d2926c7672
* deprecate BigDLNativeTransformers and add specific LMEmbedding method * deprecate and add LM methods for langchain llms * add native params to native langchain * new imple for embedding * move ut from bigdlnative to casual llm * rename embeddings api and examples update align with usage updating * docqa example hot-fix * add more api docs * add langchain ut for starcoder * support model_kwargs for transformer methods when calling causalLM and add ut * ut fix for transformers embedding * update for langchain causal supporting transformers * remove model_family in readme doc * add model_families params to support more models * update api docs and remove chatglm embeddings for now * remove chatglm embeddings in examples * new refactor for ut to add bloom and transformers llama ut * disable llama transformers embedding ut
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from bigdl.llm.langchain.embeddings import *
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from bigdl.llm.langchain.llms import *
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import pytest
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from unittest import TestCase
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import os
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class Test_Models_Basics(TestCase):
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def setUp(self):
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self.llama_model_path = os.environ.get('LLAMA_INT4_CKPT_PATH')
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self.bloom_model_path = os.environ.get('BLOOM_INT4_CKPT_PATH')
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self.gptneox_model_path = os.environ.get('GPTNEOX_INT4_CKPT_PATH')
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self.starcoder_model_path = os.environ.get('STARCODER_INT4_CKPT_PATH')
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thread_num = os.environ.get('THREAD_NUM')
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if thread_num is not None:
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self.n_threads = int(thread_num)
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else:
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self.n_threads = 2
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def test_langchain_llm_embedding_llama(self):
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bigdl_embeddings = LlamaEmbeddings(
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model_path=self.llama_model_path)
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text = "This is a test document."
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query_result = bigdl_embeddings.embed_query(text)
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doc_result = bigdl_embeddings.embed_documents([text])
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def test_langchain_llm_embedding_gptneox(self):
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bigdl_embeddings = GptneoxEmbeddings(
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model_path=self.gptneox_model_path)
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text = "This is a test document."
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query_result = bigdl_embeddings.embed_query(text)
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doc_result = bigdl_embeddings.embed_documents([text])
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def test_langchain_llm_embedding_bloom(self):
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bigdl_embeddings = BloomEmbeddings(
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model_path=self.bloom_model_path)
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text = "This is a test document."
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query_result = bigdl_embeddings.embed_query(text)
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doc_result = bigdl_embeddings.embed_documents([text])
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def test_langchain_llm_embedding_starcoder(self):
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bigdl_embeddings = StarcoderEmbeddings(
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model_path=self.starcoder_model_path)
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text = "This is a test document."
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query_result = bigdl_embeddings.embed_query(text)
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doc_result = bigdl_embeddings.embed_documents([text])
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def test_langchain_llm_llama(self):
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llm = LlamaLLM(
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model_path=self.llama_model_path,
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max_tokens=32,
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n_threads=self.n_threads)
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question = "What is AI?"
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result = llm(question)
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def test_langchain_llm_gptneox(self):
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llm = GptneoxLLM(
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model_path=self.gptneox_model_path,
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max_tokens=32,
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n_threads=self.n_threads)
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question = "What is AI?"
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result = llm(question)
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def test_langchain_llm_bloom(self):
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llm = BloomLLM(
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model_path=self.bloom_model_path,
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max_tokens=32,
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n_threads=self.n_threads)
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question = "What is AI?"
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result = llm(question)
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def test_langchain_llm_starcoder(self):
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llm = StarcoderLLM(
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model_path=self.starcoder_model_path,
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max_tokens=32,
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n_threads=self.n_threads)
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question = "What is AI?"
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result = llm(question)
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if __name__ == '__main__':
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pytest.main([__file__])
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