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Commite02eaff

Browse files
authored
fix for np.float32 serialization (#589)
1 parentdb9523c commite02eaff

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4 files changed

+150
-82
lines changed

4 files changed

+150
-82
lines changed

‎pgml-extension/examples/finetune.sql

Lines changed: 90 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,90 @@
1+
-- Exit on error (psql)
2+
\set ON_ERROR_STOP true
3+
\timingon
4+
5+
6+
SELECTpgml.load_dataset('kde4', kwargs=>'{"lang1": "en", "lang2": "es"}');
7+
CREATE OR REPLACEVIEWkde4_en_to_esAS
8+
SELECT translation->>'en'AS"en", translation->>'es'AS"es"
9+
FROMpgml.kde4
10+
LIMIT10;
11+
SELECTpgml.tune(
12+
'Translate English to Spanish',
13+
task=>'translation',
14+
relation_name=>'kde4_en_to_es',
15+
y_column_name=>'es',-- translate into spanish
16+
model_name=>'Helsinki-NLP/opus-mt-en-es',
17+
hyperparams=>'{
18+
"learning_rate": 2e-5,
19+
"per_device_train_batch_size": 16,
20+
"per_device_eval_batch_size": 16,
21+
"num_train_epochs": 1,
22+
"weight_decay": 0.01,
23+
"max_length": 128
24+
}',
25+
test_size=>0.5,
26+
test_sampling=>'last'
27+
);
28+
29+
SELECTpgml.load_dataset('imdb');
30+
SELECTpgml.tune(
31+
'IMDB Review Sentiment',
32+
task=>'text-classification',
33+
relation_name=>'pgml.imdb',
34+
y_column_name=>'label',
35+
model_name=>'distilbert-base-uncased',
36+
hyperparams=>'{
37+
"learning_rate": 2e-5,
38+
"per_device_train_batch_size": 16,
39+
"per_device_eval_batch_size": 16,
40+
"num_train_epochs": 1,
41+
"weight_decay": 0.01
42+
}',
43+
test_size=>0.5,
44+
test_sampling=>'last'
45+
);
46+
SELECTpgml.predict('IMDB Review Sentiment','I love SQL');
47+
48+
SELECTpgml.load_dataset('squad_v2');
49+
SELECTpgml.tune(
50+
'SQuAD Q&A v2',
51+
'question-answering',
52+
'pgml.squad_v2',
53+
'answers',
54+
'deepset/roberta-base-squad2',
55+
hyperparams=>'{
56+
"evaluation_strategy": "epoch",
57+
"learning_rate": 2e-5,
58+
"per_device_train_batch_size": 16,
59+
"per_device_eval_batch_size": 16,
60+
"num_train_epochs": 1,
61+
"weight_decay": 0.01,
62+
"max_length": 384,
63+
"stride": 128
64+
}',
65+
test_size=>11873,
66+
test_sampling=>'last'
67+
);
68+
69+
70+
SELECTpgml.load_dataset('billsum', kwargs=>'{"split": "ca_test"}');
71+
CREATE OR REPLACEVIEWbillsum_training_data
72+
ASSELECT title||'\n'||textAStext, summaryFROMpgml.billsum;
73+
SELECTpgml.tune(
74+
'Legal Summarization',
75+
task=>'summarization',
76+
relation_name=>'billsum_training_data',
77+
y_column_name=>'summary',
78+
model_name=>'sshleifer/distilbart-xsum-12-1',
79+
hyperparams=>'{
80+
"learning_rate": 2e-5,
81+
"per_device_train_batch_size": 2,
82+
"per_device_eval_batch_size": 2,
83+
"num_train_epochs": 1,
84+
"weight_decay": 0.01,
85+
"max_input_length": 1024,
86+
"max_summary_length": 128
87+
}',
88+
test_size=>0.01,
89+
test_sampling=>'last'
90+
);

‎pgml-extension/examples/transformers.sql

Lines changed: 50 additions & 79 deletions
Original file line numberDiff line numberDiff line change
@@ -32,89 +32,60 @@ SELECT pgml.transform(
3232
'Dominic Cobb is the foremost practitioner of the artistic science of extraction, inserting oneself into a subject''s dreams to obtain hidden information without the subject knowing, a concept taught to him by his professor father-in-law, Dr. Stephen Miles. Dom''s associates are Miles'' former students, who Dom requires as he has given up being the dream architect for reasons he won''t disclose. Dom''s primary associate, Arthur, believes it has something to do with Dom''s deceased wife, Mal, who often figures prominently and violently in those dreams, or Dom''s want to "go home" (get back to his own reality, which includes two young children). Dom''s work is generally in corporate espionage. As the subjects don''t want the information to get into the wrong hands, the clients have zero tolerance for failure. Dom is also a wanted man, as many of his past subjects have learned what Dom has done to them. One of those subjects, Mr. Saito, offers Dom a job he can''t refuse: to take the concept one step further into inception, namely planting thoughts into the subject''s dreams without them knowing. Inception can fundamentally alter that person as a being. Saito''s target is Robert Michael Fischer, the heir to an energy business empire, which has the potential to rule the world if continued on the current trajectory. Beyond the complex logistics of the dream architecture of the case and some unknowns concerning Fischer, the biggest obstacles in success for the team become worrying about one aspect of inception which Cobb fails to disclose to the other team members prior to the job, and Cobb''s newest associate Ariadne''s belief that Cobb''s own subconscious, especially as it relates to Mal, may be taking over what happens in the dreams.'
3333
]
3434
);
35+
SELECTpgml.transform(
36+
inputs=> ARRAY[
37+
'I love how amazingly simple ML has become!',
38+
'I hate doing mundane and thankless tasks. ☹️'
39+
],
40+
task=>'{"task": "text-classification",
41+
"model": "finiteautomata/bertweet-base-sentiment-analysis"
42+
}'::JSONB
43+
)AS positivity;
3544

36-
SELECTpgml.load_dataset('kde4', kwargs=>'{"lang1": "en", "lang2": "es"}');
37-
CREATE OR REPLACEVIEWkde4_en_to_esAS
38-
SELECT translation->>'en'AS"en", translation->>'es'AS"es"
39-
FROMpgml.kde4
40-
LIMIT10;
41-
SELECTpgml.tune(
42-
'Translate English to Spanish',
43-
task=>'translation',
44-
relation_name=>'kde4_en_to_es',
45-
y_column_name=>'es',-- translate into spanish
46-
model_name=>'Helsinki-NLP/opus-mt-en-es',
47-
hyperparams=>'{
48-
"learning_rate": 2e-5,
49-
"per_device_train_batch_size": 16,
50-
"per_device_eval_batch_size": 16,
51-
"num_train_epochs": 1,
52-
"weight_decay": 0.01,
53-
"max_length": 128
54-
}',
55-
test_size=>0.5,
56-
test_sampling=>'last'
57-
);
45+
SELECTpgml.transform(
46+
task=>'text-classification',
47+
inputs=> ARRAY[
48+
'I love how amazingly simple ML has become!',
49+
'I hate doing mundane and thankless tasks. ☹️'
50+
]
51+
)AS positivity;
52+
53+
SELECTpgml.transform(
54+
inputs=> ARRAY[
55+
'Stocks rallied and the British pound gained.',
56+
'Stocks making the biggest moves midday: Nvidia, Palantir and more'
57+
],
58+
task=>'{"task": "text-classification",
59+
"model": "ProsusAI/finbert"
60+
}'::JSONB
61+
)AS market_sentiment;
5862

59-
SELECTpgml.load_dataset('imdb');
60-
SELECTpgml.tune(
61-
'IMDB Review Sentiment',
62-
task=>'text-classification',
63-
relation_name=>'pgml.imdb',
64-
y_column_name=>'label',
65-
model_name=>'distilbert-base-uncased',
66-
hyperparams=>'{
67-
"learning_rate": 2e-5,
68-
"per_device_train_batch_size": 16,
69-
"per_device_eval_batch_size": 16,
70-
"num_train_epochs": 1,
71-
"weight_decay": 0.01
72-
}',
73-
test_size=>0.5,
74-
test_sampling=>'last'
63+
SELECTpgml.transform(
64+
inputs=> ARRAY[
65+
'I have a problem with my iphone that needs to be resolved asap!!'
66+
],
67+
task=>'{"task": "zero-shot-classification",
68+
"model": "roberta-large-mnli"
69+
}'::JSONB,
70+
args=>'{"candidate_labels": ["urgent", "not urgent", "phone", "tablet", "computer"]
71+
}'::JSONB
72+
)AS zero_shot;
73+
74+
SELECTpgml.transform(
75+
inputs=> ARRAY[
76+
'Hugging Face is a French company based in New York City.'
77+
],
78+
task=>'token-classification'
7579
);
76-
SELECTpgml.predict('IMDB Review Sentiment','I love SQL');
7780

78-
SELECTpgml.load_dataset('squad_v2');
79-
SELECTpgml.tune(
80-
'SQuAD Q&A v2',
81+
SELECTpgml.transform(
8182
'question-answering',
82-
'pgml.squad_v2',
83-
'answers',
84-
'deepset/roberta-base-squad2',
85-
hyperparams=>'{
86-
"evaluation_strategy": "epoch",
87-
"learning_rate": 2e-5,
88-
"per_device_train_batch_size": 16,
89-
"per_device_eval_batch_size": 16,
90-
"num_train_epochs": 1,
91-
"weight_decay": 0.01,
92-
"max_length": 384,
93-
"stride": 128
94-
}',
95-
test_size=>11873,
96-
test_sampling=>'last'
97-
);
83+
inputs=> ARRAY[
84+
'{
85+
"question": "Am I dreaming?",
86+
"context": "I got a good nights sleep last night and started a simple tutorial over my cup of morning coffee. The capabilities seem unreal, compared to what I came to expect from the simple SQL standard I studied so long ago. The answer is staring me in the face, and I feel the uncanny call from beyond the screen to check the results."
87+
}'
88+
]
89+
)AS answer;
9890

9991

100-
SELECTpgml.load_dataset('billsum', kwargs=>'{"split": "ca_test"}');
101-
CREATE OR REPLACEVIEWbillsum_training_data
102-
ASSELECT title||'\n'||textAStext, summaryFROMpgml.billsum;
103-
SELECTpgml.tune(
104-
'Legal Summarization',
105-
task=>'summarization',
106-
relation_name=>'billsum_training_data',
107-
y_column_name=>'summary',
108-
model_name=>'sshleifer/distilbart-xsum-12-1',
109-
hyperparams=>'{
110-
"learning_rate": 2e-5,
111-
"per_device_train_batch_size": 2,
112-
"per_device_eval_batch_size": 2,
113-
"num_train_epochs": 1,
114-
"weight_decay": 0.01,
115-
"max_input_length": 1024,
116-
"max_summary_length": 128
117-
}',
118-
test_size=>0.01,
119-
test_sampling=>'last'
120-
);

‎pgml-extension/src/bindings/transformers.py

Lines changed: 9 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33
importmath
44
importshutil
55
importtime
6-
6+
importnumpyasnp
77

88
importdatasets
99
fromrougeimportRouge
@@ -40,6 +40,12 @@
4040
__cache_transformer_by_model_id= {}
4141
__cache_sentence_transformer_by_name= {}
4242

43+
classNumpyJSONEncoder(json.JSONEncoder):
44+
defdefault(self,obj):
45+
ifisinstance(obj,np.float32):
46+
returnfloat(obj)
47+
returnsuper().default(obj)
48+
4349
deftransform(task,args,inputs):
4450
task=json.loads(task)
4551
args=json.loads(args)
@@ -50,7 +56,7 @@ def transform(task, args, inputs):
5056
ifpipe.task=="question-answering":
5157
inputs= [json.loads(input)forinputininputs]
5258

53-
returnjson.dumps(pipe(inputs,**args))
59+
returnjson.dumps(pipe(inputs,**args),cls=NumpyJSONEncoder)
5460

5561
defembed(transformer,text,kwargs):
5662
kwargs=json.loads(kwargs)
@@ -101,7 +107,7 @@ def tokenize_summarization(tokenizer, max_length, x, y):
101107
returndatasets.Dataset.from_dict(encoding.data)
102108

103109
deftokenize_text_generation(tokenizer,max_length,y):
104-
encoding=tokenizer(y,max_length=max_length)
110+
encoding=tokenizer(y,max_length=max_length,truncation=True,padding="max_length")
105111
returndatasets.Dataset.from_dict(encoding.data)
106112

107113
deftokenize_question_answering(tokenizer,max_length,x,y):

‎pgml-extension/tests/test.sql

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -27,3 +27,4 @@ SELECT pgml.load_dataset('wine');
2727
\i examples/multi_classification.sql
2828
\i examples/regression.sql
2929
\i examples/vectors.sql
30+

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