mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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545 lines
22 KiB
Python
545 lines
22 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 argparse
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import copy
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import os
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import time
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import traceback
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from importlib.machinery import SourceFileLoader
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from multiprocessing import get_context
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from typing import Optional, Union
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import torch
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from tensorrt_llm.auto_parallel import infer_cluster_config
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from tensorrt_llm.auto_parallel.cluster_info import cluster_infos
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from tensorrt_llm.builder import BuildConfig, Engine, build
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from tensorrt_llm.functional import PositionEmbeddingType
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from tensorrt_llm.logger import logger
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from tensorrt_llm.lora_manager import LoraConfig, LoraManager
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from tensorrt_llm.models import MODEL_MAP, PretrainedConfig
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from tensorrt_llm.models.modeling_utils import SpeculativeDecodingMode
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from tensorrt_llm.plugin import PluginConfig, add_plugin_argument
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def parse_arguments():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--checkpoint_dir', type=str, default=None)
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parser.add_argument('--model_config', type=str, default=None)
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parser.add_argument('--build_config', type=str, default=None)
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parser.add_argument('--model_cls_file', type=str, default=None)
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parser.add_argument('--model_cls_name', type=str, default=None)
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parser.add_argument(
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'--input_timing_cache',
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type=str,
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default=None,
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help=
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'The path to read timing cache, will be ignored if the file does not exist'
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)
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parser.add_argument('--output_timing_cache',
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type=str,
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default='model.cache',
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help='The path to write timing cache')
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument(
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'--profiling_verbosity',
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type=str,
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default='layer_names_only',
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choices=['layer_names_only', 'detailed', 'none'],
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help=
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'The profiling verbosity for the generated TRT engine. Set to detailed can inspect tactic choices and kernel parameters.'
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)
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parser.add_argument('--enable_debug_output',
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default=False,
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action='store_true')
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parser.add_argument(
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'--output_dir',
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type=str,
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default='engine_outputs',
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help='The path to save the serialized engine files and model configs')
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parser.add_argument('--workers',
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type=int,
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default='1',
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help='The number of workers for building in parallel')
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parser.add_argument(
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'--max_batch_size',
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type=int,
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default=256,
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help="Max number of requests that the engine can handle.")
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parser.add_argument('--max_input_len',
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type=int,
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default=1024,
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help="Max input length of one request.")
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parser.add_argument(
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'--max_seq_len',
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'--max_decoder_seq_len',
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dest='max_seq_len',
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type=int,
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default=None,
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help="Max total length of one request, including prompt and outputs. "
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"If unspecified, will try to deduce from the model config.")
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parser.add_argument('--max_beam_width', type=int, default=1)
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parser.add_argument(
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'--max_num_tokens',
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type=int,
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default=8192,
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help="Max number of batched input tokens after padding is removed "
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"(triggered by `--remove_input_padding`) in each batch.")
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parser.add_argument(
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'--opt_num_tokens',
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type=int,
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default=None,
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help='It equals to max_batch_size*max_beam_width by default, set this '
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'value as close as possible to the actual number of tokens on your workload. '
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'Note that this argument might be removed in the future.')
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parser.add_argument('--tp_size', type=int, default=1)
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parser.add_argument('--pp_size', type=int, default=1)
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parser.add_argument(
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'--max_prompt_embedding_table_size',
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'--max_multimodal_len',
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type=int,
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default=0,
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help=
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'Setting to a value > 0 enables support for prompt tuning or multimodal input.'
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)
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parser.add_argument(
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'--use_fused_mlp',
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default=False,
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action='store_true',
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help=
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'Enable horizontal fusion in GatedMLP, reduces layer input traffic and potentially improves performance. '
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'For FP8 PTQ, the downside is slight reduction of accuracy because one of the quantization scaling factors is discarded. '
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'(An example for reference only: 0.45734 vs 0.45755 for LLaMA-v2 7B using `modelopt/examples/hf/instruct_eval/mmlu.py`).'
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)
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parser.add_argument(
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'--gather_all_token_logits',
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action='store_true',
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default=False,
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help='Enable both gather_context_logits and gather_generation_logits')
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parser.add_argument('--gather_context_logits',
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action='store_true',
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default=False,
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help='Gather context logits')
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parser.add_argument('--gather_generation_logits',
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action='store_true',
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default=False,
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help='Gather generation logits')
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parser.add_argument('--builder_opt', type=int, default=None)
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parser.add_argument('--builder_force_num_profiles', type=int, default=None)
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parser.add_argument('--logits_dtype',
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type=str,
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default=None,
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choices=['float16', 'float32'])
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parser.add_argument('--weight_sparsity', default=False, action='store_true')
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parser.add_argument(
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'--max_draft_len',
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type=int,
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default=0,
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help=
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'Maximum lengths of draft tokens for speculative decoding target model.'
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)
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parser.add_argument(
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'--lora_dir',
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type=str,
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default=None,
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nargs="+",
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help="The directory of LoRA weights. "
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"Use config from the first directory if multiple directories are provided."
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)
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parser.add_argument('--lora_ckpt_source',
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type=str,
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default="hf",
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choices=["hf", "nemo"],
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help="The source of lora checkpoint.")
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parser.add_argument(
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'--lora_target_modules',
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nargs='+',
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default=None,
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choices=LoraManager.LORA_MODULE_IDS.keys(),
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help=
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"Add lora in which modules. Only be activated when use_lora_plugin is enabled."
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)
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parser.add_argument(
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'--max_lora_rank',
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type=int,
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default=64,
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help='maximum lora rank for different lora modules. '
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'It is used to compute the workspace size of lora plugin.')
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parser.add_argument('--auto_parallel',
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type=int,
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default=1,
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help='MPI world size for auto parallel.')
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parser.add_argument(
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'--gpus_per_node',
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type=int,
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default=8,
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help=
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'Number of GPUs each node has in a multi-node setup. This is a cluster spec and can be greater/smaller than world size'
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)
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parser.add_argument(
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'--cluster_key',
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type=str,
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default=None,
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choices=cluster_infos.keys(),
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help=
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'Unique name for target GPU type. Inferred from current GPU type if not specified.'
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)
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parser.add_argument(
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'--strip_plan',
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default=False,
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action='store_true',
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help=
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'Whether to strip weights from the final TRT engine under the assumption that the refit weights will be identical to those provided at build time.'
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)
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parser.add_argument(
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'--max_encoder_input_len',
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type=int,
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default=1024,
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help=
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'Specify max encoder input length when using enc-dec models. Set max_input_len to 1 to start generation from decoder_start_token_id of length 1.'
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)
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parser.add_argument(
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'--visualize_network',
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default=False,
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action='store_true',
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help=
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'TRT Networks will be exported to ONNX prior to Engine build for debugging. '
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)
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parser.add_argument(
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'--dry_run',
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default=False,
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action='store_true',
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help=
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'Run through the build process except the actual Engine build for debugging. '
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)
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parser.add_argument('--speculative_decoding_mode',
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default=None,
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choices=[
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"draft_tokens_external",
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"lookahead_decoding",
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"medusa",
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"explicit_draft_tokens",
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],
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help='Mode of speculative decoding.')
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parser.add_argument(
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'--weight_streaming',
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default=False,
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action='store_true',
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help=
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'Specify whether offloading weights to CPU and streaming loading at runtime.',
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)
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plugin_config_parser = parser.add_argument_group("plugin_config")
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add_plugin_argument(plugin_config_parser)
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args = parser.parse_args()
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if args.gather_all_token_logits:
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args.gather_context_logits = True
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args.gather_generation_logits = True
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if args.gather_context_logits and args.max_draft_len > 0:
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raise RuntimeError(
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"Gather context logits is not support with draft len > 0. "
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"If want to get the accepted tokens' logits from target model, please just enable gather_generation_logits"
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)
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return args
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def build_model(
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build_config: BuildConfig,
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rank: int = 0,
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ckpt_dir: str = None,
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model_config: Union[str, PretrainedConfig] = None,
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model_cls=None,
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dry_run:
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bool = False, # return the modified BuildConfig without actually building the engine
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**kwargs
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) -> Union[Engine, BuildConfig]:
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model_config = copy.deepcopy(model_config)
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logits_dtype = kwargs.get('logits_dtype')
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if logits_dtype is not None:
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model_config.logits_dtype = logits_dtype
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architecture = model_config.architecture
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assert not build_config.plugin_config.streamingllm or architecture == "LlamaForCausalLM", \
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"StreamingLLM is only supported in the llama model."
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real_rank = rank
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if build_config.plugin_config.reduce_fusion and model_config.mapping.tp_size == 1:
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build_config.plugin_config.reduce_fusion = False
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model_config.mapping.gpus_per_node = build_config.auto_parallel_config.gpus_per_node
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if build_config.auto_parallel_config.enabled:
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assert rank < build_config.auto_parallel_config.world_size
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assert model_config.mapping.pp_size == 1 and model_config.mapping.tp_size == 1, \
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"You must convert to full model with TP=1&&PP=1 to use auto parallel planner"
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#TODO: TRTLLM-193 remove this after the new build API for autopp is done
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rank = 0 # This is a WAR to construct a whole model and load all the weights before auto parallel
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else:
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assert rank < model_config.mapping.world_size
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rank_config = copy.deepcopy(model_config)
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rank_config.set_rank(rank)
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if model_cls is None:
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assert architecture in MODEL_MAP, \
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f"Unsupported model architecture: {architecture}"
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model_cls = MODEL_MAP[architecture]
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if ckpt_dir is None:
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model = model_cls(rank_config)
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else:
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model = model_cls.from_checkpoint(ckpt_dir, config=rank_config)
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is_checkpoint_pruned = getattr(rank_config, 'is_pruned', False)
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if build_config.plugin_config.lora_plugin is not None:
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lora_config = LoraConfig(lora_dir=kwargs['lora_dir'] or [],
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lora_ckpt_source=kwargs['lora_ckpt_source'],
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max_lora_rank=kwargs['max_lora_rank'])
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if kwargs['lora_target_modules'] is not None:
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# command line options is preferred over the modules in the lora dir
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lora_config.lora_target_modules = kwargs['lora_target_modules']
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build_config.lora_config = lora_config
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build_config.use_fused_mlp = kwargs.get('use_fused_mlp', False)
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# tells the low level build api to only build rank-th shard of the model
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if build_config.auto_parallel_config.enabled:
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model.config.mapping.rank = real_rank
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if is_checkpoint_pruned or kwargs.pop('strip_plan', False):
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build_config.use_strip_plan = True
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build_config.use_refit = kwargs.get('refit', False)
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if dry_run:
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return build_config
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return build(model, build_config)
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def build_and_save(rank, gpu_id, ckpt_dir, build_config, output_dir, log_level,
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model_config, model_cls, **kwargs):
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torch.cuda.set_device(gpu_id)
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logger.set_level(log_level)
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engine = build_model(build_config,
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rank,
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ckpt_dir,
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model_config,
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model_cls=model_cls,
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**kwargs)
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assert engine is not None
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engine.save(output_dir)
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return True
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def parallel_build(model_config: PretrainedConfig,
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ckpt_dir: Optional[str],
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build_config: BuildConfig,
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output_dir: str,
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workers: int = 1,
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log_level: str = 'info',
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model_cls=None,
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**kwargs):
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if build_config.auto_parallel_config.enabled:
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if model_config.mapping.world_size > 1:
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raise RuntimeError(
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"manually TP and PP are not supported in auto parallel mode.")
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if build_config.auto_parallel_config.debug_mode:
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world_size = 1
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else:
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world_size = build_config.auto_parallel_config.world_size
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else:
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world_size = model_config.mapping.world_size
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if workers == 1:
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for rank in range(world_size):
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passed = build_and_save(rank, rank % workers, ckpt_dir,
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build_config, output_dir, log_level,
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model_config, model_cls, **kwargs)
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assert passed, "Engine building failed, please check error log."
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else:
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with ProcessPoolExecutor(mp_context=get_context('spawn'),
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max_workers=workers) as p:
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futures = [
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p.submit(build_and_save, rank, rank % workers, ckpt_dir,
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build_config, output_dir, log_level, model_config,
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model_cls, **kwargs) for rank in range(world_size)
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]
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exceptions = []
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for future in as_completed(futures):
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try:
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future.result()
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except Exception as e:
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traceback.print_exc()
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exceptions.append(e)
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assert len(exceptions
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) == 0, "Engine building failed, please check error log."
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def main():
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args = parse_arguments()
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logger.set_level(args.log_level)
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tik = time.time()
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if not os.path.exists(args.output_dir):
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os.makedirs(args.output_dir)
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model_cls = None
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if args.model_cls_file is not None:
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assert args.model_cls_name is not None
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loader = SourceFileLoader('models', args.model_cls_file)
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mod = loader.load_module()
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model_cls = getattr(mod, args.model_cls_name)
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workers = min(torch.cuda.device_count(), args.workers)
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plugin_config = PluginConfig.from_arguments(args)
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kwargs = {
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'logits_dtype': args.logits_dtype,
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'use_fused_mlp': args.use_fused_mlp,
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'tp_size': args.tp_size,
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'pp_size': args.pp_size,
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'lora_dir': args.lora_dir,
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'lora_ckpt_source': args.lora_ckpt_source,
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'max_lora_rank': args.max_lora_rank,
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'lora_target_modules': args.lora_target_modules,
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'strip_plan': args.strip_plan,
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'refit': False,
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}
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speculative_decoding_mode = SpeculativeDecodingMode.from_arguments(args)
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ckpt_dir_or_model_config = args.checkpoint_dir if args.checkpoint_dir is not None else args.model_config
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if ckpt_dir_or_model_config.lower().endswith('.json'):
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config_path = ckpt_dir_or_model_config
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ckpt_dir = None
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else:
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config_path = os.path.join(ckpt_dir_or_model_config, 'config.json')
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ckpt_dir = ckpt_dir_or_model_config
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model_config = PretrainedConfig.from_json_file(config_path)
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if args.build_config is None:
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if args.multiple_profiles == "enable" and args.opt_num_tokens is not None:
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raise RuntimeError(
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"multiple_profiles is enabled, while opt_num_tokens is set. "
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"They are not supposed to be working in the same time for now.")
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if args.cluster_key is not None:
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cluster_config = dict(cluster_key=args.cluster_key)
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else:
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cluster_config = infer_cluster_config()
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# Extract rotary scaling which will be used for checks and default value of max_seq_len
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rotary_scaling = getattr(model_config, "rotary_scaling", None)
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if rotary_scaling is not None:
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rotary_type = rotary_scaling.get('type',
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rotary_scaling.get('rope_type'))
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rotary_factor = rotary_scaling.get(
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'factor', 1.0) if rotary_type != 'su' else 1
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else:
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rotary_factor = 1
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if args.max_seq_len is None:
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# Step 1: Find the upper bound of max_seq_len
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deduced_max_seq_len = 2048
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if model_config.max_position_embeddings is not None:
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deduced_max_seq_len = model_config.max_position_embeddings
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# Step 2: Scale max_seq_len with rotary scaling
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if rotary_factor != 1:
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deduced_max_seq_len *= rotary_factor
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logger.warning(
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f'max_seq_len is scaled to {deduced_max_seq_len} by rotary scaling {rotary_factor}'
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)
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# Step 3: Assign the new max_seq_len
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args.max_seq_len = deduced_max_seq_len
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logger.info(
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f'max_seq_len is not specified, using value {deduced_max_seq_len}'
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)
|
|
else:
|
|
if not plugin_config.streamingllm and model_config.max_position_embeddings is not None \
|
|
and model_config.position_embedding_type != PositionEmbeddingType.relative:
|
|
if args.max_seq_len > model_config.max_position_embeddings * rotary_factor:
|
|
logger.warning(
|
|
f'max_seq_len {args.max_seq_len} is larger than max_position_embeddings {model_config.max_position_embeddings} * rotary scaling {rotary_factor}, '
|
|
'the model accuracy might be affected')
|
|
|
|
if args.max_input_len > args.max_seq_len:
|
|
logger.warning(
|
|
f'max_input_len is {args.max_input_len} is larger than max_seq_len {args.max_seq_len}, clipping it to max_seq_len'
|
|
)
|
|
args.max_input_len = args.max_seq_len
|
|
|
|
build_config = BuildConfig.from_dict(
|
|
{
|
|
'max_input_len': args.max_input_len,
|
|
'max_seq_len': args.max_seq_len,
|
|
'max_batch_size': args.max_batch_size,
|
|
'max_beam_width': args.max_beam_width,
|
|
'max_num_tokens': args.max_num_tokens,
|
|
'opt_num_tokens': args.opt_num_tokens,
|
|
'max_prompt_embedding_table_size':
|
|
args.max_prompt_embedding_table_size,
|
|
'gather_context_logits': args.gather_context_logits,
|
|
'gather_generation_logits': args.gather_generation_logits,
|
|
'strongly_typed': True,
|
|
'builder_opt': args.builder_opt,
|
|
'force_num_profiles': args.builder_force_num_profiles,
|
|
'weight_sparsity': args.weight_sparsity,
|
|
'profiling_verbosity': args.profiling_verbosity,
|
|
'enable_debug_output': args.enable_debug_output,
|
|
'max_draft_len': args.max_draft_len,
|
|
'speculative_decoding_mode': speculative_decoding_mode,
|
|
'input_timing_cache': args.input_timing_cache,
|
|
'output_timing_cache': args.output_timing_cache,
|
|
'auto_parallel_config': {
|
|
'world_size':
|
|
args.auto_parallel,
|
|
'gpus_per_node':
|
|
args.gpus_per_node,
|
|
'sharded_io_allowlist': [
|
|
'past_key_value_\\d+',
|
|
'present_key_value_\\d*',
|
|
],
|
|
'same_buffer_io': {
|
|
'past_key_value_(\\d+)': 'present_key_value_\\1',
|
|
},
|
|
**cluster_config,
|
|
},
|
|
'dry_run': args.dry_run,
|
|
'visualize_network': args.visualize_network,
|
|
'max_encoder_input_len': args.max_encoder_input_len,
|
|
'weight_streaming': args.weight_streaming,
|
|
},
|
|
plugin_config=plugin_config)
|
|
else:
|
|
build_config = BuildConfig.from_json_file(args.build_config,
|
|
plugin_config=plugin_config)
|
|
|
|
parallel_build(model_config, ckpt_dir, build_config, args.output_dir,
|
|
workers, args.log_level, model_cls, **kwargs)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Total time of building all engines: {t}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|