Integrate lm-eval-harness for evaluations in NeMo #10621
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This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days. |
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| self.add_bos = add_bos | ||
| super().__init__() | ||
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| def _generate_tokens_logits(self, payload, return_text: bool = False, return_logits: bool = False): |
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Explicit returns mixed with implicit (fall through) returns Note
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[🤖]: Hi @athitten 👋, We wanted to let you know that a CICD pipeline for this PR just finished successfully So it might be time to merge this PR or get some approvals I'm just a bot so I'll leave it you what to do next. //cc @pablo-garay @ko3n1g |
athitten
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| with open("nemo/deploy/service/config.json", "w") as f: | ||
| json.dump(args_dict, f) | ||
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get_trtllm_deployable() has been moved to nemo/collections/llm/evaluation/eval_utils.py
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store_args_to_json() has been removed to avoid creating a new file to store the args. Instead triton args are stored as env vars in deploy() method to access in rest_model_api.py
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beep boop 🤖: 🙏 The following files have warnings. In case you are familiar with these, please try helping us to improve the code base. Your code was analyzed with PyLint. The following annotations have been identified: Thank you for improving NeMo's documentation! |
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[🤖]: Hi @athitten 👋, We wanted to let you know that a CICD pipeline for this PR just finished successfully So it might be time to merge this PR or get some approvals I'm just a bot so I'll leave it you what to do next. //cc @pablo-garay @ko3n1g |
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: athitten <[email protected]>
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: athitten <[email protected]>
Signed-off-by: Abhishree <[email protected]>
Signed-off-by: Abhishree <[email protected]>
1) Refactors the code and creates an evaluation folder where all util methods live 2) Add doctsrings, comments 3) Expose gather_context_logits, gather_generation_logits in trtllm and add output_generation_logits flag to return generation logits and remove output_logporbs as its not getting used anymore Signed-off-by: Abhishree <[email protected]>
Signed-off-by: Abhishree <[email protected]>
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Signed-off-by: athitten <[email protected]>
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beep boop 🤖: 🚨 The following files must be fixed before merge! Your code was analyzed with PyLint. The following annotations have been identified: Thank you for improving NeMo's documentation! |
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What does this PR do ?
Integrates lm-evaluation-harness into NeMo to run evaluations on standard academic benchmarks.
Evaluations can be run by first deploying the model on PyTriton server via deploy method followed by running the evaluate method to do the actual evaluation.
Collection: This is an independent module and does not affect any collection.
Changelog
deploymethod innemo/collections/llm/api.pyby moving utility funcs tonemo/collections/llm/evaluation/eval_utils.py. Deploy method takes care of exporting nemo model to trtllm and deploying it on PyTriton server.nemo/deploy/service/rest_model_api.pywhich contains the code to interact with the PyTriton Server to perform the actual model inference via/v1/completionsendpoint.nemo/collections/llm/api.pyto evaluate nemo model deployed on PyTriton server (via trtllm) using lm-evaluation-harness.Evaluate method takes in as input the rest service url "http://rest_service_http_address:rest_service_port/v1" and other evaluation params like eval task, num_fewshot, limit etc., and inference params like temperature, top_p, top_k, max_tokens_to_generate
nemo/collections/llm/evaluation/eval_utils.pythat creates a wrapper to interface eval prompts from lm-eval-harness with the model deployed on PyTriton.Usage
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