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hubert
knn_ast_KD_nmt
Commits
d6d128ea
Commit
d6d128ea
authored
2 years ago
by
hubert
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marked knnmt parts
parent
4f718126
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1 changed file
fairseq/fairseq/sequence_generator.py
+20
-8
20 additions, 8 deletions
fairseq/fairseq/sequence_generator.py
with
20 additions
and
8 deletions
fairseq/fairseq/sequence_generator.py
+
20
−
8
View file @
d6d128ea
...
...
@@ -91,9 +91,10 @@ class SequenceGenerator(nn.Module):
self
.
max_len
=
max_len
or
self
.
model
.
max_decoder_positions
()
self
.
args
=
args
# print("sequence generator arguments: ", args)
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
self
.
args
and
self
.
args
.
knnmt
:
self
.
knn_dstore
=
KNN_Dstore
(
args
)
###
self
.
normalize_scores
=
normalize_scores
...
...
@@ -323,13 +324,13 @@ class SequenceGenerator(nn.Module):
original_batch_idxs
=
sample
[
"
id
"
]
else
:
original_batch_idxs
=
torch
.
arange
(
0
,
bsz
).
type_as
(
tokens
)
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
self
.
args
and
self
.
args
.
knnmt
and
self
.
args
.
save_knns
:
assert
beam_size
==
1
,
"
Saving knns for beam size > 1 is too complicated!
"
knns
=
torch
.
zeros
([
bsz
,
max_len
+
1
,
self
.
args
.
k
],
dtype
=
torch
.
int
)
vals
=
torch
.
zeros
([
bsz
,
max_len
+
1
,
self
.
args
.
k
],
dtype
=
torch
.
int
)
probs
=
torch
.
zeros
([
bsz
,
max_len
+
1
,
self
.
args
.
k
],
dtype
=
torch
.
float32
)
###
for
step
in
range
(
max_len
+
1
):
# one extra step for EOS marker
...
...
@@ -372,6 +373,7 @@ class SequenceGenerator(nn.Module):
)
probs
=
probs
[:,
-
1
,
:]
*
self
.
lm_weight
lprobs
+=
probs
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
self
.
args
and
self
.
args
.
knnmt
:
queries
=
avg_attn_scores
[
self
.
args
.
knn_keytype
]
if
len
(
avg_attn_scores
.
keys
())
>
2
:
...
...
@@ -415,7 +417,7 @@ class SequenceGenerator(nn.Module):
#print(knn_scores)
#print(knn_scores[inds])
lprobs
=
knn_scores
###
lprobs
[
lprobs
!=
lprobs
]
=
torch
.
tensor
(
-
math
.
inf
).
to
(
lprobs
)
lprobs
[:,
self
.
pad
]
=
-
math
.
inf
# never select pad
...
...
@@ -505,11 +507,12 @@ class SequenceGenerator(nn.Module):
attn
,
src_lengths
,
max_len
,
knnmt
=
self
.
args
and
self
.
args
.
knnmt
and
self
.
args
.
save_knns
,
knnmt
=
self
.
args
and
self
.
args
.
knnmt
and
self
.
args
.
save_knns
,
#### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
knns
=
knns
if
self
.
args
and
self
.
args
.
save_knns
else
None
,
knn_vals
=
vals
if
self
.
args
and
self
.
args
.
save_knns
else
None
,
knn_probs
=
probs
if
self
.
args
and
self
.
args
.
save_knns
else
None
,
)
###
num_remaining_sent
-=
len
(
finalized_sents
)
assert
num_remaining_sent
>=
0
...
...
@@ -675,6 +678,7 @@ class SequenceGenerator(nn.Module):
tensor
[
mask
]
=
tensor
[
mask
][:,
:
1
,
:]
return
tensor
.
view
(
-
1
,
tensor
.
size
(
-
1
))
# adapted to fit to knnmt following https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
def
finalize_hypos
(
self
,
step
:
int
,
...
...
@@ -722,11 +726,12 @@ class SequenceGenerator(nn.Module):
pos_scores
[:,
step
]
=
eos_scores
# convert from cumulative to per-position scores
pos_scores
[:,
1
:]
=
pos_scores
[:,
1
:]
-
pos_scores
[:,
:
-
1
]
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
knnmt
:
knns
=
knns
[
bbsz_idx
,
:
step
+
1
]
knn_vals
=
knn_vals
[
bbsz_idx
,
:
step
+
1
]
knn_probs
=
knn_probs
[
bbsz_idx
,
:
step
+
1
]
###
# normalize sentence-level scores
if
self
.
normalize_scores
:
eos_scores
/=
(
step
+
1
)
**
self
.
len_penalty
...
...
@@ -775,7 +780,7 @@ class SequenceGenerator(nn.Module):
hypo_attn
=
attn_clone
[
i
]
else
:
hypo_attn
=
torch
.
empty
(
0
)
# adapted according to https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
finalized
[
sent
].
append
(
{
"
tokens
"
:
tokens_clone
[
i
],
...
...
@@ -868,8 +873,10 @@ class EnsembleModel(nn.Module):
args
=
None
,
):
if
args
:
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
args
.
knnmt
and
len
(
self
.
models
)
>
1
:
raise
ValueError
(
"
Cannot use knnmt with actual ensembles!
"
)
###
log_probs
=
[]
avg_attn
:
Optional
[
Tensor
]
=
None
encoder_out
:
Optional
[
Dict
[
str
,
List
[
Tensor
]]]
=
None
...
...
@@ -901,8 +908,10 @@ class EnsembleModel(nn.Module):
elif
attn_holder
is
not
None
:
attn
=
attn_holder
[
0
]
if
args
:
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
args
.
knnmt
:
knn_queries
=
decoder_out
[
1
][
args
.
knn_keytype
]
###
if
attn
is
not
None
:
attn
=
attn
[:,
-
1
,
:]
...
...
@@ -916,13 +925,16 @@ class EnsembleModel(nn.Module):
probs
=
probs
[:,
-
1
,
:]
if
self
.
models_size
==
1
:
if
args
:
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
args
.
knnmt
:
return
probs
,
{
"
attn
"
:
attn
,
args
.
knn_keytype
:
knn_queries
}
###
return
probs
,
attn
elif
args
:
### taken from https://github.com/urvashik/knnmt/blob/master/fairseq/sequence_generator.py
if
args
.
knnmt
:
raise
ValueError
(
"
Cannot use with a real ensemble yet!
"
)
###
log_probs
.
append
(
probs
)
if
attn
is
not
None
:
if
avg_attn
is
None
:
...
...
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