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Commitdebd9ae

Browse files
Separate embedding kwargs into init kwargs and encode kwargs (#1555)
Co-authored-by: Montana Low <montana.low@gmail.com>
1 parentfec164a commitdebd9ae

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4 files changed

+30
-6
lines changed

4 files changed

+30
-6
lines changed

‎pgml-extension/examples/embedding.sql

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@@ -0,0 +1,7 @@
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\timingon
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SELECTpgml.embed('Alibaba-NLP/gte-base-en-v1.5','hi mom','{"trust_remote_code": true}');
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SELECTpgml.embed('Alibaba-NLP/gte-base-en-v1.5','hi mom','{"device": "cuda", "trust_remote_code": true}');
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SELECTpgml.embed('Alibaba-NLP/gte-base-en-v1.5','hi mom','{"device": "cpu", "trust_remote_code": true}');
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SELECTpgml.embed('hkunlp/instructor-xl','hi mom','{"instruction": "Encode it with love"}');
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SELECTpgml.embed('mixedbread-ai/mxbai-embed-large-v1','test','{"prompt": "test prompt: "}');

‎pgml-extension/examples/image_classification.sql

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@@ -66,7 +66,7 @@ SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'xgboost', hyperpara
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-- runtimes
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SELECT*FROMpgml.train('Handwritten Digits', algorithm=>'linear', runtime=>'python');
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SELECT*FROMpgml.train('Handwritten Digits', algorithm=>'linear', runtime=>'rust');
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--SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'linear', runtime => 'rust');
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--SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'xgboost', runtime => 'python', hyperparams => '{"n_estimators": 10}'); -- too slow
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SELECT*FROMpgml.train('Handwritten Digits', algorithm=>'xgboost', runtime=>'rust', hyperparams=>'{"n_estimators": 10}');

‎pgml-extension/src/bindings/transformers/transformers.py

Lines changed: 21 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -527,8 +527,8 @@ def rank(transformer, query, documents, kwargs):
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returnrank_using(model,query,documents,kwargs)
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defcreate_embedding(transformer):
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returnSentenceTransformer(transformer)
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defcreate_embedding(transformer,kwargs):
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returnSentenceTransformer(transformer,**kwargs)
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defembed_using(model,transformer,inputs,kwargs):
@@ -545,16 +545,32 @@ def embed_using(model, transformer, inputs, kwargs):
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defembed(transformer,inputs,kwargs):
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kwargs=orjson.loads(kwargs)
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ensure_device(kwargs)
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init_kwarg_keys= [
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"device",
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"trust_remote_code",
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"revision",
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"model_kwargs",
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"tokenizer_kwargs",
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"config_kwargs",
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"truncate_dim",
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"token",
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]
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init_kwargs= {
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key:valueforkey,valueinkwargs.items()ifkeyininit_kwarg_keys
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}
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encode_kwargs= {
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key:valueforkey,valueinkwargs.items()ifkeynotininit_kwarg_keys
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}
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iftransformernotin__cache_sentence_transformer_by_name:
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__cache_sentence_transformer_by_name[transformer]=create_embedding(
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transformer
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transformer,init_kwargs
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)
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model=__cache_sentence_transformer_by_name[transformer]
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returnembed_using(model,transformer,inputs,kwargs)
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returnembed_using(model,transformer,inputs,encode_kwargs)
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defclear_gpu_cache(memory_usage:None):

‎pgml-extension/tests/test.sql

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Original file line numberDiff line numberDiff line change
@@ -31,5 +31,6 @@ SELECT pgml.load_dataset('wine');
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\i examples/vectors.sql
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\i examples/chunking.sql
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\i examples/preprocessing.sql
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\i examples/embedding.sql
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-- transformers are generally too slow to run in the test suite
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--\i examples/transformers.sql

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