# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Module test_exaone test exaone examples.""" import pytest from defs.common import (convert_weights, generate_summary_cmd, venv_check_call, venv_mpi_check_call) from defs.conftest import skip_post_blackwell from defs.trt_test_alternative import check_call @pytest.mark.parametrize("num_beams", [1, 2, 4], ids=lambda num_beams: f'nb:{num_beams}') @pytest.mark.parametrize("data_type", ['bfloat16', 'float16']) @pytest.mark.parametrize("llm_exaone_model_root", ['exaone_3.0_7.8b_instruct', 'exaone_deep_2.4b'], indirect=True) @pytest.mark.parametrize("use_weight_only", [pytest.param(True, marks=skip_post_blackwell), False], ids=["enable_weight_only", "disable_weight_only"]) def test_llm_exaone_1gpu(data_type, exaone_example_root, llm_exaone_model_root, llama_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, num_beams, use_weight_only): print("Build engines...") model_name = "exaone" model_dir = convert_weights( llm_venv=llm_venv, # NOTE # EXAONE is based on llama so reuse llama's checkpoint converter example_root=llama_example_root, cmodel_dir=cmodel_dir, model=model_name, model_path=llm_exaone_model_root, data_type=data_type, use_weight_only=use_weight_only) build_cmd = [ "trtllm-build", f"--checkpoint_dir={model_dir}", f"--output_dir={engine_dir}", f"--max_beam_width={num_beams}", ] check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) rouge1_threshold = { 1: 22, 2: 22, 4: 23, }[num_beams] print("Run summarize...") summary_cmd = generate_summary_cmd( exaone_example_root, hf_model_dir=llm_exaone_model_root, engine_dir=engine_dir, data_type=data_type, tensorrt_llm_rouge1_threshold=rouge1_threshold, use_py_session=False, dataset_dir=llm_datasets_root, rouge_dir=llm_rouge_root, num_beams=num_beams, ) venv_check_call(llm_venv, summary_cmd) @pytest.mark.skip_less_device(2) @pytest.mark.parametrize("num_beams", [1], ids=lambda num_beams: f'nb:{num_beams}') @pytest.mark.parametrize("data_type", ['float16']) @pytest.mark.parametrize("llm_exaone_model_root", ['exaone_3.0_7.8b_instruct', 'exaone_deep_2.4b'], indirect=True) def test_llm_exaone_2gpu(data_type, exaone_example_root, llm_exaone_model_root, llama_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, num_beams): tp_size = 2 print("Build engines...") model_name = "exaone" model_dir = convert_weights( llm_venv=llm_venv, # NOTE # EXAONE is based on llama so reuse llama's checkpoint converter example_root=llama_example_root, cmodel_dir=cmodel_dir, model=model_name, model_path=llm_exaone_model_root, data_type=data_type, tp_size=tp_size, pp_size=1) build_cmd = [ "trtllm-build", f"--checkpoint_dir={model_dir}", f"--output_dir={engine_dir}", f"--max_beam_width={num_beams}" ] check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) print("Run summarize...") summary_cmd = generate_summary_cmd( exaone_example_root, hf_model_dir=llm_exaone_model_root, engine_dir=engine_dir, data_type=data_type, tensorrt_llm_rouge1_threshold=22, use_py_session=False, dataset_dir=llm_datasets_root, rouge_dir=llm_rouge_root, num_beams=num_beams, ) venv_mpi_check_call(llm_venv, ["mpirun", "-n", f"{tp_size}", "--allow-run-as-root"], summary_cmd)