1. 14 11月, 2017 1 次提交
  2. 08 11月, 2017 1 次提交
    • Y
      Feature/rnn to array to lod tensor (#5411) · f72729d4
      Yu Yang 提交于
      * Add LoDRankTable
      
      LoD Rank Table stores the `level` of `lod` which is ordered by sequence
      length in descending order. It is useful when implement dynamic RNN and
      is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
      RNN slice output operators.
      
      * Add skeleton for array_to_lod_tensor and lod_tensor_to_array
      
      * Add VarType::LoDTensorArray
      
      * Add PyBind of LoDTensorArray
      
      * Add InferVarType
      
      * Add first unittest
      
      * Add ut
      
      * Add unittest
      
      * Add unittest
      
      * Add unittests
      
      * update
      
      * init
      
      * add infershape for lod_tensor_to_array_op
      
      * compelete array_to_lod_tensor_op
      
      * copy data
      
      * clean code
      
      * clean code
      
      * Fix unittest data
      
      * fix bugs
      
      * fix compile error
      
      * Refine TensorToArrayOp
      
      * refactor array_to_lod_tensor
      
      * Unittest
      
      * fix bugs
      
      * Fix unittest
      
      * Fix unittest
      
      * debug
      
      * Debug
      
      * Fix unittest
      
      * clean code
      
      * refactor
      
      * use ostream
      
      * update test
      
      * fix gpu build error
      
      * make gpu test pass
      f72729d4
  3. 05 11月, 2017 1 次提交
  4. 04 11月, 2017 1 次提交
    • Y
      Add LoDRankTable (#5349) · 74849158
      Yu Yang 提交于
      * Add LoDRankTable
      
      LoD Rank Table stores the `level` of `lod` which is ordered by sequence
      length in descending order. It is useful when implement dynamic RNN and
      is shared by dynamic RNN memory, dynamic RNN slice input and dynamic
      RNN slice output operators.
      
      * Add InferVarType
      74849158