mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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233 lines
8.9 KiB
Python
233 lines
8.9 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 os
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import pytest
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from defs.common import convert_weights, venv_check_call, venv_mpi_check_call
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from defs.conftest import get_device_memory
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from defs.trt_test_alternative import check_call
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OPT_LIST = {
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"opt-125m": {
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"build": [],
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"infer": ["--tensorrt_llm_rouge1_threshold=14"]
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},
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"opt-350m": {
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"build": [],
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"infer": ["--tensorrt_llm_rouge1_threshold=19"]
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},
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"opt-2.7b": {
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"build": [],
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"infer": ["--tensorrt_llm_rouge1_threshold=20"]
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}
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}
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@pytest.mark.parametrize("llm_opt_model_root",
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['opt-125m', 'opt-350m', 'opt-2.7b'],
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indirect=True)
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@pytest.mark.parametrize(
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"use_attention_plugin", [True, False],
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ids=["enable_attention_plugin", "disable_attention_plugin"])
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@pytest.mark.parametrize("use_gemm_plugin", [True, False],
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ids=["enable_gemm_plugin", "disable_gemm_plugin"])
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@pytest.mark.parametrize("context_fmha_type",
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['enabled', 'enabled_with_fp32_acc', 'disabled'])
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def test_llm_opt_single_gpu_summary(opt_example_root, llm_venv,
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llm_opt_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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use_attention_plugin, use_gemm_plugin,
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context_fmha_type):
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"Build & run opt summary on single gpu"
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model_name = os.path.basename(llm_opt_model_root)
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dtype = "float16"
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=opt_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_opt_model_root,
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data_type=dtype)
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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={model_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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f"--output_dir={engine_dir}",
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]
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if use_attention_plugin:
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build_cmd.append(f"--gpt_attention_plugin={dtype}")
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if context_fmha_type == 'enabled':
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == 'disabled':
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build_cmd.append("--context_fmha=disable")
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else:
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build_cmd.extend([
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"--gpt_attention_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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if use_gemm_plugin:
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build_cmd.append(f"--gemm_plugin={dtype}")
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else:
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build_cmd.append("--gemm_plugin=disable")
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build_cmd.extend(OPT_LIST[model_name]['build'])
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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 = [
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f"{opt_example_root}/../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{llm_opt_model_root}", "--data_type", "fp16",
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"--check_accuracy", f"--engine_dir={engine_dir}",
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f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
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]
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summary_cmd.extend(OPT_LIST[model_name]['infer'])
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if context_fmha_type == "enabled_with_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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@pytest.mark.skip_less_device(4)
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@pytest.mark.parametrize("llm_opt_model_root", ['opt-66b'], indirect=True)
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@pytest.mark.parametrize("use_plugin", [True, False],
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ids=["enable_plugin", "disable_plugin"])
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@pytest.mark.parametrize("context_fmha_type",
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['enabled', 'enabled_with_fp32_acc', 'disabled'])
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def test_llm_opt_4gpus_summary(opt_example_root, llm_venv, cmodel_dir,
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engine_dir, llm_opt_model_root,
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llm_datasets_root, llm_rouge_root, use_plugin,
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context_fmha_type):
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"Build & run opt 66b summary on single node 4 gpus"
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if not use_plugin and get_device_memory() < 50000:
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pytest.skip("device memory is insufficient.")
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model_name = os.path.basename(llm_opt_model_root)
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dtype = "float16"
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=opt_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_opt_model_root,
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data_type=dtype,
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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={model_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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f"--output_dir={engine_dir}",
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f"--workers={4}",
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]
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if use_plugin:
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build_cmd.append(f"--gpt_attention_plugin={dtype}")
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build_cmd.append(f"--gemm_plugin={dtype}")
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if context_fmha_type == 'enabled':
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build_cmd.append("--context_fmha=enable")
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elif context_fmha_type == 'disabled':
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build_cmd.append("--context_fmha=disable")
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else:
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build_cmd.extend([
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"--gpt_attention_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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"--gemm_plugin=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("Run summarize...")
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summary_cmd = [
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f"{opt_example_root}/../summarize.py",
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"--test_trt_llm",
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"--hf_model_dir",
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f"{llm_opt_model_root}",
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"--data_type",
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"fp16",
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"--check_accuracy",
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f"--engine_dir={engine_dir}",
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"--tensorrt_llm_rouge1_threshold=18",
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f"--dataset_dir={llm_datasets_root}",
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f"--rouge_dir={llm_rouge_root}",
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'--kv_cache_free_gpu_memory_fraction=0.8',
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]
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if context_fmha_type == "enabled_with_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(2)
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@pytest.mark.parametrize("embedding_sharding_dim", [0, 1])
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@pytest.mark.parametrize("llm_opt_model_root",
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['opt-125m', 'opt-350m', 'opt-2.7b'],
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indirect=True)
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def test_llm_opt_parallel_embedding_2gpu(opt_example_root, llm_venv,
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llm_opt_model_root, llm_datasets_root,
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llm_rouge_root, cmodel_dir, engine_dir,
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embedding_sharding_dim):
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"OPT with parallel embedding"
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print("Converting OPT model into FastTransformer format...")
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model_name = os.path.basename(llm_opt_model_root)
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dtype = "float16"
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model_dir = convert_weights(llm_venv=llm_venv,
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example_root=opt_example_root,
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cmodel_dir=cmodel_dir,
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model=model_name,
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model_path=llm_opt_model_root,
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data_type=dtype,
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gpus=2,
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use_parallel_embedding=True,
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embedding_sharding_dim=embedding_sharding_dim)
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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={model_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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f"--output_dir={engine_dir}",
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f"--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 = [
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f"{opt_example_root}/../summarize.py", "--test_trt_llm",
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"--hf_model_dir", f"{llm_opt_model_root}", "--data_type", "fp16",
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"--check_accuracy", f"--engine_dir={engine_dir}",
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"--tensorrt_llm_rouge1_threshold=14",
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f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}"
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]
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venv_mpi_check_call(llm_venv, ["mpirun", "--allow-run-as-root", "-np", "2"],
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summary_cmd)
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