mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
# 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 pytest
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from defs.common import (convert_weights, generate_summary_cmd, venv_check_call,
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venv_mpi_check_call)
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from defs.trt_test_alternative import check_call
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@pytest.mark.skip_less_device_memory(40000)
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@pytest.mark.parametrize("num_beams", [1, 2, 4],
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ids=lambda num_beams: f'nb:{num_beams}')
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@pytest.mark.parametrize("deepseek_v2_model_root", ['DeepSeek-V2-Lite'],
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indirect=True)
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def test_llm_deepseek_v2_lite_summary(deepseek_v2_example_root,
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deepseek_v2_model_root, llm_datasets_root,
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llm_rouge_root, llm_venv, cmodel_dir,
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engine_dir, num_beams):
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model_name = 'deepseek_v2_lite'
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=deepseek_v2_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=deepseek_v2_model_root,
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data_type="bfloat16")
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print("Build engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={model_dir}",
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f"--output_dir={engine_dir}",
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"--gpt_attention_plugin=bfloat16",
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f"--max_beam_width={num_beams}",
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"--use_paged_context_fmha=enable",
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"--max_seq_len=4096",
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]
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check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
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print("Run summarize...")
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summary_cmd = generate_summary_cmd(deepseek_v2_example_root,
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hf_model_dir=deepseek_v2_model_root,
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data_type="bf16",
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engine_dir=engine_dir,
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num_beams=num_beams,
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dataset_dir=llm_datasets_root,
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rouge_dir=llm_rouge_root)
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venv_check_call(llm_venv, summary_cmd)
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@pytest.mark.skip_less_device(8)
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@pytest.mark.skip_less_device_memory(80000)
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@pytest.mark.skip_less_host_memory(1536_000)
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@pytest.mark.parametrize("num_beams", [1, 2, 4],
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ids=lambda num_beams: f'nb:{num_beams}')
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@pytest.mark.parametrize("deepseek_v2_model_root", ['DeepSeek-V2'],
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indirect=True)
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def test_llm_deepseek_v2_8gpu_summary(deepseek_v2_example_root,
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deepseek_v2_model_root, llm_datasets_root,
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llm_rouge_root, llm_venv, cmodel_dir,
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engine_dir, num_beams):
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model_name = 'deepseek_v2'
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=deepseek_v2_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=deepseek_v2_model_root,
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data_type="bfloat16",
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gpus=8,
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workers=8,
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tp_size=8,
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pp_size=1)
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print("Build engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={model_dir}",
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f"--output_dir={engine_dir}",
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"--gpt_attention_plugin=bfloat16",
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f"--max_beam_width={num_beams}",
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"--use_paged_context_fmha=enable",
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"--max_seq_len=4096",
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]
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check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
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print("Run summarize...")
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summary_cmd = generate_summary_cmd(deepseek_v2_example_root,
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hf_model_dir=deepseek_v2_model_root,
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data_type="bf16",
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engine_dir=engine_dir,
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num_beams=num_beams,
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dataset_dir=llm_datasets_root,
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rouge_dir=llm_rouge_root)
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venv_mpi_check_call(llm_venv, ["mpirun", "-n", "8", "--allow-run-as-root"],
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summary_cmd)
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