, please see the developer documentation on home-assistant.io.
After you finish adding support for your device:

Update the supported devices in the README.md file.
The README.md in the repo doesn't contain a list of supported devices. It has got a link to a list of supported devices. But where or how to change that list is not that obvious

Add any new dependencies to requirements_all.txt. There is no ordering right now, so just add it to the end.

Update the .coveragerc file.

Provide some documentation for home-assistant.io. The documentation is handled in a separate git repository. It's OK to add a docstring with configuration details to the file header.
The docs repo has a _components folder in which I found documentation regarding components. I am not very familiar with the repo and I had a feeling this wasn't really the place to put my documentation. Being underscored I felt is automatically generated or something

Make sure all your code passes pylint and flake8 (PEP8 and some more) validation. To check your repository, run ./script/lint.

Create a Pull Request against the dev branch of Home Assistant.

Check for comments and suggestions on your Pull Request and keep an eye on the Travis output.

If you've added a component:

Update the file home-assistant-icons.html

Where is this file located ?
with an icon for your domain (pick one from this list).
All instructions below are about the user interface. I am not sure whether I need to do something with this when adding (in my case) a Honeywell thermostat.

Update the demo component with two states that it provides
Add your component to home-assistant.conf.example

Since you've updated home-assistant-icons.html, you've made changes to the frontend:

Run build_frontend. This will build a new version of the frontend. Make sure you add the changed files frontend.py and frontend.html to the commit.

Setting states
It is the responsibility of the component to maintain the states of the devices in your domain. Each device should be a single state and, if possible, a group should be provided that tracks the combined state of the devices.
A state can have several attributes that will help the frontend in displaying your state:

friendly_name: this name will be used as the name of the device
entity_picture: this picture will be shown instead of the domain icon
unit_of_measurement: this will be appended to the state in the interface
hidden: This is a suggestion to the frontend on if the state should be hidden

These attributes are defined in homeassistant.components.
Proper Visibility Handling
Generally, when creating a new entity for Home Assistant you will want it to be a class that inherits the homeassistant.helpers.entity.Entity class. If this is done, visibility will be handled for you.
You can set a suggestion for your entity's visibility by setting the hidden property by doing something similar to the following.
self.hidden = True

This will SUGGEST that the active frontend hides the entity. This requires that the active frontend support hidden cards (the default frontend does) and that the value of hidden be included in your attributes dictionary (see above). The Entity abstract class will take care of this for you.
Remember: The suggestion set by your component's code will always be overwritten by user settings in the configuration.yaml file. This is why you may set hidden to be False, but the property may remain True (or vice-versa).
Working on the frontend
The frontend is composed of Polymer web-components and compiled into the file frontend.html. During development you do not want to work with the compiled version but with the seperate files. To have Home Assistant serve the seperate files, set development=1 for the http-component in your config.

Notes on PyLint and PEP8 validation
In case a PyLint warning cannot be avoided, add a comment to disable the PyLint check for that line. This can be done using the format # pylint: disable=YOUR-ERROR-NAME. Example of an unavoidable PyLint warning is if you do not use the passed in datetime if you're listening for time change.

Thanks for your comment. I have tried to include your input.

It looks good. Had some minor comments, please update .coveragerc and requirements_all.txt.

Uhm.. now what do I do ? I modified the .coverage and requirements_all.txt file and I see it automagically appearing below your last comment @balloob .. Is all good or should I still make a new pull request ?

That's totally fine, this list is a chronological list of commits and comments related to this PR.

This is the initial documentation for the platform. Feedback and additions would be welcome :-)


stopping animations

Is there a way to stop ol.animation static methods in the middle of an animation?
if there isn't, can i implement stopping an animation in  ol.PreRenderFunction ?

The recomendation is to make usage questions here.

ol.animation are now deprecated (see #6079); view.animate must be used instead.
All animations are cancelled when the user interacts with the map or when anything calls view.setCenter(), view.setResolution(), or view.setRotation().

view.cancelAnimations();


Added ContentStringFormat TemplateBinding to ToolTip

Fixed a bug in the ToolTip style where the ContentStringFormat was being ignored.

@nrpog :+1:


What is the difference between this solution and interfaces solution?

Hello,
what would be the difference between this approach and using an interface for SQLite commands then you can use a different SQLite library but through the same interface?
I hope my question is clear ..English is not my native language.

What I mean like React Native cross libraries
For example, a pop dialog is working in android and ios native then react native interface just make a code that matches for both platforms and you can use it same
like these libraries
https://github.com/jacklam718/react-native-popup-dialog
https://github.com/andpor/react-native-sqlite-storage

interface NetworkHelper {
override fun get(url: String?): String
override fun post(url: String?, params :String?): String
}

Unless I misunderstand what the libraries do, that’s what this is. A
common library that renders to the native platforms.
On Tue, Jun 26, 2018 at 2:17 PM Amr Abd El Wahab El Desouky <
<EMAIL_ADDRESS>wrote:

What I mean like React Native cross libraries
For example, a pop dialog is working in android and ios native then react
native interface just make a code that matches for both platforms and you
can use it same
like these libraries
https://github.com/jacklam718/react-native-popup-dialog
https://github.com/andpor/react-native-sqlite-storage
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/touchlab/knarch.db/issues/48#issuecomment-400286313,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAELIN3heqvsWUhuSAnAwqE5bugD2cLqks5uAiY8gaJpZM4U3p9y
.

--
Kevin Galligan
https://twitter.com/kpgalligan

exactly it use native code for every platform


API key names can have the same name

Steps to reproduce

Go to User Center and add a api key.
Add another api key with the same name.

Result

The names are the same, can't tell the difference.

Expected behavior

Cannot add api keys with the same name.

Environment

Walrus version: main 829ac7a2a65c2ee5092f994e81f8b41214e376a4
UI version: dev-e2db642

Additional context
Add any other context about the problem here.

Environment

Walrus version: main de80628d3f1e14c28c190b9afea1608170721bc8
UI version: dev-efebf61

Test Result: pass


update packages

:x: Build TheIdServer 4.2.1-fix-dependencies1-0220 failed (commit https://github.com/Aguafrommars/TheIdServer/commit/5d42a17fb5 by @)

:white_check_mark: Build TheIdServer 4.2.1-fix-dependencies1-0221 completed (commit https://github.com/Aguafrommars/TheIdServer/commit/226e711b5f by @)


KAFKA-4688: The Docker image should contain version <IP_ADDRESS> of Kafka

I think we also need to update vagrantfile. I'll take a look when I get home.

@cmccabe https://github.com/apache/kafka/blob/trunk/vagrant/base.sh#L64-L69 is where we install it for Vagrant VMs.

And I should mention that I made sure the corresponding URLs will be there -- in that case we haven't been relying on random mirrors and just used a reliable source of our own.


Language of the input keyboard is not respected

Is your question not already answered in the FAQ?

[X] I made sure the question is not listed in the FAQ.

Is this a general question and not a technical issue?

[X] I am sure my question is not about a technical issue.

Question
Hello,
no matter what settings you specify via the variables, the keyboard remains in English style. How can this be changed? Difficult, especially with the special characters

i think u missed something

Problem exists with and without these variables. i think it could be a problem in the settings of the middleware. maybe it isnt there changed.


Add release notes template for automatic release generation.

Main Changes
Added a YML file that lets GitHub automatically write our release notes / changelogs for us, excluding bots and only including PRs with the enhancement, bug-fix, or breaking-change labels.

I'm not sure if this will play nicely with GoReleaser, since that is what is generating the releases now.

I'm not sure if this will play nicely with GoReleaser, since that is what is generating the releases now.

Gotcha. Is there anything in particular that you really like about GoReleaser?

Let's see what they do when they work together, can't see it being harmful


fix(DTFS2-6789): allow read-only users to have access to all banks

Introduction
When we added the read-only role, we thought that only the admin role was able to have access to all banks (when the user object has bank.id = '*'). However, since deploying, we have realised that the read-only role should be able to have access to all banks too.
Resolution
The validateBank middleware in portal and gef-ui now allows users to access all banks if they have bank.id === '*' and the user has the admin or read-only role
Misc
There is a isSuperUser function in portal, portal-api, and gef-ui that checks user.bank.id === '*' but does not check the user roles so they do not need to change for this fix

Codecov Report

Merging #2296 (01a0b22) into main (2390d16) will decrease coverage by 1.62%.
Report is 105 commits behind head on main.
The diff coverage is 3.37%.

@@            Coverage Diff             @@
##             main    #2296      +/-   ##
==========================================
- Coverage   21.01%   19.40%   -1.62%
==========================================
  Files        1030     1010      -20
  Lines       25229    24072    -1157
  Branches     4081     3935     -146
==========================================
- Hits         5303     4672     -631
+ Misses      17370    16869     -501
+ Partials     2556     2531      -25

Flag
Coverage Δ

api-test
0.48% <0.00%> (?)

ui-test
79.31% <ø> (+14.50%)
:arrow_up:

unit-test
3.30% <4.65%> (+2.58%)
:arrow_up:

Flags with carried forward coverage won't be shown. Click here to find out more.

Files
Coverage Δ

...s-function/mappings/deal/helpers/get-deal-value.js
0.00% <ø> (ø)

...re-functions/acbs-function/mappings/party/agent.js
0.00% <ø> (ø)

...ure-functions/acbs-function/mappings/party/bank.js
0.00% <ø> (ø)

...ons/acbs-function/mappings/party/bondBeneficary.js
0.00% <ø> (ø)

...nctions/acbs-function/mappings/party/bondIssuer.js
0.00% <ø> (ø)

...re-functions/acbs-function/mappings/party/buyer.js
0.00% <ø> (ø)

...ctions/acbs-function/mappings/party/indemnifier.js
0.00% <ø> (ø)

dtfs-central-api/src/helpers/isNumber.js
100.00% <100.00%> (ø)

...-function/acbs-amend-facility-loan-record/index.js
0.00% <0.00%> (ø)

...unction/acbs-amend-facility-master-record/index.js
0.00% <0.00%> (ø)

... and 46 more

... and 556 files with indirect coverage changes
:mega: Codecov offers a browser extension for seamless coverage viewing on GitHub. Try it in Chrome or Firefox today!


PSF Templating

Feature Request: PSF Templating tools
Some tools for fitting typical 2D PSF templates would be nice. VIP currently offers

[ ] 2D Airy Disk
[ ] 2D Gaussian (both symmetric and asymmetric)
[ ] 2D Moffat (I've never heard of this- need to research)

Interface
I'm not to certain about the interface. Ideally, there are some frameworks that exist that allow simple model definitions that I can just export for use. Otherwise I may have to wrap some simple code with Optim.jl
A more structured solution involves creating a struct for the outputs that can easily retrieve the parameters, the evaluated model, and information about the inputs so things like residuals can be easily plotted via plot_recipes. This is what VIP does and is kind of nice to get the automatic plots, but I'm not sure how I feel about it as a library function.
Organization
All this should go in some separate file psf_templates.jl and a corresponding documentation section should be made for it.

I've taken this idea and turned it into the package JuliaAstro/PSFModels.jl


Cryptowatch API has been shut down

Code needs to be cleaned-up - Cryptowatch API has been shut down

One possible source for getting fiat currencies could be:
curl -L "https://datahub.io/core/currency-codes/r/codes-all.csv"


[TASK] Expose different types for different runtimes

Multi-types package for different deployments (i.e. Tangle, Protocol-substrate, dkg-substrate separated).

Goal

have users that only require interaction with protocol-substrate (no dkg) to create api instances that use vAnchor types.

i.e. import '@webb-tools/types/protocol-substrate

tasks

expose yarn scripts with cli arguments for calling into types generation scripts to support different folder type generation output.
Update relevant scripts

Cons

Anyone importing the types package will download all deployment types. Could be large?

Since it's mostly types, it won't add to bundle size - just repo size.

We can add the types to the include types so we don't have to import the types while using the API
{
...
  "include": [
	"@webb-tools/types",
    "src/**/*",
<EMAIL_ADDRESS>  ],
..
}

This issue is now closed as it has become outdated.


Match Region display with the site

We tweaked the display of this information in the website, this matches that format.

New release has this, looks good:
pscale region list
  NAME (7)                            SLUG           ENABLED
 ----------------------------------- -------------- ---------
  AWS us-east-1 (Northern Virginia)   us-east        Yes
  AWS us-west-2 (Oregon)              us-west        Yes
  AWS eu-west-1 (Dublin)              eu-west        Yes
  AWS ap-south-1 (Mumbai)             ap-south       Yes
  AWS ap-southeast-1 (Singapore)      ap-southeast   Yes
  AWS ap-northeast-1 (Tokyo)          ap-northeast   Yes
  AWS eu-central-1 (Frankfurt)        eu-central     Yes


Restart-Service should call systemctl restart on systemd-present Linux distributions.

Summary of the new feature / enhancement
I want to be able to restart sshd from Windows and Linux in the same way after modifying sshd_config. This is almost possible, because Get-Content and Set-Content are already cross-platform. However, I currently must interact with the init-system when restarting services on Linux, despite an abstraction for net and sc (Restart-Service) already existing in PowerShell, and the potential solution being, I expect, so easy.
https://learn.microsoft.com/en-us/powershell/scripting/learn/remoting/ssh-remoting-in-powershell-core?view=powershell-7.3#install-the-ssh-service-on-a-windows-computer:~:text=restart-service sshd
PS /$Home> Restart-Service sshd
Restart-Service: The term 'Restart-Service' is not recognized as a name of a cmdlet, function, script file, or executable program.
Check the spelling of the name, or if a path was included, verify that the path is correct and try again.

Proposed technical implementation details (optional)
Call systemctl restart sshd if systemd if Get-Command systemctl returns True.

PS /$Home> systemctl start sshd && journalctl -fexu sshd
Dec 28 18:29:10 beedell.rokejulianlockhart.1669806072 systemd[1]: Starting sshd.service - OpenSSH server daemon...
░░ Subject: A start job for unit sshd.service has begun execution
░░ Defined-By: systemd
░░ A start job for unit sshd.service has begun execution.
░░ The job identifier is 322.
Dec 28 18:29:10 beedell.rokejulianlockhart.1669806072 sshd[1412]: Server listening on <IP_ADDRESS> port 22.
Dec 28 18:29:10 beedell.rokejulianlockhart.1669806072 sshd[1412]: Server listening on :: port 22.

https://serverfault.com/a/858700

Take it easy. /usr/bin/bash can't restart a service either, it can't even create directory without /usr/bin/mkdir.

... and the potential solution being, I expect, so easy.

It may be simple solution for this single case but it raises a whole new set of expectations that PowerShell will provide systems management functions on all platforms equivalent to what it does on windows.

The term 'Restart-Service' is not recognized as a name of a cmdlet, function, script ...

I would suggest a better solution would be to have new GitHub projects that implements the equivalent missing cmdlets for different platforms much like PowerShell/ThreadJob is a separate project. Given the platforms that PowerShell supports as a scripting language, that would imply there need to be equivalents for Linux as well as macOS, and for Linux for systemd, openrc or traditional init systems.

The @PowerShell/wg-powershell-cmdlets discussed this.  We agreed that there is existing precedent to provide cmdlet abstraction over Unix commands (see Restart-Computer as an example).
In this case, we recommend a fallback system to search for common commands used by various distros (including macOS) to restart a service on those platforms and if no common command is found then fail the entire operation,.  We also suggest looking if sudo exists and using that to avoid the inevitable permission problem as sudo cannot elevate cmdlets.  Check the exit code and if non-zero, emit STDERR as an ErrorRecord.
We would expect Get-Service, Stop-Service and Start-Service to also use the same/similar code.
For capability beyond what is provided by the existing cmdlet, we would recommend as a separate module published to PowerShellGallery.

https://github.com/PowerShell/PowerShell/issues/18863#issuecomment-1916091227

@microsoft-github-policy-service, that doesn't matter. How long an issue takes to be remediated does not whatsoever impact its importance.

https://github.com/PowerShell/PowerShell/issues/18863#issuecomment-2255130326

https://github.com/PowerShell/PowerShell/issues/18863#issuecomment-1917427178


Pooled connections are dying after timeout

I use AWS RDS, where default timeout connection is 8 hours. After 8 hours one of pooled connections is dying because of this limit and ping() can't detect it (or reset() doesn't work as expected with really dead connections).

Please try just published v1.2.1
MyPool::get_conn should deal with broken connections automatically.

Thank you very much. Updated, will report tomorrow.

No more 500's, thank you very much! :)

@blackbeam I'm having what appears to be the same issue, but with the current version 7.1.2.
I'm using the pool in what I believe is a pretty standard way:
let server: hyper::server::Server = /* server */;
let pool = my::Pool::new(&*DB_URL).unwrap();
let handler = move |req, res| {
    let pool = pool.clone();
    // respond
};

server.handle(handler).unwrap();

This works fine for a while. After an extended period of time (more than a few hours), requests hang indefinitely. It's not an issue with Hyper (at least not directly), because requests that don't involve getting a pooled connection are successful. The pool itself seems to be intact because (in my simple setup) it is being cloned whether it is used or not, and some requests (as noted) are successful.
Sorry I can't provide more useful information. I'm not a MySQL expert, and the issue isn't providing any error details (or even any errors!).

I think it could be related to blackbeam/mysql_async#2
Which mysql/mariadb version do you use?

Just tried to reproduce this using latest mysql and mariadb but couldn't.
Behavior was emulated using wait_timeout = 5 of mysql server (default is 8 hours).
According to inserted printnls pool connections was dropped by server (as desired), but pool successfully fixed them for following calls to Pool::get_conn, Pool::prepare and Pool::prep_exec.
Anyway. Your case doesn't look like timed out connection because it shouldn't hang indefinitely.
Need further investigation.
I am still interested in which server and version do you use.

Works fine with 5.6 and 5.7, both on busy and idling servers (aws rds).
On Sun, 18 Dec 2016 at 16:43, AnatolyI<EMAIL_ADDRESS>wrote:

Just tried to reproduce this using latest mysql and mariadb but couldn't.
Behavior was emulated using wait_timeout = 5 of mysql server (default is
8 hours).
According to inserted printnls pool connections was dropped by server (as
desired), but pool successfully fixed them for following calls to
Pool::get_conn, Pool::prepare and Pool::prep_exec.
Anyway. Your case doesn't look like timed out connection because it
shouldn't hang indefinitely.
Need further investigation.
I am still interested in which server and version do you use.
—
You are receiving this because you modified the open/close state.
Reply to this email directly, view it on GitHub
https://github.com/blackbeam/rust-mysql-simple/issues/34#issuecomment-267821910,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AAgIEODb8cu6h_RDQNDC4ftEIaBfookzks5rJTh3gaJpZM4G9dT7
.

Thanks for the response!
The server MySQL version is 5.6.26. wait_timeout is the default 8 hours (I don't have control over server configuration). Please let me know if there are any other variables that might be relevant.
(Also, I know that it is not your project, that it uses completely different logic, and that there are reasons to prefer your pool implementation, but the connection pooling of r2d2-mysql works fine for my case. Just in case that is relevant.)

Also, to clarify, this is not MariaDB: version_comment shows MySQL Community Server (GPL)

Pool uses a bit different routines for get_conn and prep_exec. Which one do you use inside of a handler?
And also for clarification, do mysql server located on the same host?

I'm using prep_exec for a SELECT query.
The MySQL server is on a separate server.
Thanks!

Ahh.. Still can't reproduce.
Could you please provide result of SHOW VARIABLES;?
And also what if you set read_timeout and write_timeout? Will it hang indefinitely?

Sorry for the long delay—I was on break.
I've pasted the output for SHOW VARIABLES at https://gist.github.com/kardeiz/5e9196a397a0e2603760db9ec46484d3.
I'm currently waiting to see whether setting read/write timeout on the client makes a difference.

Interesting—setting read_timeout and write_timeout to 5 seconds on mysql::OptsBuilder seems to have resolved the issue. However, there is a long delay (5-10 seconds) in processing a request if I wait more than 5 seconds between requests (I guess the client needs to completely reconnect?). Would the optimal value be just under 8 hours (the server's read/write timeout value)?

Could you please set new tcp_keepalive_time_ms option to something like one minute?
You need to switch to master branch to do this:
[dependencies.mysql]
git = "https://github.com/blackbeam/rust-mysql-simple.git"


fix: keeping Markdown content while resizing window on Dashboard

SUMMARY
#11257
After editing markdown and resizing window, edited text remains and window stays in Editor mode.
BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF
Before

After

TEST PLAN

ADDITIONAL INFORMATION

[ ] Has associated issue:
[ ] Changes UI
[ ] Requires DB Migration.
[ ] Confirm DB Migration upgrade and downgrade tested.
[ ] Introduces new feature or API
[ ] Removes existing feature or API

Codecov Report

Merging #11392 into master will decrease coverage by 5.87%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #11392      +/-   ##
==========================================
- Coverage   65.78%   59.90%   -5.88%
==========================================
  Files         838      394     -444
  Lines       39841    24960   -14881
  Branches     3655        0    -3655
==========================================
- Hits        26208    14952   -11256
+ Misses      13532    10008    -3524
+ Partials      101        0     -101

Flag
Coverage Δ

#cypress
?

#javascript
?

#python
59.90% <ø> (-1.03%)
:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

superset/db_engines/hive.py
0.00% <0.00%> (-85.72%)
:arrow_down:

superset/databases/commands/create.py
31.91% <0.00%> (-59.58%)
:arrow_down:

superset/db_engine_specs/hive.py
53.90% <0.00%> (-30.08%)
:arrow_down:

superset/views/database/mixins.py
59.64% <0.00%> (-22.81%)
:arrow_down:

superset/db_engine_specs/mysql.py
79.59% <0.00%> (-12.25%)
:arrow_down:

superset/db_engine_specs/presto.py
69.76% <0.00%> (-11.67%)
:arrow_down:

superset/databases/commands/update.py
85.71% <0.00%> (-8.17%)
:arrow_down:

superset/databases/api.py
83.49% <0.00%> (-7.08%)
:arrow_down:

superset/databases/dao.py
94.11% <0.00%> (-5.89%)
:arrow_down:

superset/views/database/validators.py
78.94% <0.00%> (-5.27%)
:arrow_down:

... and 464 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update cae54ac...8d993bb. Read the comment docs.

Thanks for the fix. when markdown component is in edit mode, or under resizing, did you notice there is a solid border (like an empty box) below markdown component?

This box is not correct (and it was introduced recently). Could you also remove this box? thanks!

Thanks a lot for suggestion @graceguo-supercat 👍 I added it to separate PR to not mix those two bugs: #11423


handle subscripts for functions and vars, eg. f_1(x) != f_2(x)

Previously:
for functions... proccess_sympy(f_1(x)) would return f*x, now it returns f_{1}(x)
for variables...proccess_sympy(x_1+x_2) would return x+x, now it returns x_{1}+x_{2}.
Also handles expressions in the subscripts, eg. f_{2x+3} is preserved.

Thanks for the pull request! Is it possible to make the subscripts print correctly with the latex printer?
For example:
>>> process_latex.process_sympy("h_{\\int x dx}(x)")
h_{Integral(x, x)}(x)
>>> latex(_)
'\\operatorname{h_{{Integral(x, x)}}}{\\left (x \\right )}'

which looks like , instead of

Hrm... made some progress but tricky part is getting a regex to recognize a subscript with \\([a-z,A-Z]*), because some of these have substrings that look like escape chars. Need to move on to my main project, but if you want I can upload what I have so far, though it doesn't solve the problem completely.


[main] Upgrade to latest dependencies

Cron -knative-prow-robot
/cc knative-sandbox/eventing-writers lberk
/assign knative-sandbox/eventing-writers lberk
Produced by: knative-sandbox/knobots/actions/update-deps
Details:
bumping knative.dev/eventing bd37622...16771b5:
  > 16771b5 Bump cloudevents/sdk-go to v2.12 (# 6552)
  > e5154d3 Update community files (# 6550)
bumping knative.dev/hack 6887217...0198902:
  > 0198902 Format go code (# 226)
  > 7dff557 Update community files (# 233)

/lgtm
/approve