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PyTorch Ops to oneDNN Functions Mapping
Jing Xu edited this page Nov 16, 2023
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1 revision
PyTorch uses ops that are registered to corresponding Math Kernel Library (MKL) functions in oneDNN. The available implementations are defined in this YAML file native_functions.yaml in the aten library of PyTorch. By doing a search for the keyword “mkldnn”, all the mappings can be found.
This is summarized in the following table:
| PyTorch | Op oneDNN Function |
|---|---|
add.Tensor |
mkldnn_add |
add_.Tensor |
mkldnn_add_ |
add.out |
mkldnn_add_out |
copy_ |
copy_mkldnn_ |
empty.memory_format |
empty_mkldnn |
mkldnn_linear |
mkldnn_linear |
mkldnn_linear_backward_input |
mkldnn_linear_backward_input |
mkldnn_linear_backward_weights |
mkldnn_linear_backward_weights |
mkldnn_linear_backward |
mkldnn_linear_backward |
mkldnn_max_pool2d |
mkldnn_max_pool2d |
mkldnn_max_pool2d_backward |
mkldnn_max_pool2d_backward |
mkldnn_max_pool3d |
mkldnn_max_pool3d |
mkldnn_max_pool3d_backward |
mkldnn_max_pool3d_backward |
mkldnn_convolution |
mkldnn_convolution |
mkldnn_rnn_layer |
mkldnn_rnn_layer |
mkldnn_rnn_layer_backward |
mkldnn_rnn_layer_backward |
mul.Tensor |
mkldnn_mul |
mul_.Tensor |
mkldnn_mul_ |
mul.out |
mkldnn_mul_out |
native_batch_norm |
mkldnn_batch_norm |
_native_batch_norm_legit |
_mkldnn_batch_norm_legit |
_native_batch_norm_legit.no_stats |
_mkldnn_batch_norm_legit_no_stats |
native_batch_norm_backward |
mkldnn_batch_norm_backward |
_mkldnn_reshape |
mkldnn_reshape |
relu |
mkldnn_relu |
relu_ |
mkldnn_relu_ |
_prelu_kernel |
mkldnn_prelu_backward |
gelu |
mkldnn_gelu |
gelu_backward |
mkldnn_gelu_backward |
sigmoid |
mkldnn_sigmoid |
sigmoid_ |
mkldnn_sigmoid_ |
_softmax |
mkldnn_softmax |
tanh |
mkldnn_tanh |
tanh_ |
mkldnn_tanh_ |
threshold_backward |
mkldnn_relu_backward |
_mkldnn_transpose |
mkldnn_transpose |
_mkldnn_transpose_ |
mkldnn_transpose_ |
clone |
mkldnn_clone |
zero_ |
mkldnn_zero_ |
_to_dense |
mkldnn_to_dense |
to_mkldnn |
dense_to_mkldnn |
mkldnn_reorder_conv2d_weight |
mkldnn_reorder_conv2d_weight |
mkldnn_reorder_conv3d_weight |
mkldnn_reorder_conv3d_weight |
view |
mkldnn_view |
adaptive_avg_pool2d.out |
mkldnn_adaptive_avg_pool2d_out_stub |
mkldnn_adaptive_avg_pool2d |
mkldnn_adaptive_avg_pool2d |
mkldnn_adaptive_avg_pool2d.out |
mkldnn_adaptive_avg_pool2d_out |
mkldnn_adaptive_avg_pool2d_backward |
mkldnn_adaptive_avg_pool2d_backward |
avg_pool2d.out |
mkldnn_avg_pool2d_out |
avg_pool2d |
mkldnn_avg_pool2d |
avg_pool2d_backward.grad_input |
mkldnn_avg_pool2d_backward_out |
avg_pool2d_backward |
mkldnn_avg_pool2d_backward |
avg_pool3d.out |
mkldnn_avg_pool3d_out |
avg_pool3d |
mkldnn_avg_pool3d |
avg_pool3d_backward.grad_input |
mkldnn_avg_pool3d_backward_out |
avg_pool3d_backward |
mkldnn_avg_pool3d_backward |
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