MKL-DNN integration does not support backward pass · facebookarchive/caffe2#2376

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help wanted

説明

MKL-DNN integration (in caffe2/mkl) implements only forward pass operators, although MKL-DNN itself provides primitives for both forward and backward pass. Implementation of ConvGradient operator would let Caffe2 use MKL-DNN for training on CPU, and support some models which need backprop (e.g. deep dream).

コントリビューターガイド

技術スタック: cpppythonshell
領域: machine learningai
Issue 種別: feature
難度: 4
推定時間: 3-5 days
活動状況: stale
明確さ: clear
前提条件: Knowledge of MKL DNN primitiveC++ development experienceFamiliarity with Caffe2 operator registration
初心者向け度: 15
調査方針: Review existing forward pass implementation in caffe2/mkl directory to understand operator structure. Study MKL DNN documentation for backward pass primitives, particularly for convolution. Examine Caffe2's gradient operator patterns elsewhere (e.g., CPU or CUDA operators) to define ConvGradient. Propose a plan with specific files to modify and tests to add.