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Transformer add activate option #3158

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merged 4 commits into from
Aug 30, 2022
Merged

Transformer add activate option #3158

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cd05514
add activate option
FrostML Aug 30, 2022
e044b9a
Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleNLP i…
FrostML Aug 30, 2022
2009e48
infer, test=document_fix
FrostML Aug 30, 2022
8aefe07
test=document_fix
FrostML Aug 30, 2022
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11 changes: 9 additions & 2 deletions paddlenlp/transformers/transformer/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -657,6 +657,8 @@ class TransformerModel(nn.Layer):
The start token id and also be used as padding id. Defaults to 0.
eos_id (int, optional):
The end token id. Defaults to 1.
activation (str, optional):
The activation used in FFN. Defaults to "relu".
"""

def __init__(self,
Expand All @@ -673,7 +675,8 @@ def __init__(self,
attn_dropout=None,
act_dropout=None,
bos_id=0,
eos_id=1):
eos_id=1,
activation="relu"):
super(TransformerModel, self).__init__()
self.trg_vocab_size = trg_vocab_size
self.emb_dim = d_model
Expand Down Expand Up @@ -708,7 +711,7 @@ def __init__(self,
dropout=dropout,
attn_dropout=attn_dropout,
act_dropout=act_dropout,
activation="relu",
activation=activation,
normalize_before=True)

if weight_sharing:
Expand Down Expand Up @@ -784,6 +787,7 @@ def forward(self, src_word, trg_word):
trg_pos = paddle.cast(
trg_word != self.bos_id, dtype=src_word.dtype) * paddle.arange(
start=0, end=trg_max_len, dtype=trg_word.dtype)

with paddle.static.amp.fp16_guard():
src_emb = self.src_word_embedding(src_word)
src_pos_emb = self.src_pos_embedding(src_pos)
Expand Down Expand Up @@ -861,6 +865,8 @@ class InferTransformerModel(TransformerModel):
Specify beam search version. It should be in one
of [`v1`, `v2`]. If `v2`, need to set `alpha`(default to 0.6) for length
penalty. Default to `v1`.
activation (str, optional):
The activation used in FFN. Defaults to "relu".
kwargs:
The key word arguments can be `rel_len` and `alpha`:

Expand Down Expand Up @@ -893,6 +899,7 @@ def __init__(self,
max_out_len=256,
output_time_major=False,
beam_search_version='v1',
activation="relu",
**kwargs):
args = dict(locals())
args.pop("self")
Expand Down