"comment":"\nSum operator.\n\nThis operators sums the input tensors. All the inputs can carry the\nLoD (Level of Details) information. However, the output only shares \nthe LoD information with the first input.\n",
"comment":"\nSum operator.\n\nThis operators sums the input tensors. All the inputs can carry the\nLoD (Level of Details) information. However, the output only shares\nthe LoD information with the first input.\n",
"inputs":[
{
"name":"X",
...
...
@@ -4548,7 +4548,7 @@
"attrs":[]
},{
"type":"sequence_concat",
"comment":"\nThe sequence_concat operator concatenates multiple LoDTensors.\nIt only supports sequence (LoD Tensor with level number is 1) \nor a nested sequence (LoD tensor with level number is 2) as its input.\n- Case1:\n If the axis is other than 0(here, axis is 1 and level is 1),\n each input should have the same LoD information and the LoD \n information of the output keeps the same as the input.\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)\n LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)\n\n- Case2:\n If the axis is 0(here, leve is 0), the inputs are concatenated along \n time steps, the LoD information of the output need to re-compute.\n The LoD information of level-1 should be same.\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\n- Case3:\n If the axis is 0(here, level is 1).\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)\n\n- Case4:\n If the LoD number is 1, axis is 0, level is 0\n\n LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\nNOTE: The levels of all the inputs should be the same.\n ",
"comment":"\nThe sequence_concat operator concatenates multiple LoDTensors.\nIt only supports sequence (LoD Tensor with level number is 1)\nor a nested sequence (LoD tensor with level number is 2) as its input.\n- Case1:\n If the axis is other than 0(here, axis is 1 and level is 1),\n each input should have the same LoD information and the LoD\n information of the output keeps the same as the input.\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)\n LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)\n\n- Case2:\n If the axis is 0(here, leve is 0), the inputs are concatenated along\n time steps, the LoD information of the output need to re-compute.\n The LoD information of level-1 should be same.\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\n- Case3:\n If the axis is 0(here, level is 1).\n\n LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)\n\n- Case4:\n If the LoD number is 1, axis is 0, level is 0\n\n LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)\n LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)\n LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)\n\nNOTE: The levels of all the inputs should be the same.\n ",
