Page Menu
Home
c4science
Search
Configure Global Search
Log In
Files
F60162387
fsmt-make-super-tiny-model.py
No One
Temporary
Actions
Download File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Subscribers
None
File Metadata
Details
File Info
Storage
Attached
Created
Sat, Apr 27, 23:43
Size
3 KB
Mime Type
text/x-python
Expires
Mon, Apr 29, 23:43 (2 d)
Engine
blob
Format
Raw Data
Handle
17310874
Attached To
R11484 ADDI
fsmt-make-super-tiny-model.py
View Options
#!/usr/bin/env python
# coding: utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from
pathlib
import
Path
import
json
import
tempfile
from
transformers
import
FSMTTokenizer
,
FSMTConfig
,
FSMTForConditionalGeneration
from
transformers.models.fsmt.tokenization_fsmt
import
VOCAB_FILES_NAMES
mname_tiny
=
"tiny-wmt19-en-ru"
# Build
# borrowed from a test
vocab
=
[
"l"
,
"o"
,
"w"
,
"e"
,
"r"
,
"s"
,
"t"
,
"i"
,
"d"
,
"n"
,
"w</w>"
,
"r</w>"
,
"t</w>"
,
"lo"
,
"low"
,
"er</w>"
,
"low</w>"
,
"lowest</w>"
,
"newer</w>"
,
"wider</w>"
,
"<unk>"
,
]
vocab_tokens
=
dict
(
zip
(
vocab
,
range
(
len
(
vocab
))))
merges
=
[
"l o 123"
,
"lo w 1456"
,
"e r</w> 1789"
,
""
]
with
tempfile
.
TemporaryDirectory
()
as
tmpdirname
:
build_dir
=
Path
(
tmpdirname
)
src_vocab_file
=
build_dir
/
VOCAB_FILES_NAMES
[
"src_vocab_file"
]
tgt_vocab_file
=
build_dir
/
VOCAB_FILES_NAMES
[
"tgt_vocab_file"
]
merges_file
=
build_dir
/
VOCAB_FILES_NAMES
[
"merges_file"
]
with
open
(
src_vocab_file
,
"w"
)
as
fp
:
fp
.
write
(
json
.
dumps
(
vocab_tokens
))
with
open
(
tgt_vocab_file
,
"w"
)
as
fp
:
fp
.
write
(
json
.
dumps
(
vocab_tokens
))
with
open
(
merges_file
,
"w"
)
as
fp
:
fp
.
write
(
"
\n
"
.
join
(
merges
))
tokenizer
=
FSMTTokenizer
(
langs
=
[
"en"
,
"ru"
],
src_vocab_size
=
len
(
vocab
),
tgt_vocab_size
=
len
(
vocab
),
src_vocab_file
=
src_vocab_file
,
tgt_vocab_file
=
tgt_vocab_file
,
merges_file
=
merges_file
,
)
config
=
FSMTConfig
(
langs
=
[
'ru'
,
'en'
],
src_vocab_size
=
1000
,
tgt_vocab_size
=
1000
,
d_model
=
4
,
encoder_layers
=
1
,
decoder_layers
=
1
,
encoder_ffn_dim
=
4
,
decoder_ffn_dim
=
4
,
encoder_attention_heads
=
1
,
decoder_attention_heads
=
1
,
)
tiny_model
=
FSMTForConditionalGeneration
(
config
)
print
(
f
"num of params {tiny_model.num_parameters()}"
)
# Test
batch
=
tokenizer
([
"Making tiny model"
],
return_tensors
=
"pt"
)
outputs
=
tiny_model
(
**
batch
)
print
(
"test output:"
,
len
(
outputs
.
logits
[
0
]))
# Save
tiny_model
.
half
()
# makes it smaller
tiny_model
.
save_pretrained
(
mname_tiny
)
tokenizer
.
save_pretrained
(
mname_tiny
)
print
(
f
"Generated {mname_tiny}"
)
# Upload
# transformers-cli upload tiny-wmt19-en-ru
Event Timeline
Log In to Comment