diff --git a/core/predictor/framework/bsf.h b/core/predictor/framework/bsf.h old mode 100755 new mode 100644 index c32745fbe464c1b0b4271c286c5a23e65b1499b0..8d135180edb72a4c118c34281d08ca047f3e1528 --- a/core/predictor/framework/bsf.h +++ b/core/predictor/framework/bsf.h @@ -332,6 +332,9 @@ struct Task { // feature_vector[0]是由shape0_index的范围值组成的vector,包含两个元素最小和最大值。 // feature_vector[1]是由lod组成的vector，包含指定batch的lod信息. // feature_vector[2]是由单个元素的组成的vector，元素值为1表示是nobatch的feedvar。 + // feature_vector[3]是2维lod组成的vector,包含指定batch的2-level lod。 + // 之所以把二维lod + // 加入到feature_vector[3]，是为了兼容原有代码，尽可能小的改动。 // if 为 nobatch feedvar情况。 // else if 为带lod的feedvar情况。 @@ -343,6 +346,7 @@ struct Task { } else if (set_feed_lod_index.size() > 0 && set_feed_lod_index.find(feedvar_index) != set_feed_lod_index.end()) { + int lod_size = (*inVectorT_ptr)[feedvar_index].lod.size(); std::vector feed_lod_vector(end_batch - start_batch); for (size_t lod_index = start_batch + 1, vector_index = 0; lod_index < end_batch + 1; @@ -351,9 +355,35 @@ struct Task { (*inVectorT_ptr)[feedvar_index].lod[0][lod_index] - (*inVectorT_ptr)[feedvar_index].lod[0][start_batch]; } - size_t shape0_start = (*inVectorT_ptr)[feedvar_index].lod[0][start_batch]; - size_t shape0_end = (*inVectorT_ptr)[feedvar_index].lod[0][end_batch]; - feature_vector = {{shape0_start, shape0_end}, feed_lod_vector}; + if (lod_size == 1) { + size_t shape0_start = + (*inVectorT_ptr)[feedvar_index].lod[0][start_batch]; + size_t shape0_end = (*inVectorT_ptr)[feedvar_index].lod[0][end_batch]; + feature_vector = {{shape0_start, shape0_end}, feed_lod_vector}; + } else if (lod_size == 2) { + size_t 2level_lod_start_index = + (*inVectorT_ptr)[feedvar_index].lod[0][start_batch]; + size_t 2level_lod_end_index = + (*inVectorT_ptr)[feedvar_index].lod[0][end_batch]; + int 2level_lod_size = 2level_lod_end_index - 2level_lod_start_index; + std::vector feed_2level_lod_vector(2level_lod_size); + for (size_t 2lod_index = 2level_lod_start_index + 1, vector_index = 0; + 2lod_index < 2level_lod_end_index + 1; + ++vector_index, ++2lod_index) { + feed_2level_lod_vector[vector_index] = + (*inVectorT_ptr)[feedvar_index].lod[1][2lod_index] - + (*inVectorT_ptr)[feedvar_index].lod[1][2level_lod_start_index]; + } + size_t shape0_start = + (*inVectorT_ptr)[feedvar_index].lod[1][2level_lod_start_index]; + size_t shape0_end = + (*inVectorT_ptr)[feedvar_index].lod[1][2level_lod_end_index]; + feature_vector = {{shape0_start, shape0_end}, + feed_lod_vector, + {}, + feed_2level_lod_vector}; + } + // feature_vector.push_back(feed_lod_vector); } else { feature_vector = {{start_batch, end_batch}}; @@ -368,15 +398,23 @@ struct Task { for (size_t index = 0; index < vector_fetch_lod_index.size(); ++index) { size_t data_length = 0; size_t lod_length = 0; + size_t 2lod_length = 0; size_t total_shape0 = 0; + int lod_size = 1; size_t feedvar_index = vector_fetch_lod_index[index]; // 由于PaddleTensor的resize实现，是每次都会清空，所以必须先统计总长度。 for (size_t taskmeta_index = 0; taskmeta_index < total_taskmeta_num; ++taskmeta_index) { + lod_size = outLodTensorVector[taskmeta_index][index].lod.size(); data_length += outLodTensorVector[taskmeta_index][index].data.length(); lod_length += outLodTensorVector[taskmeta_index][index].lod[0].size(); total_shape0 += outLodTensorVector[taskmeta_index][index].shape[0]; + if (lod_size == 2) { + 2lod_length += + outLodTensorVector[taskmeta_index][index].lod[0] + [lod_length - 1]; + } } // 一次性扩容PaddleTensor中的data和lod paddle::PaddleTensor& fetchVarTensor = (*outVectorT_ptr)[feedvar_index]; @@ -394,12 +432,22 @@ struct Task { fetchVarTensor.lod[0].push_back(0); } fetchVarTensor.lod[0].resize(lod_length + 1, 0); + if (lod_size == 2) { + if (fetchVarTensor.lod.size() <= 1) { + fetchVarTensor.lod.push_back({0}); + } else if (fetchVarTensor.lod[1].size() <= 0) { + fetchVarTensor.lod[1].push_back(0); + } + fetchVarTensor.lod[1].resize(2lod_length + 1, 0); + } // size_t data_length_offset = 0; size_t lod_length_offset = 0; + size_t 2lod_length_offset = 0; size_t once_data_length = 0; size_t once_lod_length = 0; + size_t once_2lod_length = 0; for (size_t taskmeta_index = 0; taskmeta_index < total_taskmeta_num; ++taskmeta_index) { // process data @@ -421,6 +469,18 @@ struct Task { outLodTensorVector[taskmeta_index][index].lod[0][once_index]; lod_length_offset++; } + if (lod_size == 2) { + size_t last_2lod_value = fetchVarTensor.lod[1][2lod_length_offset]; + once_2lod_length = + outLodTensorVector[taskmeta_index][index].lod[1].size(); + for (size_t once_index = 0; once_index < once_2lod_length; + ++once_index) { + fetchVarTensor.lod[1][2lod_length_offset + 1] = + last_2lod_value + + outLodTensorVector[taskmeta_index][index].lod[1][once_index]; + 2lod_length_offset++; + } + } } } } @@ -467,10 +527,16 @@ struct TaskMeta { feedvar_type.push_back(feature.size()); if (feature.size() == 1) { feed_lod_vector.push_back({}); + feed_2level_lod_vector.push_back({}); } else if (feature.size() == 2) { feed_lod_vector.push_back(feature[1]); - } else { + feed_2level_lod_vector.push_back({}); + } else if (feature.size() == 3) { feed_lod_vector.push_back({}); + feed_2level_lod_vector.push_back({}); + } else if (feature.size() == 4) { + feed_lod_vector.push_back(feature[1]); + feed_2level_lod_vector.push_back(feature[3]); } } } @@ -482,6 +548,7 @@ struct TaskMeta { size_t taskmeta_index; std::vector> feed_shape0_range; std::vector> feed_lod_vector; + std::vector> feed_2level_lod_vector; std::vector feedvar_type; }; @@ -647,8 +714,15 @@ class BatchTasks { _total_shape0_batch_in[feedvar_index] += tm.feed_shape0_range[feedvar_index][1] - tm.feed_shape0_range[feedvar_index][0]; - } else if (tm.feedvar_type[feedvar_index] == 2) { - // lod类型的feedvar + } else if (tm.feedvar_type[feedvar_index] == 3) { + // tm.feedvar_type[feedvar_index] == 3 + // nobatch类型的feedvar. + // 此时不累加，且值应为1 + _total_shape0_batch_in[feedvar_index] = + tm.feed_shape0_range[feedvar_index][1] - + tm.feed_shape0_range[feedvar_index][0]; + } else { + // lod类型的feedvar 可能是1维lod 也可能是2维lod // 累计计算shape0的累加值，为后面初始化PaddleTensor做准备。 _total_shape0_batch_in[feedvar_index] += tm.feed_shape0_range[feedvar_index][1] - @@ -667,13 +741,23 @@ class BatchTasks { _batch_in_lod[feedvar_index][0].push_back( last_lod_value + tm.feed_lod_vector[feedvar_index][lod_index]); } - } else { - // tm.feedvar_type[feedvar_index] == 3 - // nobatch类型的feedvar. - // 此时不累加，且值应为1 - _total_shape0_batch_in[feedvar_index] = - tm.feed_shape0_range[feedvar_index][1] - - tm.feed_shape0_range[feedvar_index][0]; + + // 2维lod 需要额外处理2维lod信息。 + if (tm.feedvar_type[feedvar_index] == 4) { + if (_batch_in_lod[feedvar_index].size() <= 1) { + _batch_in_lod[feedvar_index].push_back({0}); + } else if (_batch_in_lod[feedvar_index][1].size() <= 0) { + _batch_in_lod[feedvar_index][1].push_back(0); + } + size_t last_lod_value = _batch_in_lod[feedvar_index][1].back(); + for (size_t lod_index = 0; + lod_index < tm.feed_2level_lod_vector[feedvar_index].size(); + ++lod_index) { + _batch_in_lod[feedvar_index][1].push_back( + last_lod_value + + tm.feed_2level_lod_vector[feedvar_index][lod_index]); + } + } } } return _rem_size; @@ -1165,6 +1249,14 @@ class BatchTasks { size_t shape0_index_end = _batch_out[fetchvar_index].lod[0][last_batch + add]; size_t shape0_length = shape0_index_end - shape0_index_start; + size_t lod_size = _batch_out[fetchvar_index].lod.size(); + if (lod_size == 2) { + shape0_index_start = + _batch_out[fetchvar_index].lod[1][shape0_index_start]; + shape0_index_end = + _batch_out[fetchvar_index].lod[1][shape0_index_end]; + shape0_length = shape0_index_end - shape0_index_start; + } // task被拆分为多个taskmeta时，不能直接拷入task->outVectorT_ptr // 此时,先拷入task->outLodTensorVector[taskmeta_index] // 当task所有的taskmeta都完成时，再按照顺序进行拷贝回task->outVectorT_ptr。 @@ -1198,6 +1290,24 @@ class BatchTasks { (_batch_out[fetchvar_index].lod[0][lod_index] - last_lod_value); } + if (lod_size == 2) { + if (fetchVarTensor.lod.size() <= 1) { + fetchVarTensor.lod.push_back({}); + } + size_t last_lod0_value = + _batch_out[fetchvar_index].lod[0][last_batch]; + size_t end_lod0_value = + _batch_out[fetchvar_index].lod[0][last_batch + add]; + size_t lod1_size = end_lod0_value - last_lod0_value; + fetchVarTensor.lod[1].resize(lod1_size, 0); + for (size_t lod_index = last_lod0_value + 1, my_index = 0; + lod_index < end_lod0_value + 1; + ++lod_index, ++my_index) { + fetchVarTensor.lod[1][my_index] = + _batch_out[fetchvar_index].lod[1][lod_index] - + _batch_out[fetchvar_index].lod[1][last_lod0_value]; + } + } } else { // task未被拆分为多个taskmeta，故只有某个线程中的taskmeta会操作task不存在多线程竞争 // 此时resize后，直接写入task->outVectorT_ptr中即可。 @@ -1239,6 +1349,27 @@ class BatchTasks { (_batch_out[fetchvar_index].lod[0][lod_index] - last_lod_value); } + + if (lod_size == 2) { + if (fetchVarTensor.lod.size() <= 1) { + fetchVarTensor.lod.push_back({}); + } else if (fetchVarTensor.lod[1].size() <= 0) { + fetchVarTensor.lod[1].push_back(0); + } + size_t last_lod0_value = + _batch_out[fetchvar_index].lod[0][last_batch]; + size_t end_lod0_value = + _batch_out[fetchvar_index].lod[0][last_batch + add]; + size_t lod1_size = end_lod0_value - last_lod0_value; + fetchVarTensor.lod[1].resize(lod1_size + 1, 0); + for (size_t lod_index = last_lod0_value + 1, my_index = 1; + lod_index < end_lod0_value + 1; + ++lod_index, ++my_index) { + fetchVarTensor.lod[1][my_index] = + _batch_out[fetchvar_index].lod[1][lod_index] - + _batch_out[fetchvar_index].lod[1][last_lod0_value]; + } + } } } else { // 普通fetchvar情况，此时该Task总的fetchvar_batch =