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体验新版 GitCode,发现更多精彩内容 >>
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2c4dbca7
编写于
11月 27, 2019
作者:
G
Guo Sheng
提交者:
GitHub
11月 27, 2019
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电子邮件补丁
差异文件
Fix the shape desc of attn_bias in Transformer. (#3994)
上级
ae6c6e64
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
76 addition
and
65 deletion
+76
-65
PaddleNLP/PaddleMT/transformer/desc.py
PaddleNLP/PaddleMT/transformer/desc.py
+67
-59
PaddleNLP/PaddleMT/transformer/inference_model.py
PaddleNLP/PaddleMT/transformer/inference_model.py
+3
-2
PaddleNLP/PaddleMT/transformer/predict.py
PaddleNLP/PaddleMT/transformer/predict.py
+3
-2
PaddleNLP/PaddleMT/transformer/train.py
PaddleNLP/PaddleMT/transformer/train.py
+3
-2
未找到文件。
PaddleNLP/PaddleMT/transformer/desc.py
浏览文件 @
2c4dbca7
...
@@ -12,65 +12,73 @@
...
@@ -12,65 +12,73 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
# The placeholder for batch_size in compile time. Must be -1 currently to be
def
get_input_descs
(
args
):
# consistent with some ops' infer-shape output in compile time, such as the
"""
# sequence_expand op used in beamsearch decoder.
Generate a dict mapping data fields to the corresponding data shapes and
batch_size
=
None
data types.
# The placeholder for squence length in compile time.
"""
seq_len
=
None
# The placeholder for batch_size in compile time. Must be -1 currently to be
# The placeholder for head number in compile time.
# consistent with some ops' infer-shape output in compile time, such as the
n_head
=
8
# sequence_expand op used in beamsearch decoder.
# The placeholder for model dim in compile time.
batch_size
=
None
d_model
=
512
# The placeholder for squence length in compile time.
# Here list the data shapes and data types of all inputs.
seq_len
=
None
# The shapes here act as placeholder and are set to pass the infer-shape in
# The head number.
# compile time.
n_head
=
getattr
(
args
,
"n_head"
,
8
)
input_descs
=
{
# The model dim.
# The actual data shape of src_word is:
d_model
=
getattr
(
args
,
"d_model"
,
512
)
# [batch_size, max_src_len_in_batch]
"src_word"
:
[(
batch_size
,
seq_len
),
"int64"
,
2
],
# Here list the data shapes and data types of all inputs.
# The actual data shape of src_pos is:
# The shapes here act as placeholder and are set to pass the infer-shape in
# [batch_size, max_src_len_in_batch, 1]
# compile time.
"src_pos"
:
[(
batch_size
,
seq_len
),
"int64"
],
input_descs
=
{
# This input is used to remove attention weights on paddings in the
# The actual data shape of src_word is:
# encoder.
# [batch_size, max_src_len_in_batch]
# The actual data shape of src_slf_attn_bias is:
"src_word"
:
[(
batch_size
,
seq_len
),
"int64"
,
2
],
# [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
# The actual data shape of src_pos is:
"src_slf_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
# [batch_size, max_src_len_in_batch, 1]
# The actual data shape of trg_word is:
"src_pos"
:
[(
batch_size
,
seq_len
),
"int64"
],
# [batch_size, max_trg_len_in_batch, 1]
# This input is used to remove attention weights on paddings in the
"trg_word"
:
[(
batch_size
,
seq_len
),
"int64"
,
# encoder.
2
],
# lod_level is only used in fast decoder.
# The actual data shape of src_slf_attn_bias is:
# The actual data shape of trg_pos is:
# [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch]
# [batch_size, max_trg_len_in_batch, 1]
"src_slf_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
"trg_pos"
:
[(
batch_size
,
seq_len
),
"int64"
],
# The actual data shape of trg_word is:
# This input is used to remove attention weights on paddings and
# [batch_size, max_trg_len_in_batch, 1]
# subsequent words in the decoder.
"trg_word"
:
[(
batch_size
,
seq_len
),
"int64"
,
# The actual data shape of trg_slf_attn_bias is:
2
],
# lod_level is only used in fast decoder.
# [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
# The actual data shape of trg_pos is:
"trg_slf_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
# [batch_size, max_trg_len_in_batch, 1]
# This input is used to remove attention weights on paddings of the source
"trg_pos"
:
[(
batch_size
,
seq_len
),
"int64"
],
# input in the encoder-decoder attention.
# This input is used to remove attention weights on paddings and
# The actual data shape of trg_src_attn_bias is:
# subsequent words in the decoder.
# [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
# The actual data shape of trg_slf_attn_bias is:
"trg_src_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
# [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch]
# This input is used in independent decoder program for inference.
"trg_slf_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
# The actual data shape of enc_output is:
# This input is used to remove attention weights on paddings of the source
# [batch_size, max_src_len_in_batch, d_model]
# input in the encoder-decoder attention.
"enc_output"
:
[(
batch_size
,
seq_len
,
d_model
),
"float32"
],
# The actual data shape of trg_src_attn_bias is:
# The actual data shape of label_word is:
# [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch]
# [batch_size * max_trg_len_in_batch, 1]
"trg_src_attn_bias"
:
[(
batch_size
,
n_head
,
seq_len
,
seq_len
),
"float32"
],
"lbl_word"
:
[(
None
,
1
),
"int64"
],
# This input is used in independent decoder program for inference.
# This input is used to mask out the loss of paddding tokens.
# The actual data shape of enc_output is:
# The actual data shape of label_weight is:
# [batch_size, max_src_len_in_batch, d_model]
# [batch_size * max_trg_len_in_batch, 1]
"enc_output"
:
[(
batch_size
,
seq_len
,
d_model
),
"float32"
],
"lbl_weight"
:
[(
None
,
1
),
"float32"
],
# The actual data shape of label_word is:
# This input is used in beam-search decoder.
# [batch_size * max_trg_len_in_batch, 1]
"init_score"
:
[(
batch_size
,
1
),
"float32"
,
2
],
"lbl_word"
:
[(
None
,
1
),
"int64"
],
# This input is used in beam-search decoder for the first gather
# This input is used to mask out the loss of paddding tokens.
# (cell states updation)
# The actual data shape of label_weight is:
"init_idx"
:
[(
batch_size
,
),
"int32"
],
# [batch_size * max_trg_len_in_batch, 1]
}
"lbl_weight"
:
[(
None
,
1
),
"float32"
],
# This input is used in beam-search decoder.
"init_score"
:
[(
batch_size
,
1
),
"float32"
,
2
],
# This input is used in beam-search decoder for the first gather
# (cell states updation)
"init_idx"
:
[(
batch_size
,
),
"int32"
],
}
return
input_descs
# Names of word embedding table which might be reused for weight sharing.
# Names of word embedding table which might be reused for weight sharing.
word_emb_param_names
=
(
word_emb_param_names
=
(
...
...
PaddleNLP/PaddleMT/transformer/inference_model.py
浏览文件 @
2c4dbca7
...
@@ -93,10 +93,11 @@ def do_save_inference_model(args):
...
@@ -93,10 +93,11 @@ def do_save_inference_model(args):
# define input and reader
# define input and reader
input_field_names
=
desc
.
encoder_data_input_fields
+
desc
.
fast_decoder_data_input_fields
input_field_names
=
desc
.
encoder_data_input_fields
+
desc
.
fast_decoder_data_input_fields
input_descs
=
desc
.
get_input_descs
(
args
.
args
)
input_slots
=
[{
input_slots
=
[{
"name"
:
name
,
"name"
:
name
,
"shape"
:
desc
.
input_descs
[
name
][
0
],
"shape"
:
input_descs
[
name
][
0
],
"dtype"
:
desc
.
input_descs
[
name
][
1
]
"dtype"
:
input_descs
[
name
][
1
]
}
for
name
in
input_field_names
]
}
for
name
in
input_field_names
]
input_field
=
InputField
(
input_slots
)
input_field
=
InputField
(
input_slots
)
...
...
PaddleNLP/PaddleMT/transformer/predict.py
浏览文件 @
2c4dbca7
...
@@ -134,10 +134,11 @@ def do_predict(args):
...
@@ -134,10 +134,11 @@ def do_predict(args):
# define input and reader
# define input and reader
input_field_names
=
desc
.
encoder_data_input_fields
+
desc
.
fast_decoder_data_input_fields
input_field_names
=
desc
.
encoder_data_input_fields
+
desc
.
fast_decoder_data_input_fields
input_descs
=
desc
.
get_input_descs
(
args
.
args
)
input_slots
=
[{
input_slots
=
[{
"name"
:
name
,
"name"
:
name
,
"shape"
:
desc
.
input_descs
[
name
][
0
],
"shape"
:
input_descs
[
name
][
0
],
"dtype"
:
desc
.
input_descs
[
name
][
1
]
"dtype"
:
input_descs
[
name
][
1
]
}
for
name
in
input_field_names
]
}
for
name
in
input_field_names
]
input_field
=
InputField
(
input_slots
)
input_field
=
InputField
(
input_slots
)
...
...
PaddleNLP/PaddleMT/transformer/train.py
浏览文件 @
2c4dbca7
...
@@ -175,10 +175,11 @@ def do_train(args):
...
@@ -175,10 +175,11 @@ def do_train(args):
input_field_names
=
desc
.
encoder_data_input_fields
+
\
input_field_names
=
desc
.
encoder_data_input_fields
+
\
desc
.
decoder_data_input_fields
[:
-
1
]
+
desc
.
label_data_input_fields
desc
.
decoder_data_input_fields
[:
-
1
]
+
desc
.
label_data_input_fields
input_descs
=
desc
.
get_input_descs
(
args
.
args
)
input_slots
=
[{
input_slots
=
[{
"name"
:
name
,
"name"
:
name
,
"shape"
:
desc
.
input_descs
[
name
][
0
],
"shape"
:
input_descs
[
name
][
0
],
"dtype"
:
desc
.
input_descs
[
name
][
1
]
"dtype"
:
input_descs
[
name
][
1
]
}
for
name
in
input_field_names
]
}
for
name
in
input_field_names
]
input_field
=
InputField
(
input_slots
)
input_field
=
InputField
(
input_slots
)
...
...
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