[Good First Issue]: Operation list to be supported

[mvafin]

Hello, everyone. I noticed an interest in PyTorch GFI. Right now we have the following list of operations that can be enabled:

If you want to enable one or more of these operations, please comment with short description of how you plan to enable it and what OpenVINO operations would you use and I will create GFI for you.

Please note that not all operations can be easily enabled.

aten::__contains__
aten::__is__
aten::__isnot__
aten::__lshift__
aten::__rshift__
aten::_autocast_to_full_precision
aten::_autocast_to_reduced_precision
aten::_fake_quantize_learnable_per_channel_affine
aten::_fake_quantize_learnable_per_tensor_affine
aten::_nested_tensor_from_mask
aten::_sparse_mm
aten::_standard_gamma
aten::_to_copy
aten::_transformer_encoder_layer_fwd
aten::affine_grid_generator
aten::angle
aten::bernoulli_
aten::binary_cross_entropy_with_logits
aten::bincount
aten::bitwise_right_shift
aten::clear
aten::constant_pad_nd
aten::cross_entropy_loss
aten::Delete
aten::dict
aten::dot
aten::embedding_renorm_
aten::equal
aten::extend
aten::fft_fft
aten::fft_ifft
aten::fft_irfft2
aten::fft_rfft
aten::fft_rfft2
aten::find
aten::format
aten::fractional_max_pool2d
aten::get
aten::grad
aten::histc
aten::hstack
aten::isin
aten::items
aten::kthvalue
aten::linalg_det
aten::linalg_multi_dot
aten::logit
aten::lstm_cell
aten::max_unpool2d
aten::mT
aten::nan_to_num
aten::pad_sequence
aten::percentFormat
aten::poisson
aten::polar
aten::pop
aten::quantized_lstm
aten::rad2deg
aten::randperm
aten::ravel
aten::replace
aten::reverse
aten::rms_norm
aten::set_
aten::smooth_l1_loss
aten::special_logit
aten::stack
aten::str
aten::take
aten::tensordot
aten::to_dense
aten::to_mkldnn
aten::to_padded_tensor
aten::uniform_
aten::unique_consecutive
aten::values
aten::vstack
prim::CallFunction
prim::CallMethod
prim::ConstantMKLDNNTensor
prim::data
prim::dtype
prim::is_nested
prim::isinstance
prim::layout
prim::mkldnn_convolution
prim::tolist
prim::unchecked_cast
prim::Uninitialized
quantized::batch_norm2d
quantized::conv1d
quantized::conv1d_relu
quantized::conv2d_prepack
quantized::linear_dynamic
quantized::linear_relu
quantized::relu6
torch_ipex::ipex_linear
torch_scatter::segment_mean_csr

[itsbharatj]

.take

[github-actions]

Thank you for looking into this issue! Please let us know if you have any questions or require any help.

[itsbharatj]

Hey @mvafin thank you for this list for all the operations that have to be enabled. I would be very excited to work and implement many of these components.

Just for my understanding, is there any avaibilbe resource for the formula or description of the respective items?

[itsbharatj]

Hey @mvafin thank you for this list for all the operations that have to be enabled. I would be very excited to work and implement many of these components.

Just for my understanding, is there any avaibilbe resource for the formula or description of the respective items?

Looking into the PyTorch Aten Library for more information on this. Would be great if you can post some other resources on the this, to better understand and contribute.

[darksapien23151]

hey!, @itsbharatj seems like a long list to go!
would be glad to be contributing along with you, kinda split the work for a faster progress?!

@mvafin is it possible that we could work together on it?

[itsbharatj]

@darksapien23151 Definately! Would love to

[mvafin]

@itsbharatj @darksapien23151 Please take one or two operations to work on. Do not take this issue. New issue will be created for operations you took.
I would recommend investigating the operation using pytorch documentation and code.
Please also write short description of how would you support this operation, write a formula you would use, OpenVINO operations that will be used in decomposition, etc.

[darksapien23151]

@mvafin aight, got it!