mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
342 lines
13 KiB
Python
342 lines
13 KiB
Python
import argparse
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import json
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import os
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import shutil
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import time
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import traceback
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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import tensorrt_llm
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from tensorrt_llm._utils import release_gc
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import GPTForCausalLM
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from tensorrt_llm.models.gpt.convert import (UnpackedNemoCheckpointDir,
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copy_tokenizer_files, load_hf_gpt,
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unpack_nemo_ckpt,
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update_tokenizer_paths)
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from tensorrt_llm.models.modeling_utils import QuantConfig
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from tensorrt_llm.quantization import QuantAlgo
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_dir', type=str, default=None)
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parser.add_argument('--nemo_ckpt_path', type=str, default=None)
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parser.add_argument(
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'--gpt_variant',
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default=None,
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choices=[
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None, 'gpt2', 'santacoder', 'starcoder', 'starcoder2', 'persimmon',
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'kosmos-2', 'nemotron'
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],
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help=
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"By default the script will try to infer the gpt_variant from model_dir. "
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"Or users may overwrite gpt_variant by explicitly passing the variant.")
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parser.add_argument('--tp_size',
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type=int,
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default=1,
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help='N-way tensor parallelism size')
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parser.add_argument('--pp_size',
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type=int,
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default=1,
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help='N-way pipeline parallelism size')
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parser.add_argument(
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'--dtype',
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type=str,
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default='auto',
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choices=['auto', 'float16', 'bfloat16', 'float32'],
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help=
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"The data type for the model weights and activations if not quantized. "
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"If 'auto', the data type is automatically inferred from the source model; "
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"however, if the source dtype is float32, it is converted to float16.")
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parser.add_argument("--load_model_on_cpu", action="store_true")
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parser.add_argument(
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'--use_parallel_embedding',
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action="store_true",
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default=False,
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help=
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'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled'
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)
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parser.add_argument(
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'--embedding_sharding_dim',
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type=int,
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default=0,
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choices=[0, 1],
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help=
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'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). '
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'To shard it along hidden dimension, set embedding_sharding_dim=1'
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'Note: embedding sharing is only enabled when embedding_sharding_dim = 0'
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)
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parser.add_argument(
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'--use_weight_only',
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default=False,
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action="store_true",
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help='Quantize weights for the various GEMMs to INT4/INT8.'
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'See --weight_only_precision to set the precision')
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parser.add_argument(
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'--weight_only_precision',
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const='int8',
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type=str,
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nargs='?',
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default='int8',
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choices=['int8', 'int4'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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parser.add_argument(
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'--calib_dataset',
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type=str,
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default='lambada',
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help=
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"The huggingface dataset name or the local directory of the dataset for calibration."
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)
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parser.add_argument(
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'--int8_kv_cache',
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default=False,
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action="store_true",
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help=
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'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
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)
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parser.add_argument(
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'--per_channel',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor for the GEMM\'s result. '
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'per_channel instead uses a different static scaling factor for each channel. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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'--per_token',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor to scale activations in the int8 range. '
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'per_token chooses at run time, and for each token, a custom scaling factor. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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"--smoothquant",
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"-sq",
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type=float,
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default=None,
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help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
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" to Smoothquant the model, and output int8 weights."
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" A good first try is 0.5. Must be in [0, 1]")
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parser.add_argument("--dataset_cache_dir",
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type=str,
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default=None,
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help="cache dir to load the hugging face dataset")
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parser.add_argument('--output_dir',
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type=str,
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default='tllm_checkpoint',
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help='The path to save the TensorRT-LLM checkpoint')
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parser.add_argument(
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'--workers',
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type=int,
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default=1,
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help='The number of workers for converting checkpoint in parallel')
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument(
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'--nemo_rename_key',
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type=str,
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nargs='+',
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default=[],
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help=
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"Change a layer name when loading a NeMo checkpoint. Should follow <old_name_pattern>:<new_name_pattern>"
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)
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args = parser.parse_args()
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tensorrt_llm.logger.set_level(args.log_level)
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return args
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def args_to_quant_config(args: argparse.Namespace) -> QuantConfig:
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'''return config dict with quantization info based on the command line args
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'''
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quant_config = QuantConfig()
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if args.use_weight_only:
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if args.weight_only_precision == 'int8':
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quant_config.quant_algo = QuantAlgo.W8A16
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elif args.weight_only_precision == 'int4':
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quant_config.quant_algo = QuantAlgo.W4A16
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elif args.smoothquant:
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quant_config.smoothquant_val = args.smoothquant
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if args.per_channel:
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if args.per_token:
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quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
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else:
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quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
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else:
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if args.per_token:
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quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
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else:
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quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN
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if args.int8_kv_cache:
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quant_config.kv_cache_quant_algo = QuantAlgo.INT8
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# Check if model ckpt is pre-quantized to fp8.
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hf_quant_config_path = f"{args.model_dir}/hf_quant_config.json"
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if os.path.exists(hf_quant_config_path):
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with open(hf_quant_config_path, 'r') as f:
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hf_quant_config = json.load(f)
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if hf_quant_config.get("producer", {}).get("name") == "modelopt":
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modelopt_quant_config = hf_quant_config.get("quantization", {})
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if modelopt_quant_config.get("quant_algo", None) == QuantAlgo.FP8:
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quant_config.quant_algo = QuantAlgo.FP8
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if modelopt_quant_config.get("kv_cache_quant_algo",
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None) == QuantAlgo.FP8:
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quant_config.kv_cache_quant_algo = QuantAlgo.FP8
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return quant_config
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def convert_and_save_hf(args):
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model_dir = args.model_dir
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load_model_on_cpu = args.load_model_on_cpu
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world_size = args.tp_size * args.pp_size
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override_fields = {
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'use_parallel_embedding': args.use_parallel_embedding,
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'embedding_sharding_dim': args.embedding_sharding_dim,
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'gpt_variant': args.gpt_variant,
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}
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quant_config = args_to_quant_config(args)
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is_prequantized_to_fp8 = quant_config.quant_algo == QuantAlgo.FP8
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if is_prequantized_to_fp8:
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override_fields.update({'is_prequantized_to_fp8': True})
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if args.smoothquant is not None or args.int8_kv_cache:
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mapping = Mapping(world_size=world_size,
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tp_size=args.tp_size,
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pp_size=args.pp_size)
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GPTForCausalLM.quantize(
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args.model_dir,
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args.output_dir,
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dtype=args.dtype,
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mapping=mapping,
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quant_config=quant_config,
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device='cpu' if args.load_model_on_cpu else 'cuda',
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calib_dataset=args.calib_dataset,
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**override_fields)
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else:
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# Defer weight loading if checkpoint is prequantized to fp8.
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if is_prequantized_to_fp8:
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hf_model_or_dir = model_dir
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else:
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hf_model_or_dir = load_hf_gpt(model_dir, load_model_on_cpu)
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def convert_and_save_rank(args, rank):
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mapping = Mapping(world_size=world_size,
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rank=rank,
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tp_size=args.tp_size,
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pp_size=args.pp_size)
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model = GPTForCausalLM.from_hugging_face(hf_model_or_dir,
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args.dtype,
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mapping=mapping,
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quant_config=quant_config,
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**override_fields)
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model.save_checkpoint(args.output_dir, save_config=(rank == 0))
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del model
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execute(args.workers, [convert_and_save_rank] * world_size, args)
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release_gc()
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def execute(workers, func, args):
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if workers == 1:
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for rank, f in enumerate(func):
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f(args, rank)
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else:
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with ThreadPoolExecutor(max_workers=workers) as p:
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futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
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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(
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exceptions
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) == 0, "Checkpoint conversion failed, please check error log."
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def convert_and_save_nemo(args):
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world_size = args.tp_size * args.pp_size
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quant_config = args_to_quant_config(args)
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override_fields = {
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'use_parallel_embedding': True,
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'embedding_sharding_dim': 0,
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}
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nemo_ckpt_dir = os.path.join(args.output_dir, "unpacked")
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nemo_ckpt_dir = unpack_nemo_ckpt(args.nemo_ckpt_path, nemo_ckpt_dir)
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def convert_and_save_rank(args, rank):
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mapping = Mapping(world_size=world_size,
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rank=rank,
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tp_size=args.tp_size,
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pp_size=args.pp_size)
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model = GPTForCausalLM.from_nemo(
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nemo_ckpt_dir,
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dtype=args.dtype,
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mapping=mapping,
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quant_config=quant_config,
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load_model_on_cpu=args.load_model_on_cpu,
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nemo_rename_key=args.nemo_rename_key,
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**override_fields)
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model.save_checkpoint(args.output_dir, save_config=(rank == 0))
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del model
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execute(args.workers, [convert_and_save_rank] * world_size, args)
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release_gc()
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# Copy tokenizer files
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unpacked_checkpoints_dir = UnpackedNemoCheckpointDir(
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nemo_ckpt_dir, load_checkpoints_to_cpu=args.load_model_on_cpu)
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nemo_model_config = unpacked_checkpoints_dir.model_config
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tokenizer_config = update_tokenizer_paths(
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nemo_model_config["tokenizer"],
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unpacked_checkpoints_dir.get_all_tokenizer_file_paths())
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copy_tokenizer_files(tokenizer_config, Path(args.output_dir))
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# Clean up unpacked nemo checkpoint
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shutil.rmtree(nemo_ckpt_dir)
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def main():
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# TODO(qijun): Currently, the convert script depends on a torch op:
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# torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix,
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# which is included in tensorrt_llm Python package. Otherwise, the convert
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# script does not need to import tensorrt_llm. Will remove it after reimplementing
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# the op with PyTorch.
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print(tensorrt_llm.__version__)
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args = parse_arguments()
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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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if args.model_dir is not None:
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convert_and_save_hf(args)
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elif args.nemo_ckpt_path is not None:
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convert_and_save_nemo(args)
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else:
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raise NotImplementedError("No source model path specified!")
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tok = time.time()
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t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
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print(f'Total time of converting checkpoints: {t}')
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if __name__ == '__main__':
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main()
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