mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 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 os
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import pytest
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from defs.common import (convert_weights, generate_deterministic_cmd,
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venv_mpi_check_call)
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from defs.conftest import skip_pre_hopper
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from defs.trt_test_alternative import check_call
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@skip_pre_hopper
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@pytest.mark.skip_less_device(4)
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@pytest.mark.skip_less_device_memory(80000)
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@pytest.mark.parametrize("data_type", ['float16', 'bfloat16'])
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@pytest.mark.parametrize("llm_mixtral_model_root",
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['Mixtral-8x7B-Instruct-v0.1'],
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indirect=True)
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def test_llm_mixtral_4gpus_deterministic(llama_example_root,
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llm_mixtral_model_root,
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deterministic_test_root, llm_venv,
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cmodel_dir, engine_dir, data_type):
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tp_size, pp_size = 4, 1
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world_size = tp_size * pp_size
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moe_tp_size = tp_size
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os.environ['FORCE_DETERMINISTIC'] = "1"
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print("Convert checkpoint...")
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=llama_example_root,
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cmodel_dir=cmodel_dir,
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model="mixtral-instruct",
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model_path=llm_mixtral_model_root,
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tp_size=tp_size,
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moe_tp_size=moe_tp_size,
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pp_size=pp_size,
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data_type=data_type,
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workers=world_size)
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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={ckpt_dir}",
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f"--output_dir={engine_dir}",
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f"--workers={world_size}",
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"--use_paged_context_fmha=enable",
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"--max_batch_size=256",
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"--max_num_tokens=33280",
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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 deterministic test...")
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deterministic_accuracy_threshold = 1
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payload = os.path.join(deterministic_test_root, "payload.json")
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deterministic_cmd = generate_deterministic_cmd(
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deterministic_test_root,
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engine_dir=engine_dir,
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tokenizer_dir=llm_mixtral_model_root,
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payload=payload,
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deterministic_accuracy_threshold=deterministic_accuracy_threshold)
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venv_mpi_check_call(
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llm_venv, ["mpirun", "-n", f"{world_size}", "--allow-run-as-root"],
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deterministic_cmd)
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os.environ.pop('FORCE_DETERMINISTIC', None)
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