[Tests]: Adding dummy causal models for testing in regular CI run #427
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Signed-off-by: Abukhoyer Shaik <[email protected]>
abukhoy
requested review from
quic-rishinr,
ochougul,
quic-hemagnih and
quic-amitraj
as code owners
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
Signed-off-by: Abukhoyer Shaik <[email protected]>
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Purpose of this PR:
This update aims to reduce test execution time for causal language model inference. Previously, tests were run using full-scale models with one or two layers, which was inefficient and time-consuming. Refactoring CLI api testing for independent testing and redundant conftest code.
What’s Changed:
Introduced dummy models with significantly smaller configurations by adjusting parameters such as
max_position_embeddings, num_hidden_layers, num_attention_heads, hidden_size, intermediate_size, vocab_size and additional_params.These lightweight models are used exclusively for testing purposes to ensure faster execution without compromising test coverage.
And CLI testing has two test scripts one is for export, compile, and execute, another is for infer cli api.
Note: This optimization is applied only to causal language models.