mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-04-05 04:08:58 +08:00
336 lines
13 KiB
Python
336 lines
13 KiB
Python
import argparse
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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 ThreadPoolExecutor, as_completed
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from transformers import AutoConfig
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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.logger import logger
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import QWenForCausalLM
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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, required=True)
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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(
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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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'--disable_weight_only_quant_plugin',
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default=False,
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action="store_true",
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help=
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'By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
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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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'--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', 'int4_gptq'],
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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='ccdv/cnn_dailymail',
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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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"--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(
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'--per_channel',
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action="store_true",
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default=False,
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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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action="store_true",
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default=False,
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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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'--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_group',
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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 weights in the int4 range. '
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'per_group chooses at run time, and for each group, a custom scaling factor. '
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'The flag is built for GPTQ/AWQ quantization.')
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parser.add_argument('--group_size',
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type=int,
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default=128,
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help='Group size used in GPTQ quantization.')
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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('--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(
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'--moe_tp_size',
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type=int,
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default=-1,
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help=
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'N-way tensor parallelism size for MOE, default is tp_size, which will do tp-only for MoE'
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)
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parser.add_argument(
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'--moe_ep_size',
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type=int,
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default=-1,
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help=
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'N-way expert parallelism size for MOE, default is 1, which will do tp-only for MoE'
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)
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args = parser.parse_args()
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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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if args.weight_only_precision == 'int4_gptq':
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quant_config.group_size = args.group_size
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quant_config.has_zero_point = True
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quant_config.pre_quant_scale = False
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quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
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return quant_config
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def update_quant_config_from_hf(quant_config, hf_config,
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override_fields) -> tuple[QuantConfig, dict]:
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hf_config_dict = hf_config.to_dict()
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if hf_config_dict.get('quantization_config'):
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# update the quant_algo, and clamp_val.
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if hf_config_dict['quantization_config'].get('quant_method') == 'awq':
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logger.info(
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"Load quantization configs from huggingface model_config.")
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quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
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quant_config.group_size = hf_config_dict['quantization_config'].get(
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'group_size', 128)
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quant_config.has_zero_point = hf_config_dict[
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'quantization_config'].get('zero_point', False)
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override_fields.update({"use_autoawq": True})
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elif hf_config_dict['quantization_config'].get(
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'quant_method') == 'gptq':
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logger.info(
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"Load quantization configs from huggingface model_config.")
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desc_act = hf_config_dict['quantization_config'].get(
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'desc_act', False)
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if desc_act:
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raise ValueError("GPTQ with desc_act=True is not implemented!")
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quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
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quant_config.group_size = hf_config_dict['quantization_config'].get(
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'group_size', 128)
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quant_config.has_zero_point = hf_config_dict[
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'quantization_config'].get('sym', False)
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return quant_config, override_fields
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def args_to_build_options(args):
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return {
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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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'disable_weight_only_quant_plugin':
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args.disable_weight_only_quant_plugin
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}
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def convert_and_save_hf(args):
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model_dir = args.model_dir
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world_size = args.tp_size * args.pp_size
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# Need to convert the cli args to the kay-value pairs and override them in the generate config dict.
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# Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now,
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# before the refactor is done.
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override_fields = {}
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override_fields.update(args_to_build_options(args))
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quant_config = args_to_quant_config(args)
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try:
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hf_config = AutoConfig.from_pretrained(model_dir,
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trust_remote_code=True)
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quant_config, override_fields = update_quant_config_from_hf(
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quant_config, hf_config, override_fields)
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except:
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logger.warning("AutoConfig cannot load the huggingface config.")
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if args.smoothquant is not None or args.int8_kv_cache:
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mapping = Mapping(
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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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moe_tp_size=args.moe_tp_size,
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moe_ep_size=args.moe_ep_size,
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)
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QWenForCausalLM.quantize(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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calib_dataset=args.calib_dataset,
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**override_fields)
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else:
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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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moe_tp_size=args.moe_tp_size,
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moe_ep_size=args.moe_ep_size)
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qwen = QWenForCausalLM.from_hugging_face(model_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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qwen.save_checkpoint(args.output_dir, save_config=(rank == 0))
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del qwen
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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 main():
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print(tensorrt_llm.__version__)
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args = parse_arguments()
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if (args.moe_tp_size == -1 and args.moe_ep_size == -1):
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# moe default to tp-only
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args.moe_tp_size = args.tp_size
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args.moe_ep_size = 1
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elif (args.moe_tp_size == -1):
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args.moe_tp_size = args.tp_size // args.moe_ep_size
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elif (args.moe_ep_size == -1):
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args.moe_ep_size = args.tp_size // args.moe_tp_size
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assert (args.moe_tp_size * args.moe_ep_size == args.tp_size
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), "moe_tp_size * moe_ep_size must equal to tp_size"
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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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assert args.model_dir is not None
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convert_and_save_hf(args)
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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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