#!/usr/bin/env python3 # SPDX-FileCopyrightText: Copyright (c) 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. import argparse as _arg from pathlib import Path import run from build_engines_utils import init_model_spec_module init_model_spec_module() import os import model_spec import tensorrt_llm.bindings as _tb def generate_output(engine: str, model_spec_obj: model_spec.ModelSpec, max_output_len: int = 8): model = 'vicuna-7b-redrafter' hf_model = 'vicuna-7b-v1.3' resources_dir = Path(__file__).parent.resolve().parent models_dir = resources_dir / 'models' hf_dir = models_dir / hf_model tp_pp_dir = 'tp1-pp1-gpu/' engine_dir = models_dir / 'rt_engine' / model / engine / tp_pp_dir data_dir = resources_dir / 'data' input_filename = model_spec_obj.get_input_file() input_file = data_dir / input_filename model_data_dir = data_dir / model output_dir = model_data_dir / 'sampling' base_output_name = os.path.splitext(model_spec_obj.get_results_file())[0] args = run.parse_arguments([ '--engine_dir', str(engine_dir), '--input_file', str(input_file), '--tokenizer_dir', str(hf_dir), '--output_npy', str(output_dir / (base_output_name + '.npy')), '--output_csv', str(output_dir / (base_output_name + '.csv')), '--max_output_len', str(max_output_len), '--use_py_session', ]) run.main(args) print(f"Output saved at {str(output_dir / base_output_name)}.[npy|csv]") def generate_outputs(): print(f'Generating outputs for ReDrafter FP16') max_output_len = 128 model_spec_obj = model_spec.ModelSpec('input_vicuna.npy', _tb.DataType.HALF) model_spec_obj.use_gpt_plugin() model_spec_obj.set_max_output_length(max_output_len) model_spec_obj.use_packed_input() model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED) model_spec_obj.use_explicit_draft_tokens_decoding() generate_output(engine=model_spec_obj.get_model_path(), model_spec_obj=model_spec_obj, max_output_len=max_output_len) if __name__ == '__main__': parser = _arg.ArgumentParser() args = parser.parse_args() generate_outputs() print("Done")