mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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1ac0566a93
* fix for cp > kvHeadNum Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com> * fix for None kv_head_num Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com> --------- Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com>
592 lines
23 KiB
Python
592 lines
23 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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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.layers import MoeConfig
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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 LLaMAForCausalLM
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from tensorrt_llm.models.convert_utils import infer_dtype
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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('--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(
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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('--cp_size',
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type=int,
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default=1,
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help='N-way context parallelism size')
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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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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('--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('--n_head', type=int, default=32)
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parser.add_argument('--n_kv_head', type=int, default=None)
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parser.add_argument('--n_embd', type=int, default=4096)
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parser.add_argument('--inter_size', type=int, default=11008)
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parser.add_argument('--multiple_of', type=int, default=None)
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parser.add_argument('--ffn_dim_multiplier', type=float, default=None)
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parser.add_argument('--rms_norm_eps', type=float, default=1e-06)
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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', 'int8_gptq', 'int4_gptq', 'int4_awq'],
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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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"--calib_size",
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type=int,
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default=512,
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help=
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"Number of samples for calibration. Set to -1 to use the whole dataset.",
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)
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parser.add_argument(
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"--calib_max_seq_length",
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type=int,
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default=512,
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help="Max Sequence length 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('--use_qserve',
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default=False,
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action="store_true",
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help='Use QServe W4A8 quantization.')
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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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'--fp8_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. fp8_kv_cache chooses int8 quantization for KV'
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)
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parser.add_argument(
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'--quant_ckpt_path',
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type=str,
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default=None,
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help='Path of a quantized model checkpoint in .safetensors format')
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parser.add_argument("--use_fp8",
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action="store_true",
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default=False,
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help="Enable FP8 per-tensor quantization")
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parser.add_argument("--use_nvfp4",
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action="store_true",
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default=False,
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help="Enable NVFP4 quantization")
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parser.add_argument("--use_fp8_rowwise",
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action="store_true",
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default=False,
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help="Enable Fp8 per-token per-channel quantization")
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parser.add_argument(
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"--use_meta_fp8_rowwise_recipe",
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action="store_true",
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default=False,
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help=
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"Enable Meta's LLaMA 3.1 recipe for Fp8 per-token per-channel quantization. "
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"This skips quantization for the first and last Transformer layers and all the Attention layers. "
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"This option is effective only if use_fp8_rowwise is enabled.")
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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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'If --use_qserve is enabled, this option also decides whether we use per-group or per-channel version of QServe'
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)
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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('--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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) # AWQ is only supported by quantize.py script
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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_num_experts',
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default=0,
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type=int,
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help='Specify the number of experts to use for MOE layers')
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parser.add_argument(
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'--moe_top_k',
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default=0,
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type=int,
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help=
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'Specify the top_k value to use for MOE layers. Default to 1 if --moe_num_experts is set'
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)
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parser.add_argument(
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'--moe_renorm_mode',
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default=MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE,
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type=int,
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help=
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'Controls renormalization after gate logits. Check layers/moe.py for accepted values',
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)
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parser.add_argument(
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'--save_config_only',
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action="store_true",
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default=False,
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help=
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'Only save the model config w/o read and converting weights, be careful, this is for debug only'
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)
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parser.add_argument(
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'--remove_duplicated_kv_heads',
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action="store_true",
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default=False,
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help=
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'Only used to remove the duplicated kv heads of llama-3.1 405B HF model.'
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)
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parser.add_argument('--log_level', type=str, default='info')
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args = parser.parse_args()
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# changing the default to be consistent as the cli help said.
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if args.moe_num_experts and args.moe_top_k == 0:
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args.moe_top_k = 1
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return args
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def precision_to_config(precision, group_size, quant_config) -> QuantConfig:
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'''update config dict for weight-only quantization
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'''
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quant_config = QuantConfig()
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precision_to_algo = {
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'int8': QuantAlgo.W8A16,
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'int4': QuantAlgo.W4A16,
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'int8_gptq': QuantAlgo.W8A16_GPTQ,
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'int4_gptq': QuantAlgo.W4A16_GPTQ,
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'int4_awq': QuantAlgo.W4A16_AWQ
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}
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quant_config.quant_algo = precision_to_algo.get(precision)
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if precision in {'int4_gptq', 'int8_gptq'}:
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quant_config.group_size = 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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elif precision == 'int4_awq':
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quant_config.group_size = group_size
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quant_config.has_zero_point = False
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quant_config.pre_quant_scale = True
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return quant_config
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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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quant_config = precision_to_config(args.weight_only_precision,
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args.group_size, quant_config)
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elif args.use_fp8:
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quant_config.quant_algo = QuantAlgo.FP8
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elif args.use_nvfp4:
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quant_config.quant_algo = QuantAlgo.NVFP4
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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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elif args.use_fp8_rowwise:
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quant_config.quant_algo = QuantAlgo.FP8_PER_CHANNEL_PER_TOKEN
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# this will be overwritten if specified in the hf config.
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quant_config.clamp_val = [-1200.0, 1200.0]
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elif args.use_qserve:
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quant_config.quant_algo = QuantAlgo.W4A8_QSERVE_PER_GROUP if args.per_group else QuantAlgo.W4A8_QSERVE_PER_CHANNEL
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quant_config.use_meta_recipe = args.use_meta_fp8_rowwise_recipe
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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.fp8_kv_cache:
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quant_config.kv_cache_quant_algo = QuantAlgo.FP8
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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(
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'quant_method') == 'fbgemm_fp8':
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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.FP8_PER_CHANNEL_PER_TOKEN
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activation_scale_ub = hf_config_dict['quantization_config'].get(
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'activation_scale_ub', 1200.0)
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quant_config.clamp_val = [-activation_scale_ub, activation_scale_ub]
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elif 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 convert_and_save_meta(args, rank):
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mapping = Mapping(world_size=args.tp_size * args.pp_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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rank=rank)
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llama = LLaMAForCausalLM.from_meta_ckpt(
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args.meta_ckpt_dir,
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args.dtype,
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quant_config=args_to_quant_config(args),
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mapping=mapping,
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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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llama.config.mapping.cp_size = args.cp_size
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llama.config.mapping.attn_tp_size = -1
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llama.config.mapping.attn_cp_size = -1
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llama.config.mapping.world_size *= args.cp_size
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llama.save_checkpoint(args.output_dir, save_config=(rank == 0))
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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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'remove_duplicated_kv_heads': args.remove_duplicated_kv_heads,
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'quant_ckpt_path': args.quant_ckpt_path,
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'load_model_on_cpu': args.load_model_on_cpu,
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}
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def from_cli_args(args):
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n_kv_head = args.n_kv_head if args.n_kv_head is not None else args.n_head
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config = {
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'architecture': "LlamaForCausalLM",
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'dtype': infer_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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'ffn_dim_multiplier': args.ffn_dim_multiplier,
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'multiple_of': args.multiple_of,
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'num_key_value_heads': 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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'norm_epsilon': args.rms_norm_eps,
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'moe': {
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'num_experts': args.moe_num_experts,
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'top_k': args.moe_top_k,
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'normalization_mode': args.moe_renorm_mode,
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},
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'mapping': {
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'world_size': args.tp_size * args.pp_size * args.cp_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,
|
||
'cp_size': args.cp_size,
|
||
},
|
||
'quantization': args_to_quant_config(args).to_dict()
|
||
}
|
||
config.update(args_to_build_options(args))
|
||
return config
|
||
|
||
|
||
def convert_and_save_hf(args):
|
||
model_dir = args.model_dir
|
||
load_by_shard = args.load_by_shard
|
||
world_size = args.tp_size * args.pp_size
|
||
# Need to convert the cli args to the kay-value pairs and override them in the generate config dict.
|
||
# Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now,
|
||
# before the refactor is done.
|
||
override_fields = {}
|
||
override_fields.update(args_to_build_options(args))
|
||
|
||
quant_config = args_to_quant_config(args)
|
||
|
||
try:
|
||
hf_config = AutoConfig.from_pretrained(model_dir,
|
||
trust_remote_code=True)
|
||
quant_config, override_fields = update_quant_config_from_hf(
|
||
quant_config, hf_config, override_fields)
|
||
except:
|
||
# llava_llama needs its own defined config.
|
||
logger.warning("AutoConfig cannot load the huggingface config.")
|
||
|
||
if args.smoothquant is not None or args.int8_kv_cache:
|
||
assert not args.load_by_shard, "When using quantization, TRT-LLM needs to load the whole HF model, thus load by shard not supported"
|
||
mapping = Mapping(world_size=world_size,
|
||
tp_size=args.tp_size,
|
||
pp_size=args.pp_size,
|
||
moe_tp_size=args.moe_tp_size,
|
||
moe_ep_size=args.moe_ep_size,
|
||
cp_size=args.cp_size)
|
||
# TODO: support moe quantization for tp + ep
|
||
LLaMAForCausalLM.quantize(
|
||
args.model_dir,
|
||
args.output_dir,
|
||
dtype=args.dtype,
|
||
mapping=mapping,
|
||
quant_config=quant_config,
|
||
device='cpu' if args.load_model_on_cpu else 'cuda',
|
||
calib_dataset=args.calib_dataset,
|
||
calib_batches=args.calib_size,
|
||
calib_max_seq_length=args.calib_max_seq_length,
|
||
**override_fields)
|
||
else:
|
||
# When not loading by shard, preload one complete model and then slice per rank weights from this
|
||
# this saves the disk reloading time
|
||
def convert_and_save_rank(args, rank):
|
||
mapping = Mapping(world_size=world_size,
|
||
rank=rank,
|
||
tp_size=args.tp_size,
|
||
pp_size=args.pp_size,
|
||
moe_tp_size=args.moe_tp_size,
|
||
moe_ep_size=args.moe_ep_size)
|
||
tik = time.time()
|
||
llama = LLaMAForCausalLM.from_hugging_face(
|
||
model_dir,
|
||
args.dtype,
|
||
mapping=mapping,
|
||
quant_config=quant_config,
|
||
load_by_shard=load_by_shard,
|
||
**override_fields,
|
||
)
|
||
print(
|
||
f'Total time of reading and converting: {time.time()-tik:.3f} s'
|
||
)
|
||
llama.config.mapping.cp_size = args.cp_size
|
||
llama.config.mapping.attn_tp_size = -1
|
||
llama.config.mapping.attn_cp_size = -1
|
||
llama.config.mapping.world_size *= args.cp_size
|
||
tik = time.time()
|
||
llama.save_checkpoint(args.output_dir, save_config=(rank == 0))
|
||
del llama
|
||
print(f'Total time of saving checkpoint: {time.time()-tik:.3f} s')
|
||
|
||
execute(args.workers, [convert_and_save_rank] * world_size, args)
|
||
release_gc()
|
||
|
||
|
||
def execute(workers, func, args):
|
||
if workers == 1:
|
||
for rank, f in enumerate(func):
|
||
f(args, rank)
|
||
else:
|
||
with ThreadPoolExecutor(max_workers=workers) as p:
|
||
futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
|
||
exceptions = []
|
||
for future in as_completed(futures):
|
||
try:
|
||
future.result()
|
||
except Exception as e:
|
||
traceback.print_exc()
|
||
exceptions.append(e)
|
||
assert len(
|
||
exceptions
|
||
) == 0, "Checkpoint conversion failed, please check error log."
|
||
|
||
|
||
def main():
|
||
print(tensorrt_llm.__version__)
|
||
args = parse_arguments()
|
||
logger.set_level(args.log_level)
|
||
|
||
world_size = args.tp_size * args.pp_size * args.cp_size
|
||
if (args.moe_tp_size == -1 and args.moe_ep_size == -1):
|
||
# moe default to tp-only
|
||
args.moe_tp_size = args.tp_size
|
||
args.moe_ep_size = 1
|
||
elif (args.moe_tp_size == -1):
|
||
args.moe_tp_size = args.tp_size // args.moe_ep_size
|
||
elif (args.moe_ep_size == -1):
|
||
args.moe_ep_size = args.tp_size // args.moe_tp_size
|
||
assert (args.moe_tp_size * args.moe_ep_size == args.tp_size
|
||
), "moe_tp_size * moe_ep_size must equal to tp_size"
|
||
tik = time.time()
|
||
|
||
if not os.path.exists(args.output_dir):
|
||
os.makedirs(args.output_dir)
|
||
|
||
if (args.model_dir is None
|
||
and args.meta_ckpt_dir is None): # generate fake config.json
|
||
config = from_cli_args(args)
|
||
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
|
||
json.dump(config, f, indent=4)
|
||
elif args.meta_ckpt_dir is not None:
|
||
assert args.model_dir is None, "Shall not specify both meta checkpoint dir and hugging face dir"
|
||
execute(args.workers, [convert_and_save_meta] * world_size, args)
|
||
else: # all other paths from hf model
|
||
assert args.model_dir is not None
|
||
assert (
|
||
args.quant_ckpt_path is not None and
|
||
(args.weight_only_precision in {'int4_gptq', 'int8_gptq'}
|
||
or args.use_qserve)
|
||
) or args.quant_ckpt_path is None, "only gptq weights or qserve need this option"
|
||
convert_and_save_hf(args)
|
||
|
||
tok = time.time()
|
||
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
||
print(f'Total time of converting checkpoints: {t}')
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|