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• 标题：lstm输出的size不对
• 版本、环境信息：

   1）PaddlePaddle版本：1.5.1

• 模型信息

   lstm https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/api_cn/layers_cn/nn_cn.html
ernie

• 复现信息：
• 问题描述：

代码片段
def seq_from_ernie(
args,
src_ids,
position_ids,
sentence_ids,
task_ids,
input_mask,
config,
use_fp16,
):
"""cls_from_ernie"""
ernie = ErnieModel(
src_ids=src_ids,
position_ids=position_ids,
sentence_ids=sentence_ids,
task_ids=task_ids,
input_mask=input_mask,
config=config,
use_fp16=use_fp16,
)
seq_feats = ernie.get_sequence_output()
'''
seq_feats = fluid.layers.dropout(
x=seq_feats,
dropout_prob=0.1,
dropout_implementation="upscale_in_train",
)
'''
return seq_feats

if is_classify:
pyreader = fluid.layers.py_reader(
capacity=50,
shapes=[
[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1],
#[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
#[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
#[-1, args.max_seq_len, 1],
[-1, 1],
[-1, 1],
[-1, 1],
],
dtypes=[
'int64', 'int64', 'int64', 'int64', 'float32',
'int64', 'int64', 'int64', 'int64', 'float32',
#'int64', 'int64', 'int64', 'int64', 'float32',
'int64',
'int64',
'int64',
],
lod_levels=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
name=task_name + "_" + pyreader_name,
use_double_buffer=True,
)

(
src_ids_1, sent_ids_1, pos_ids_1, task_ids_1, input_mask_1,
src_ids_2, sent_ids_2, pos_ids_2, task_ids_2, input_mask_2,
#src_ids_3, sent_ids_3, pos_ids_3, task_ids_3, input_mask_3,
labels,
types,
qids,
) = fluid.layers.read_file(pyreader)

seq_feats_query = seq_from_ernie(
args,
src_ids=src_ids_1,
position_ids=pos_ids_1,
sentence_ids=sent_ids_1,
task_ids=task_ids_1,
input_mask=input_mask_1,
config=ernie_config,
use_fp16=args.use_fp16,
)

seq_feats_left = seq_from_ernie(
args,
src_ids=src_ids_2,
position_ids=pos_ids_2,
sentence_ids=sent_ids_2,
task_ids=task_ids_2,
input_mask=input_mask_2,
config=ernie_config,
use_fp16=args.use_fp16,
)
seq_feats_query = fluid.layers.transpose(seq_feats_query,[1,0,2])
seq_feats_left = fluid.layers.transpose(seq_feats_left,[1,0,2])
print('seq_feats_query',seq_feats_query)
num_layers,batch_size,hidden_size,max_len = 1,32,64,128
query_init_h = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
query_init_c = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
query_rnn_out, query_last_h, query_last_c = fluid.layers.lstm(
seq_feats_query,
query_init_h,query_init_c,max_len, hidden_size, num_layers,
is_bidirec=True
)
print('query_rnn_out',query_rnn_out)
print('seq_feats_left',seq_feats_left)
left_init_h = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
left_init_c = fluid.layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
left_rnn_out, left_last_h, left_last_c = fluid.layers.lstm(
seq_feats_left,
left_init_h,left_init_c,max_len, hidden_size, num_layers,
is_bidirec=True
)
#query_rnn_out = fluid.layers.transpose(query_rnn_out,[1,0,2])
#left_rnn_out = fluid.layers.transpose(left_rnn_out,[1,0,2])
print('left_rnn_out',left_rnn_out)
说下整个过程，先用ernie get_sequence_output 获得整个序列每个位置的向量(768维），然后过lstm，输出按理来说的维度应该是预设的hidden_size2,然而事实不是这样的。
输入数据 第三个分量是原始的dimension 768
seq_feats_query name: "transpose_96.tmp_0"
2020-09-02 13:42:55 type {
2020-09-02 13:42:55 type: LOD_TENSOR
2020-09-02 13:42:55 lod_tensor {
2020-09-02 13:42:55 tensor {
2020-09-02 13:42:55 data_type: FP32
2020-09-02 13:42:55 dims: 128
2020-09-02 13:42:55 dims: -1
2020-09-02 13:42:55 dims: 768
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 persistable: false
2020-09-02 13:42:55
经过lstm 输出数据，第三个分量不是我预期的hidden_size2 =128而仍然是768
2020-09-02 13:42:55 query_rnn_out name: "cudnn_lstm_0.tmp_0"
2020-09-02 13:42:55 type {
2020-09-02 13:42:55 type: LOD_TENSOR
2020-09-02 13:42:55 lod_tensor {
2020-09-02 13:42:55 tensor {
2020-09-02 13:42:55 data_type: FP32
2020-09-02 13:42:55 dims: 128
2020-09-02 13:42:55 dims: -1
2020-09-02 13:42:55 dims: 768
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 persistable: false
这个也是同理，输入768维，输出hidden_size 仍未768.与文档中提到的并不符合，

2020-09-02 13:42:55 seq_feats_left name: "transpose_97.tmp_0"
2020-09-02 13:42:55 type {
2020-09-02 13:42:55 type: LOD_TENSOR
2020-09-02 13:42:55 lod_tensor {
2020-09-02 13:42:55 tensor {
2020-09-02 13:42:55 data_type: FP32
2020-09-02 13:42:55 dims: 128
2020-09-02 13:42:55 dims: -1
2020-09-02 13:42:55 dims: 768
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 persistable: false
2020-09-02 13:42:55
2020-09-02 13:42:55 left_rnn_out name: "cudnn_lstm_1.tmp_0"
2020-09-02 13:42:55 type {
2020-09-02 13:42:55 type: LOD_TENSOR
2020-09-02 13:42:55 lod_tensor {
2020-09-02 13:42:55 tensor {
2020-09-02 13:42:55 data_type: FP32
2020-09-02 13:42:55 dims: 128
2020-09-02 13:42:55 dims: -1
2020-09-02 13:42:55 dims: 768
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 }
2020-09-02 13:42:55 persistable: false