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| # Copyright (C) 2023-2024 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import pytest | ||
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| model_ids = [ | ||
| ("TinyLlama/TinyLlama-1.1B-Chat-v1.0", "TinyLlama-1.1B-Chat-v1.0/pytorch/dldt/FP16/"), | ||
| # ("meta-llama/Llama-2-7b-chat-hf", "Llama-2-7b-chat-hf/pytorch/dldt/FP16/"), | ||
| # ("microsoft/phi-1_5", "phi-1_5/"), | ||
| # ("google/gemma-2b-it", "gemma-2b-it/pytorch/dldt/FP16/"), | ||
| ] | ||
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| def run_hf_ov_genai_comparison(model_fixture, generation_config, prompt): | ||
| model_id, path, tokenizer, model = model_fixture | ||
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| generation_config_hf = generation_config.copy() | ||
| # in OpenVINO GenAI this parameter is called stop_criteria, | ||
| # while in HF it's called early_stopping. | ||
| # HF values True, False and "never" correspond to OV GenAI values "early", "heuristic" and "never" | ||
| if generation_config_hf.get('stop_criteria'): | ||
| generation_config_hf['early_stopping'] = stop_criteria_map()[generation_config_hf.pop('stop_criteria')] | ||
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| encoded_prompt = tokenizer.encode(prompt, return_tensors='pt', add_special_tokens=True) | ||
| hf_encoded_output = model.generate(encoded_prompt, **generation_config_hf) | ||
| hf_output = tokenizer.decode(hf_encoded_output[0, encoded_prompt.shape[1]:]) | ||
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| import sys | ||
| # sys.path.append('../../src/python/openvino_genai/') | ||
| sys.path.append('/home/epavel/devel/openvino.genai/src/python/openvino_genai/') | ||
| import py_generate_pipeline as genai | ||
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| pipe = genai.LLMPipeline(path) | ||
| ov_output = pipe.generate(prompt, **generation_config) | ||
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| if hf_output != ov_output: | ||
| print(f'hf_output: {hf_output}') | ||
| print(f'ov_output: {ov_output}') | ||
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| assert hf_output == ov_output | ||
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| def stop_criteria_map(): | ||
| return {"never": "never", "early": True, "heuristic": False} | ||
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| @pytest.fixture(scope="module", params=model_ids) | ||
| def model_fixture(request): | ||
| model_id, path = request.param | ||
| from transformers import AutoTokenizer, AutoModelForCausalLM | ||
| tokenizer = AutoTokenizer.from_pretrained(model_id) | ||
| model = AutoModelForCausalLM.from_pretrained(model_id) | ||
| return model_id, path, tokenizer, model | ||
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| test_cases = [ | ||
| (dict(max_new_tokens=20, do_sample=False), 'table is made of'), # generation_config, prompt | ||
| (dict(num_beam_groups=3, num_beams=15, num_return_sequences=15, max_new_tokens=20, diversity_penalty=1.0), 'table is made of'), | ||
| (dict(num_beam_groups=3, num_beams=15, num_return_sequences=15, max_new_tokens=20, diversity_penalty=1.0), 'Alan Turing was a'), | ||
| (dict(num_beam_groups=3, num_beams=15, num_return_sequences=15, max_new_tokens=30, diversity_penalty=1.0), 'Alan Turing was a'), | ||
| (dict(num_beam_groups=2, num_beams=8, num_return_sequences=8, max_new_tokens=20, diversity_penalty=1.0), 'table is made of'), | ||
| (dict(num_beam_groups=2, num_beams=8, num_return_sequences=8, max_new_tokens=20, diversity_penalty=1.0), 'The Sun is yellow because'), | ||
| (dict(num_beam_groups=2, num_beams=8, num_return_sequences=8, max_new_tokens=20, diversity_penalty=1.5), 'The Sun is yellow because'), | ||
| ] | ||
| @pytest.mark.parametrize("generation_config,prompt", test_cases) | ||
| def test_greedy_decoding(model_fixture, generation_config, prompt): | ||
| run_hf_ov_genai_comparison(model_fixture, generation_config, prompt) | ||
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| prompts = ['The Sun is yellow because'] #, 'Alan Turing was a', 'table is made of'] | ||
| @pytest.mark.parametrize("num_beam_groups", [2, 3]) | ||
| @pytest.mark.parametrize("group_size", [5, 3]) | ||
| @pytest.mark.parametrize("max_new_tokens", [20, 15]) | ||
| @pytest.mark.parametrize("diversity_penalty", [1.0, 1.5]) | ||
| @pytest.mark.parametrize("prompt", prompts) | ||
| def test_beam_search_decoding(model_fixture, num_beam_groups, group_size, | ||
| max_new_tokens, diversity_penalty, prompt): | ||
| generation_config = dict( | ||
| num_beam_groups=num_beam_groups, | ||
| num_beams=num_beam_groups * group_size, | ||
| diversity_penalty=diversity_penalty, | ||
| num_return_sequences=num_beam_groups * group_size, | ||
| max_new_tokens=max_new_tokens, | ||
| ) | ||
| run_hf_ov_genai_comparison(model_fixture, generation_config, prompt) | ||
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| @pytest.mark.parametrize("stop_criteria", ["never", "early", "heuristic"]) | ||
| @pytest.mark.parametrize("prompt", prompts) | ||
| @pytest.mark.parametrize("max_new_tokens", [20, 15]) | ||
| def test_greedy_decoding(model_fixture, stop_criteria, prompt, max_new_tokens): | ||
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| generation_config = dict( | ||
| num_beam_groups=2, | ||
| num_beams=2 * 3, | ||
| diversity_penalty=1.0, | ||
| num_return_sequences=2 * 3, | ||
| max_new_tokens=max_new_tokens, | ||
| stop_criteria=stop_criteria, | ||
| ) | ||
| run_hf_ov_genai_comparison(model_fixture, generation_config, prompt) | ||
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