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Autograd.grad breaks in hypernetwork implementations
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4
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134
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October 30, 2023
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Multiple backprops through encoder part of the network
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0
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67
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October 25, 2023
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RuntimeErrNo RNNmodule but error: cudnn RNN backward can only be called in training mode
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1
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81
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October 25, 2023
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Masked_scatter_ problem
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1
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77
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October 24, 2023
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Is the index_add_ function differentiable?
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1
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84
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October 24, 2023
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How may one detach only a certain variable in the computational graph?
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3
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79
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October 24, 2023
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Getting the Jacobian of transformer encoder outputs with respect to the inputs
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3
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92
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October 23, 2023
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Overlap backward and optimizer update
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4
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123
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October 23, 2023
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How does one reuse the autograd computational graph
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1
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103
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October 22, 2023
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Function ‘LinalgSvdBackward0’ returned nan values in its 0th output
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1
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120
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October 22, 2023
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Retain_graph = True Not working
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3
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108
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October 22, 2023
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Function 'LinalgEighBackward0' returned nan
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6
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314
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October 21, 2023
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Should the requires_grad=False of model weights when using torch.inference_mode
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4
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165
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October 20, 2023
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Difference between torch.Tensor.requires_grad and torch.nn.parameter.Parameter
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2
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95
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October 20, 2023
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Verify the implementation of loss function
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3
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122
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October 18, 2023
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Save the gradient of the intermediate tensor
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2
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102
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October 17, 2023
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Use custom stream in custom torch.autograd.Function
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1
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83
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October 17, 2023
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Grad returns repeating values for independent batch entries and doesn't want to second order derivative
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2
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100
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October 17, 2023
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Gradient of input and model parameters
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2
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79
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October 16, 2023
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Reusing Jacobian and Hessian computational graph
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4
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1248
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October 16, 2023
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PyTorch learning rate - possible edition in an external file?
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2
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56
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October 14, 2023
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Scheduler.step() after each epoch or after each minibatch
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3
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5245
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October 14, 2023
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GCN using GraphConv in half precision
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0
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82
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October 14, 2023
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Efficient computation of Hessian with respect to network weights using autograd.grad and symmetry of Hessian matrix
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8
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696
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October 13, 2023
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Custom torch.autograd.Function backward()
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1
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68
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October 12, 2023
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RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [100, 2, 6]], which is output 0 of SigmoidBackward0, is at version 6; expected version 0 instead. Hint: the backtrace fur
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1
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147
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October 11, 2023
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Most efficient way to run model on gpu while optimizing input
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1
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93
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October 11, 2023
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How to predict the memory required to store a graph for higher order gradients
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2
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136
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October 11, 2023
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Gradients not being calculated as expected
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2
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109
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October 11, 2023
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How can I use torch's `VHP` routine inside of the `step` method when defining a custom optimizer?
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5
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110
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October 11, 2023
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