mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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416 lines
17 KiB
Python
416 lines
17 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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"""Module test_mpt test mpt examples."""
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import os
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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.conftest import skip_pre_ada
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from defs.trt_test_alternative import check_call
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@pytest.mark.skip_less_device(4)
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@pytest.mark.parametrize("data_type", ['bfloat16'])
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@pytest.mark.parametrize("use_plugins", [True, False],
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ids=['enable_plugins', 'disable_plugins'])
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@pytest.mark.parametrize(
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"context_fmha_type",
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['enable_context_fmha', 'enable_context_fmha_fp32_acc', 'disable_fmha'])
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def test_llm_mpt_7b_1node_4gpus(mpt_example_root, llm_venv,
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llm_mpt_7b_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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data_type, use_plugins, context_fmha_type):
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"mpt 7b test on 4gpus"
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print("Converting MPT weights...")
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model_name = os.path.basename(llm_mpt_7b_model_root)
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=mpt_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_mpt_7b_model_root,
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data_type=data_type,
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gpus=4)
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print("Building 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"--max_batch_size={4}",
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f"--max_input_len={2048}",
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f"--max_seq_len={2560}",
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f"--workers={4}",
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]
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if use_plugins:
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if context_fmha_type == "enable_fmha":
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == "disable_fmha":
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build_cmd.append("--context_fmha=disable")
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build_cmd.extend([
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f"--gpt_attention_plugin={data_type}", f"--gemm_plugin={data_type}"
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])
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else:
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build_cmd.extend([
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"--gpt_attention_plugin=disable",
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"--gemm_plugin=disable",
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"--context_fmha=disable",
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"--paged_kv_cache=disable",
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"--remove_input_padding=disable",
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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("Running inference...")
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_7b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=18,
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dataset_dir=llm_datasets_root,
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rouge_dir=llm_rouge_root)
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if context_fmha_type == "enable_context_fmha_fp32_acc":
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summary_cmd.append("--enable_context_fmha_fp32_acc")
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venv_mpi_check_call(llm_venv, ["mpirun", "-n", "4", "--allow-run-as-root"],
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summary_cmd)
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@pytest.mark.skip_less_device(4)
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@pytest.mark.parametrize("data_type", ['bfloat16'])
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@pytest.mark.parametrize("use_plugins", [True, False],
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ids=['enable_plugins', 'disable_plugins'])
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@pytest.mark.parametrize(
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"context_fmha_type",
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['enable_context_fmha', 'enable_context_fmha_fp32_acc', 'disable_fmha'])
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def test_llm_mpt_30b_1node_4gpus(mpt_example_root, llm_venv,
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llm_mpt_30b_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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data_type, use_plugins, context_fmha_type):
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"mpt 30b test on 4gpus"
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print("Converting MPT weights...")
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model_name = os.path.basename(llm_mpt_30b_model_root)
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=mpt_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_mpt_30b_model_root,
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data_type=data_type,
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gpus=4)
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print("Building 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"--max_batch_size={4}",
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f"--max_input_len={1024}",
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f"--max_seq_len={1124}",
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f"--workers={4}",
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]
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if use_plugins:
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if context_fmha_type == "enable_fmha":
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == "disable_fmha":
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build_cmd.append("--context_fmha=disable")
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build_cmd.extend([
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f"--gpt_attention_plugin={data_type}", f"--gemm_plugin={data_type}"
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])
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else:
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build_cmd.extend([
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"--gpt_attention_plugin=disable",
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"--gemm_plugin=disable",
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"--context_fmha=disable",
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"--paged_kv_cache=disable",
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"--remove_input_padding=disable",
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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("Running inference...")
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_30b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=17,
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dataset_dir=llm_datasets_root,
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rouge_dir=llm_rouge_root)
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if context_fmha_type == "enable_context_fmha_fp32_acc":
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summary_cmd.append("--enable_context_fmha_fp32_acc")
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venv_mpi_check_call(llm_venv, ["mpirun", "-n", "4", "--allow-run-as-root"],
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summary_cmd)
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@pytest.mark.parametrize(
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"context_fmha_type",
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['enable_context_fmha', 'enable_context_fmha_fp32_acc', 'disable_fmha'])
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def test_llm_mpt_7b_1node_1gpu(mpt_example_root, llm_venv,
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llm_mpt_7b_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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context_fmha_type):
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"mpt-7b test on one gpu"
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ckpt_dir = convert_weights(llm_venv, mpt_example_root, cmodel_dir, "mpt-7b",
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llm_mpt_7b_model_root)
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print("Building 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"--max_batch_size={2}",
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f"--max_input_len={1024}",
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f"--max_beam_width={5}",
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"--gemm_plugin=float16",
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]
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if context_fmha_type == "enable_fmha":
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == "disable_fmha":
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build_cmd.append("--context_fmha=disable")
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check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)
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print("Running inference...")
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_7b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=20,
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dataset_dir=llm_datasets_root,
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rouge_dir=llm_rouge_root)
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if context_fmha_type == "enable_context_fmha_fp32_acc":
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summary_cmd.append("--enable_context_fmha_fp32_acc")
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venv_check_call(llm_venv, summary_cmd)
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# transformers compatibility issues
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# ImportError: cannot import name '_expand_mask' from 'transformers.models.bloom.modeling_bloom'
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def test_llm_mpt_125m_summary(mpt_example_root, llm_venv,
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llm_mpt_125m_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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update_transformers):
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"mpt-125m summary test"
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=mpt_example_root,
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cmodel_dir=cmodel_dir,
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model="mpt-125m",
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model_path=llm_mpt_125m_model_root,
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data_type="float32")
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print("Building 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"--max_batch_size={8}",
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f"--max_input_len={924}",
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f"--max_seq_len={1024}",
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"--gpt_attention_plugin=float32",
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"--gemm_plugin=float32",
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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("Running summary...")
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_125m_model_root,
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engine_dir=engine_dir,
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batch_size=1,
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data_type="fp32",
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tensorrt_llm_rouge1_threshold=10,
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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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@skip_pre_ada
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def test_llm_mpt_7b_fp8_summary(mpt_example_root, llm_mpt_7b_model_root,
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llm_datasets_root, llm_rouge_root, llm_venv,
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engine_dir, qcache_dir):
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"Build & Run mpt 7b with fp8."
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# Quantize HF mpt 7b checkpoint into FP8 format
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quantize_cmd = [
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f"{mpt_example_root}/../quantization/quantize.py",
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f"--model_dir={llm_mpt_7b_model_root}",
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f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
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"--dtype=float16",
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"--qformat=fp8",
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"--kv_cache_dtype=fp8",
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f"--output_dir={qcache_dir}/quantized_fp8",
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]
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venv_check_call(llm_venv, quantize_cmd)
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print("Building engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={qcache_dir}/quantized_fp8/",
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f"--output_dir={engine_dir}",
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f"--max_input_len={1024}",
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"--gpt_attention_plugin=float16",
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"--gemm_plugin=float16",
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"--remove_input_padding=enable",
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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 mpt-7b fp8...')
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_7b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=20,
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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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def test_llm_mpt_7b_awq_int4_summary(mpt_example_root, llm_mpt_7b_model_root,
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llm_datasets_root, llm_rouge_root,
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llm_venv, engine_dir, qcache_dir):
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"Build & Run mpt 7b with awq int4 gpus"
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# Quantize HF mpt-7b checkpoint into int4 format
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quantize_cmd = [
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f"{mpt_example_root}/../quantization/quantize.py",
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f"--model_dir={llm_mpt_7b_model_root}",
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f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
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"--dtype=float16",
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"--qformat=int4_awq",
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"--calib_size=32",
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f"--output_dir={qcache_dir}/quantized_int4",
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]
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venv_check_call(llm_venv, quantize_cmd)
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print("Building engines...")
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build_cmd = [
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"trtllm-build",
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f"--checkpoint_dir={qcache_dir}/quantized_int4/",
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f"--output_dir={engine_dir}",
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f"--max_batch_size={64}",
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f"--max_input_len={1024}",
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"--gemm_plugin=float16",
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"--gpt_attention_plugin=float16",
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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 mpt-7b awq int4...')
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_7b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=20,
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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.parametrize("data_type", ['int8', 'int4'])
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def test_llm_mpt_7b_weight_only(mpt_example_root, llm_venv,
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llm_mpt_7b_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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data_type):
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"mpt-7b test with weight only"
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=mpt_example_root,
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cmodel_dir=cmodel_dir,
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model="mpt-7b",
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model_path=llm_mpt_7b_model_root,
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weight_only_precision=data_type)
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print("Building 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"--max_batch_size={64}",
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f"--max_input_len={1024}",
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"--gemm_plugin=float16",
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"--gpt_attention_plugin=float16",
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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("Running inference...")
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# For weight-only int4, mpt-7b has bad accuracy while mpt-125m and
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# mpt-30b has comparable accuracy with FP16.
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summary_cmd = generate_summary_cmd(mpt_example_root,
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hf_model_dir=llm_mpt_7b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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tensorrt_llm_rouge1_threshold=20,
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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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# transformers compatibility issues
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# ImportError: cannot import name '_expand_mask' from 'transformers.models.bloom.modeling_bloom'
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@pytest.mark.skip_less_device(2)
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@pytest.mark.parametrize("num_beams", [1, 4],
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ids=lambda num_beams: f'nb:{num_beams}')
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def test_llm_replit_code_v1_5_3b_1node_2gpus(mpt_example_root, llm_venv,
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llm_replit_code_v1_5_3b_model_root,
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llm_datasets_root, llm_rouge_root,
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cmodel_dir, engine_dir, num_beams,
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update_transformers):
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"replit code v1_5 3b test with 2gpus"
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ckpt_dir = convert_weights(llm_venv=llm_venv,
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example_root=mpt_example_root,
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cmodel_dir=cmodel_dir,
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model="mpt_replit_code",
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model_path=llm_replit_code_v1_5_3b_model_root,
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data_type="bfloat16",
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gpus=2)
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print("Building 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"--max_batch_size={16}",
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f"--max_input_len={1024}",
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f"--max_beam_width={num_beams}",
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"--gemm_plugin=bfloat16",
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"--gpt_attention_plugin=bfloat16",
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"--workers=2",
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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("Running inference...")
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summary_cmd = generate_summary_cmd(
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mpt_example_root,
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hf_model_dir=llm_replit_code_v1_5_3b_model_root,
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engine_dir=engine_dir,
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data_type="fp16",
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num_beams=num_beams,
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tensorrt_llm_rouge1_threshold=10,
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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", "2", "--allow-run-as-root"],
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summary_cmd)
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