mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
32ed92e449
Co-authored-by: Rong Zhou <130957722+ReginaZh@users.noreply.github.com> Co-authored-by: Onur Galoglu <33498883+ogaloglu@users.noreply.github.com> Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com>
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
#!/usr/bin/env python3
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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse as _arg
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from pathlib import Path
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import run
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from build_engines_utils import init_model_spec_module
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from mpi4py.MPI import COMM_WORLD
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init_model_spec_module()
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import os
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import model_spec
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import tensorrt_llm.bindings as _tb
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def generate_output(engine: str,
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num_beams: int,
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model_spec_obj: model_spec.ModelSpec,
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tp_size: int = 1,
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pp_size: int = 1,
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max_output_len: int = 8):
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model = 'llama-7b-hf'
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resources_dir = Path(__file__).parent.resolve().parent
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models_dir = resources_dir / 'models'
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hf_dir = models_dir / model
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tp_pp_dir = 'tp' + str(tp_size) + '-pp' + str(pp_size) + '-gpu/'
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engine_dir = models_dir / 'rt_engine' / model / engine / tp_pp_dir
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data_dir = resources_dir / 'data'
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input_file = data_dir / 'input_tokens.npy'
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model_data_dir = data_dir / model
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if num_beams <= 1:
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output_dir = model_data_dir / 'sampling'
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else:
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output_dir = model_data_dir / ('beam_search_' + str(num_beams))
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base_output_name = os.path.splitext(model_spec_obj.get_results_file())[0]
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args = run.parse_arguments([
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'--engine_dir',
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str(engine_dir), '--input_file',
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str(input_file), '--tokenizer_dir',
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str(hf_dir), '--output_npy',
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str(output_dir / (base_output_name + '.npy')), '--output_csv',
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str(output_dir / (base_output_name + '.csv')), '--max_output_len',
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str(max_output_len), '--num_beams',
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str(num_beams), '--use_py_session'
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])
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run.main(args)
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def generate_outputs(num_beams, only_multi_gpu=False):
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if not only_multi_gpu:
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tp_pp_sizes = [(1, 1)]
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elif COMM_WORLD.size == 4:
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tp_pp_sizes = [(4, 1), (2, 2), (1, 4)]
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elif COMM_WORLD.size == 2:
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tp_pp_sizes = [(1, 2)]
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else:
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raise RuntimeError(
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f"The world size of MPI {COMM_WORLD.size} is not equal to 1, 2, or 4."
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)
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model_spec_obj = model_spec.ModelSpec('input_tokens.npy', _tb.DataType.HALF)
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model_spec_obj.use_gpt_plugin()
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model_spec_obj.set_kv_cache_type(_tb.KVCacheType.PAGED)
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model_spec_obj.use_packed_input()
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for tp_size, pp_size in tp_pp_sizes:
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print(
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f'Generating outputs for Llama FP16 with TP={tp_size} and PP={pp_size}'
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)
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model_spec_obj.use_tensor_parallelism(tp_size)
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model_spec_obj.use_pipeline_parallelism(pp_size)
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generate_output(engine=model_spec_obj.get_model_path(),
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num_beams=num_beams,
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tp_size=tp_size,
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pp_size=pp_size,
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model_spec_obj=model_spec_obj)
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if __name__ == '__main__':
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parser = _arg.ArgumentParser()
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parser.add_argument(
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"--only_multi_gpu",
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action="store_true",
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help="Generate data with Pipeline and Tensor Parallelism")
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args = parser.parse_args()
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generate_outputs(num_beams=1, only_multi_gpu=args.only_multi_gpu)
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generate_outputs(num_beams=2, only_multi_gpu=args.only_multi_gpu)
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print("Done")
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