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chenpangpang
transformers
Commits
18e5bdbe
Commit
18e5bdbe
authored
Dec 24, 2019
by
patrickvonplaten
Browse files
fix repetition penalty error in modeling_utils.py
parent
81db12c3
Changes
1
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10 additions
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2 deletions
+10
-2
src/transformers/modeling_utils.py
src/transformers/modeling_utils.py
+10
-2
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src/transformers/modeling_utils.py
View file @
18e5bdbe
...
@@ -728,7 +728,11 @@ class PreTrainedModel(nn.Module):
...
@@ -728,7 +728,11 @@ class PreTrainedModel(nn.Module):
if
repetition_penalty
!=
1.0
:
if
repetition_penalty
!=
1.0
:
for
i
in
range
(
batch_size
):
for
i
in
range
(
batch_size
):
for
previous_tokens
in
set
(
input_ids
[
i
].
tolist
()):
for
previous_tokens
in
set
(
input_ids
[
i
].
tolist
()):
next_token_logits
[
i
,
previous_tokens
]
/=
repetition_penalty
# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if
next_token_logits
[
i
,
previous_tokens
]
<
0
:
next_token_logits
[
i
,
previous_tokens
]
*=
repetition_penalty
else
:
next_token_logits
[
i
,
previous_tokens
]
/=
repetition_penalty
if
do_sample
:
if
do_sample
:
# Temperature (higher temperature => more likely to sample low probability tokens)
# Temperature (higher temperature => more likely to sample low probability tokens)
...
@@ -807,7 +811,11 @@ class PreTrainedModel(nn.Module):
...
@@ -807,7 +811,11 @@ class PreTrainedModel(nn.Module):
if
repetition_penalty
!=
1.0
:
if
repetition_penalty
!=
1.0
:
for
i
in
range
(
batch_size
*
num_beams
):
for
i
in
range
(
batch_size
*
num_beams
):
for
previous_tokens
in
set
(
input_ids
[
i
].
tolist
()):
for
previous_tokens
in
set
(
input_ids
[
i
].
tolist
()):
scores
[
i
,
previous_tokens
]
/=
repetition_penalty
# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if
scores
[
i
,
previous_tokens
]
<
0
:
scores
[
i
,
previous_tokens
]
*=
repetition_penalty
else
:
scores
[
i
,
previous_tokens
]
/=
repetition_penalty
if
do_sample
:
if
do_sample
:
# Temperature (higher temperature => more likely to sample low probability tokens)
# Temperature (higher temperature => more likely to sample low probability tokens)
...
...
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