mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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202bed4574
Signed-off-by: nv-guomingz <137257613+nv-guomingz@users.noreply.github.com> Signed-off-by: Wangshanshan <30051912+dominicshanshan@users.noreply.github.com>
491 lines
20 KiB
Python
491 lines
20 KiB
Python
import argparse
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import json
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import os
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import time
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from pathlib import Path
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from tqdm import tqdm
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from transformers import LlamaConfig
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import tensorrt_llm
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models.eagle.config import EagleConfig
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from tensorrt_llm.models.eagle.model import EagleForCausalLM
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from tensorrt_llm.models.model_weights_loader import ModelWeightsLoader
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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('--meta_ckpt_dir', type=str, default=None)
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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('--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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parser.add_argument('--vocab_size', type=int, default=32000)
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parser.add_argument('--n_positions', type=int, default=2048)
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parser.add_argument('--n_layer', type=int, default=32)
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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', '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('--load_by_shard',
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action='store_true',
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help='Load a pretrained model shard-by-shard.')
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parser.add_argument('--hidden_act', type=str, default='silu')
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parser.add_argument('--rotary_base', type=float, default=10000.0)
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parser.add_argument('--rotary_scaling', nargs=2, type=str, default=None)
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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/AWQ quantization.')
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parser.add_argument("--storage-type",
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"-t",
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type=str,
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default="fp32",
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choices=["fp32", "fp16"])
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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("--load-model-on-cpu", action="store_true")
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parser.add_argument("--convert-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('--eagle_model_dir', type=str, default=None)
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parser.add_argument('--max_draft_len', type=int, default=63)
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parser.add_argument(
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'--num_eagle_layers',
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type=int,
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default=4,
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help=
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'Maximum depth of the EAGLE choices tree, i.e. maximum number of accepted draft tokens.'
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)
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parser.add_argument(
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'--max_non_leaves_per_layer',
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type=int,
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default=10,
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help='Maximum number of non-leaf nodes in the EAGLE choice tree.')
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args = parser.parse_args()
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return args
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def convert_and_save_hf(config, args):
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world_size = args.tp_size * args.pp_size
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tllm_config = EagleConfig.from_dict(config)
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for rank in range(world_size):
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tllm_config.mapping = Mapping(world_size=world_size,
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rank=rank,
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cp_size=1,
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tp_size=args.tp_size,
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pp_size=args.pp_size)
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model = EagleForCausalLM(tllm_config)
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def check_and_update(module, dict):
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if hasattr(module, 'tllm_to_externel_key_dict'):
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module.tllm_to_externel_key_dict.update(dict)
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else:
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module.tllm_to_externel_key_dict = dict
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def copy(tensors):
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if isinstance(tensors, list):
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if None in tensors:
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return tensors
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else:
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return [tensor.clone() for tensor in tensors]
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elif tensors is None:
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return tensors
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else:
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return tensors.clone()
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shared_weight_prefixs = []
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tllm_weights = {}
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customized_dict = {"drafter": ""}
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if args.eagle_model_dir is None:
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# Single checkpoint for ModelOpt
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for idx, eagle_net in enumerate(model.eagle_nets):
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check_and_update(eagle_net.drafter.fc, {"fc": "fc"})
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check_and_update(eagle_net.drafter.vocab_embedding,
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{f"eagle_nets.{idx}": "model"})
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check_and_update(eagle_net.lm_head, {f"eagle_nets.{idx}": ""})
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shared_weight_prefixs.append(f"eagle_nets.{idx}")
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customized_dict[f'eagle_nets.{idx}'] = 'eagle_module'
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loader = ModelWeightsLoader(eagle_model_dir, customized_dict)
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loader.update_key_mapping(model)
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for tllm_key, _ in tqdm(model.named_parameters()):
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if any([
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tllm_key.startswith(prefix)
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for prefix in shared_weight_prefixs
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]):
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tllm_weights.update(loader.load(tllm_key, preprocess=copy))
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else:
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tllm_weights.update(loader.load(tllm_key))
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loader.fill(tllm_weights)
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else:
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# Double checkpoint for HF
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for idx, eagle_net in enumerate(model.eagle_nets):
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check_and_update(eagle_net.drafter.fc, {"fc": "fc"})
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check_and_update(eagle_net.drafter.vocab_embedding,
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{f"eagle_nets.{idx}": ""})
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check_and_update(eagle_net.lm_head, {f"eagle_nets.{idx}": ""})
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shared_weight_prefixs.append(f"eagle_nets.{idx}")
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customized_dict[f'eagle_nets.{idx}'] = ''
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# Load base model
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base_loader = ModelWeightsLoader(args.model_dir)
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base_loader.update_key_mapping(model)
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for tllm_key, _ in tqdm(model.transformer.named_parameters()):
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tllm_weights.update(base_loader.load("transformer." + tllm_key))
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tllm_weights.update(base_loader.load("lm_head.weight"))
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for idx in range(args.num_eagle_layers):
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tllm_weights.update(
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base_loader.load(f"eagle_nets.{idx}.lm_head.weight",
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preprocess=copy))
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# Load eagle model
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eagle_loader = ModelWeightsLoader(eagle_model_dir, customized_dict)
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eagle_loader.update_key_mapping(model)
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for tllm_key, _ in tqdm(model.eagle_nets.named_parameters()):
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if not tllm_key.endswith("lm_head.weight"):
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if any([
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tllm_key.startswith(prefix)
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for prefix in shared_weight_prefixs
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]):
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tllm_weights.update(
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eagle_loader.load("eagle_nets." + tllm_key,
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preprocess=copy))
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else:
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tllm_weights.update(
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eagle_loader.load("eagle_nets." + tllm_key))
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base_loader.fill(tllm_weights)
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model.save_checkpoint(args.output_dir, save_config=(rank == 0))
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if __name__ == '__main__':
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# TODO(qijun): Currently, the convert script depends on a torch op:
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# torch.ops.fastertransformer.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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world_size = args.tp_size * args.pp_size
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assert args.pp_size == 1, "Pipeline parallelism is not supported in EAGLE yet."
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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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hf_config = None
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eagle_model_dir = args.model_dir if args.eagle_model_dir is None else args.eagle_model_dir
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if args.model_dir is not None:
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hf_config = LlamaConfig.from_pretrained(args.model_dir)
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args.model_type = hf_config.model_type
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args.n_head = hf_config.num_attention_heads
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args.inter_size = hf_config.intermediate_size
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args.n_layer = hf_config.num_hidden_layers
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args.n_embd = hf_config.hidden_size
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args.n_kv_head = hf_config.num_key_value_heads
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args.rms_norm_eps = hf_config.rms_norm_eps
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args.vocab_size = hf_config.vocab_size
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args.rotary_scaling = hf_config.rope_scaling
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args.rotary_base = hf_config.rope_theta
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args.n_positions = hf_config.max_position_embeddings
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args.dtype = str(
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hf_config.torch_dtype)[6:] if args.dtype == 'auto' else args.dtype
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if 'head_dim' in hf_config:
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args.head_dim = hf_config.head_dim
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else:
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args.head_dim = args.n_embd // args.n_head
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if 'head_size' in hf_config:
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args.head_size = hf_config.head_size
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else:
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args.head_size = args.head_dim
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if args.eagle_model_dir is None:
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hf_config_eagle = hf_config.eagle
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args.n_head_eagle = hf_config_eagle['num_attention_heads']
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args.inter_size_eagle = hf_config_eagle['intermediate_size']
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args.n_layer_eagle = hf_config_eagle['num_hidden_layers']
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args.n_embd_eagle = hf_config_eagle['hidden_size']
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args.n_kv_head_eagle = hf_config_eagle['num_key_value_heads']
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args.rms_norm_eps_eagle = hf_config_eagle['rms_norm_eps']
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args.n_positions_eagle = hf_config_eagle['max_position_embeddings']
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if 'head_dim' in hf_config_eagle:
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args.head_dim_eagle = hf_config_eagle['head_dim']
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else:
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args.head_dim_eagle = args.n_embd_eagle // args.n_head_eagle
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if 'head_size' in hf_config_eagle:
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args.head_size_eagle = hf_config_eagle['head_size']
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else:
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args.head_size_eagle = args.head_dim_eagle
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else:
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hf_config_eagle = LlamaConfig.from_pretrained(args.eagle_model_dir)
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args.n_head_eagle = hf_config_eagle.num_attention_heads
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args.inter_size_eagle = hf_config_eagle.intermediate_size
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args.n_layer_eagle = hf_config_eagle.num_hidden_layers
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args.n_embd_eagle = hf_config_eagle.hidden_size
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args.n_kv_head_eagle = hf_config_eagle.num_key_value_heads
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args.rms_norm_eps_eagle = hf_config_eagle.rms_norm_eps
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args.n_positions_eagle = hf_config_eagle.max_position_embeddings
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if 'head_dim' in hf_config_eagle:
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args.head_dim_eagle = hf_config_eagle.head_dim
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else:
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args.head_dim_eagle = args.n_embd_eagle // args.n_head_eagle
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if 'head_size' in hf_config_eagle:
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args.head_size_eagle = hf_config_eagle.head_size
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else:
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args.head_size_eagle = args.head_dim_eagle
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elif args.meta_ckpt_dir is not None:
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assert False, "meta ckpt is not supported yet"
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with open(Path(args.meta_ckpt_dir, "params.json")) as fp:
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meta_config: dict = json.load(fp)
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args.n_embd = meta_config["dim"]
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args.n_head = meta_config["n_heads"]
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args.n_layer = meta_config["n_layers"]
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args.n_kv_head = meta_config.get("n_kv_heads", args.n_head)
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if "hidden_dim" in meta_config:
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args.inter_size = meta_config["hidden_dim"]
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else:
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args.multiple_of = meta_config.get("multiple_of", 1)
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n_embd = int(4 * args.n_embd * 2 / 3)
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args.ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1)
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args.inter_size = args.multiple_of * (
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(int(n_embd * args.ffn_dim_multiplier) + args.multiple_of - 1)
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// args.multiple_of)
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args.rms_norm_eps = meta_config["norm_eps"]
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if args.rotary_scaling is not None:
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# assert args.use_gpt_attention_plugin, "RoPE scaling is only supported through GPT attention plugin."
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rotary_scaling = {
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"type": args.rotary_scaling["rope_type"],
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}
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args.rotary_scaling = rotary_scaling
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eagle_net_config = {
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'architecture': "LlamaForCausalLM",
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'dtype': args.dtype,
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'logits_dtype': 'float32',
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'num_hidden_layers': args.n_layer_eagle,
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'num_attention_heads': args.n_head_eagle,
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'hidden_size': args.n_embd_eagle,
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'intermediate_size': args.inter_size_eagle,
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'num_key_value_heads': args.n_kv_head_eagle,
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'vocab_size': args.vocab_size,
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'position_embedding_type': 'rope_gpt_neox',
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'max_position_embeddings': args.n_positions_eagle,
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'hidden_act': args.hidden_act,
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'rotary_base': args.rotary_base,
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'rotary_scaling': args.rotary_scaling,
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'norm_epsilon': args.rms_norm_eps_eagle,
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'quantization': {
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'quant_algo': None,
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'kv_cache_quant_algo': None,
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},
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'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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},
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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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'head_dim': args.head_dim_eagle,
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'head_size': args.head_size_eagle
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}
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config = {
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'architecture': 'EagleForCausalLM',
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'dtype': args.dtype,
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'logits_dtype': 'float32',
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'num_hidden_layers': args.n_layer,
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'num_attention_heads': args.n_head,
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'hidden_size': args.n_embd,
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'intermediate_size': args.inter_size,
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'num_key_value_heads': args.n_kv_head,
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'vocab_size': args.vocab_size,
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'position_embedding_type': 'rope_gpt_neox',
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'max_position_embeddings': args.n_positions,
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'hidden_act': args.hidden_act,
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'rotary_base': args.rotary_base,
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'rotary_scaling': args.rotary_scaling,
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'norm_epsilon': args.rms_norm_eps,
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'quantization': {
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'quant_algo': None,
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'kv_cache_quant_algo': None,
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},
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'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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},
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'use_parallel_embedding': args.use_parallel_embedding,
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'embedding_sharding_dim': args.embedding_sharding_dim,
|
||
'max_draft_len': args.max_draft_len,
|
||
'num_eagle_layers': args.num_eagle_layers,
|
||
'max_non_leaves_per_layer': args.max_non_leaves_per_layer,
|
||
'eagle_net_config': eagle_net_config,
|
||
'head_dim': args.head_dim,
|
||
'head_size': args.head_size
|
||
}
|
||
|
||
assert args.max_draft_len <= 256, "args.max_draft_len > 256 is not supported"
|
||
|
||
if args.use_weight_only:
|
||
if args.weight_only_precision == 'int8':
|
||
config['quantization']['quant_algo'] = QuantAlgo.W8A16
|
||
elif args.weight_only_precision == 'int4':
|
||
config['quantization']['quant_algo'] = QuantAlgo.W4A16
|
||
elif args.smoothquant:
|
||
if args.per_channel:
|
||
if args.per_token:
|
||
config['quantization'][
|
||
'quant_algo'] = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
|
||
else:
|
||
config['quantization'][
|
||
'quant_algo'] = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
|
||
else:
|
||
if args.per_token:
|
||
config['quantization'][
|
||
'quant_algo'] = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
|
||
else:
|
||
config['quantization'][
|
||
'quant_algo'] = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN
|
||
|
||
if args.int8_kv_cache:
|
||
config['quantization']['kv_cache_quant_algo'] = QuantAlgo.INT8
|
||
|
||
if args.weight_only_precision == 'int4_gptq':
|
||
config['quantization'].update({
|
||
"group_size": args.group_size,
|
||
"has_zero_point": True,
|
||
"pre_quant_scale": False,
|
||
'quant_algo': QuantAlgo.W4A16_GPTQ
|
||
})
|
||
|
||
# Update quant config if hf_quant_config.json exists
|
||
quant_config = {}
|
||
try:
|
||
with open(eagle_model_dir + '/' + 'hf_quant_config.json') as f:
|
||
quant_config = json.load(f)
|
||
if "lm_head" in quant_config['quantization']['exclude_modules']:
|
||
quant_config['quantization']['exclude_modules'] += [
|
||
f"eagle_nets.{i}.lm_head"
|
||
for i in range(args.num_eagle_layers)
|
||
]
|
||
config['quantization'].update(quant_config['quantization'])
|
||
config['eagle_net_config']['quantization'].update(
|
||
quant_config['quantization'])
|
||
except IOError:
|
||
pass
|
||
|
||
convert_and_save_hf(config, args)
|
||
|
||
tok = time.time()
|
||
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
||
print(f'Total time of converting checkpoints: {t}')
|