mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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f044eb8d94
* Update TensorRT-LLM --------- Co-authored-by: wangruohui <12756472+wangruohui@users.noreply.github.com>
517 lines
20 KiB
Python
517 lines
20 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import 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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import tensorrt as trt
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import torch
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import torch.multiprocessing as mp
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from transformers import AutoModelForCausalLM
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from weight import (get_scaling_factors, load_from_awq_gpt_j,
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load_from_bin_gpt_j, load_from_hf_gpt_j, parse_config)
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import tensorrt_llm
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from tensorrt_llm.builder import Builder
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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 quantize_model
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from tensorrt_llm.network import net_guard
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from tensorrt_llm.plugin.plugin import ContextFMHAType
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from tensorrt_llm.quantization import QuantMode
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MODEL_NAME = "gptj"
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hf_gpt = None
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awq_gptj_config = None
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def get_engine_name(model, dtype, tp_size, rank):
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return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)
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def serialize_engine(engine, path):
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logger.info(f'Serializing engine to {path}...')
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tik = time.time()
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with open(path, 'wb') as f:
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f.write(bytearray(engine))
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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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logger.info(f'Engine serialized. Total time: {t}')
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def parse_arguments(args):
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size',
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type=int,
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default=1,
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help='world size, only support tensor parallelism now')
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parser.add_argument(
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'--model_dir',
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type=str,
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default=None,
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help='The path to HF GPT-J model / checkpoints to read weights from')
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parser.add_argument(
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'--ft_model_dir',
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type=str,
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default=None,
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help=
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'The path to FT-format (binary) GPT-J model / checkpoints to read weights from'
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)
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parser.add_argument('--dtype',
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type=str,
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default='float16',
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choices=['float16', 'float32'])
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parser.add_argument('--logits_dtype',
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type=str,
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default='float32',
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choices=['float16', 'float32'])
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parser.add_argument(
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'--timing_cache',
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type=str,
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default='model.cache',
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help=
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'The path of to read timing cache from, will be ignored if the file does not exist'
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)
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument('--vocab_size', type=int, default=50401)
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parser.add_argument('--n_layer', type=int, default=28)
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parser.add_argument('--n_positions', type=int, default=2048)
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parser.add_argument('--n_embd', type=int, default=4096)
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parser.add_argument('--n_head', type=int, default=16)
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parser.add_argument('--hidden_act', type=str, default='gelu')
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parser.add_argument('--rotary_dim', type=int, default=64)
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parser.add_argument('--max_batch_size', type=int, default=256)
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parser.add_argument('--max_input_len', type=int, default=200)
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parser.add_argument('--max_output_len', type=int, default=200)
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parser.add_argument('--max_beam_width', type=int, default=1)
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parser.add_argument('--use_gpt_attention_plugin',
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nargs='?',
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const='float16',
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type=str,
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default=False,
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choices=['float16', 'float32'])
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parser.add_argument('--use_gemm_plugin',
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nargs='?',
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const='float16',
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type=str,
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default=False,
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choices=['float16', 'float32'])
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parser.add_argument('--use_layernorm_plugin',
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nargs='?',
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const='float16',
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type=str,
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default=False,
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choices=['float16', 'float32'])
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parser.add_argument('--parallel_build', default=False, action='store_true')
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parser.add_argument('--enable_context_fmha',
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default=False,
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action='store_true')
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parser.add_argument('--enable_context_fmha_fp32_acc',
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default=False,
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action='store_true')
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parser.add_argument('--gpus_per_node', type=int, default=8)
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parser.add_argument(
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'--output_dir',
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type=str,
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default='gpt_outputs',
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help=
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'The path to save the serialized engine files, timing cache file and model configs'
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)
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parser.add_argument('--remove_input_padding',
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default=False,
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action='store_true')
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parser.add_argument('--enable_fp8', default=False, action='store_true')
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parser.add_argument(
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'--quantized_fp8_model_path',
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type=str,
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default=None,
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help='Path of a quantized model checkpoint that in .npz format')
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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 fp8 quantization for KV'
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)
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parser.add_argument(
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'--int8_kv_cache',
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default=False,
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action="store_true",
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help=
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'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
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)
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parser.add_argument(
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'--use_inflight_batching',
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action="store_true",
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default=False,
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help="Activates inflight batching mode of gptAttentionPlugin.")
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parser.add_argument(
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'--paged_kv_cache',
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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 contiguous KV cache. By setting this flag you enable paged KV cache'
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)
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parser.add_argument('--tokens_per_block',
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type=int,
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default=64,
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help='Number of tokens per block in paged KV cache')
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parser.add_argument(
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'--max_num_tokens',
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type=int,
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default=None,
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help='Define the max number of tokens supported by the engine')
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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(
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'--use_weight_only',
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default=False,
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action="store_true",
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help='Quantize weights for the various GEMMs to INT4/INT8.'
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'See --weight_only_precision to set the precision')
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parser.add_argument(
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'--weight_only_precision',
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const='int8',
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type=str,
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nargs='?',
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default='int8',
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choices=['int8', 'int4'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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parser.add_argument(
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'--strongly_typed',
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default=False,
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action="store_true",
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help=
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'This option is introduced with trt 9.1.0.1+ and will reduce the building time significantly for fp8.'
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)
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args = parser.parse_args(args)
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logger.set_level(args.log_level)
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if not args.remove_input_padding:
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if args.use_gpt_attention_plugin:
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logger.warning(
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f"It is recommended to specify --remove_input_padding when using GPT attention plugin"
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)
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if args.model_dir is not None:
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global hf_gpt
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if args.use_weight_only and args.weight_only_precision == 'int4' and args.per_group:
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logger.info(f'Loading AWQ GPTJ model from {args.model_dir}...')
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global awq_gptj_config
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with open(args.model_dir + "/config.json",
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encoding='utf-8') as config_file:
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awq_gptj_config = json.load(config_file)
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args.n_embd = awq_gptj_config['n_embd']
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args.n_head = awq_gptj_config['n_head']
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args.n_layer = awq_gptj_config['n_layer']
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args.n_positions = awq_gptj_config['n_positions']
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args.vocab_size = awq_gptj_config['vocab_size']
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if args.vocab_size % 64 != 0:
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args.vocab_size = int(
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(awq_gptj_config['vocab_size'] + 63) / 64) * 64
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print(
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"vocab_size is {}, to use awq we pad it to {}.".format(
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awq_gptj_config['vocab_size'], args.vocab_size))
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hf_gpt = torch.load(args.model_dir + "/gptj_quantized.pth")
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else:
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logger.info(f'Loading HF GPTJ model from {args.model_dir}...')
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hf_gpt = AutoModelForCausalLM.from_pretrained(args.model_dir)
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args.n_embd = hf_gpt.config.n_embd
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args.n_head = hf_gpt.config.n_head
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args.n_layer = hf_gpt.config.n_layer
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args.n_positions = hf_gpt.config.n_positions
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args.vocab_size = hf_gpt.config.vocab_size
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elif args.ft_model_dir is not None:
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logger.info(f"Setting model configuration from {args.ft_model_dir}.")
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n_embd, n_head, n_layer, n_positions, vocab_size, _, hidden_act, rotary_pct, bias, inter_size, multi_query_mode, dtype, prompt_num_tasks, prompt_max_vocab_size = parse_config(
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Path(args.ft_model_dir) / "config.ini")
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args.n_embd = n_embd
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args.n_head = n_head
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args.n_layer = n_layer
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args.n_positions = n_positions
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args.vocab_size = vocab_size
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args.hidden_act = hidden_act
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args.rotary_pct = rotary_pct
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args.bias = bias
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args.dtype = dtype
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args.inter_size = inter_size
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args.multi_query_mode = multi_query_mode
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if args.use_weight_only:
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if args.per_group:
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assert args.weight_only_precision == 'int4', "We only support per-group quantization (AWQ/GPT-Q) with INT4 precision"
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args.quant_mode = QuantMode.from_description(
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quantize_weights=True,
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quantize_activations=False,
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per_token=False,
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per_channel=False,
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per_group=True,
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use_int4_weights=True)
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else:
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args.quant_mode = QuantMode.use_weight_only(
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args.weight_only_precision == 'int4')
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else:
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args.quant_mode = QuantMode(0)
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if args.int8_kv_cache:
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args.quant_mode = args.quant_mode.set_int8_kv_cache()
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elif args.fp8_kv_cache:
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assert (
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args.use_gpt_attention_plugin
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), "You have to use GPT attention plugin when fp8 KV cache is set"
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args.quant_mode = args.quant_mode.set_fp8_kv_cache()
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if args.enable_fp8:
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args.quant_mode = args.quant_mode.set_fp8_qdq()
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if args.use_inflight_batching:
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if not args.use_gpt_attention_plugin:
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args.use_gpt_attention_plugin = 'float16'
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logger.info(
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f"Using GPT attention plugin for inflight batching mode. Setting to default '{args.use_gpt_attention_plugin}'"
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)
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if not args.remove_input_padding:
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args.remove_input_padding = True
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logger.info(
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"Using remove input padding for inflight batching mode.")
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if not args.paged_kv_cache:
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args.paged_kv_cache = True
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logger.info("Using paged KV cache for inflight batching mode.")
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if args.max_num_tokens is not None:
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assert args.enable_context_fmha
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return args
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def build_rank_engine(builder: Builder,
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builder_config: tensorrt_llm.builder.BuilderConfig,
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engine_name, rank, args):
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'''
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@brief: Build the engine on the given rank.
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@param rank: The rank to build the engine.
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@param args: The cmd line arguments.
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@return: The built engine.
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'''
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kv_dtype = trt.float16 if args.dtype == 'float16' else trt.float32
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mapping = Mapping(world_size=args.world_size,
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rank=rank,
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tp_size=args.world_size) # TP only
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# Initialize Module
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tensorrt_llm_gpt = tensorrt_llm.models.GPTJForCausalLM(
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num_layers=args.n_layer,
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num_heads=args.n_head,
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hidden_size=args.n_embd,
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vocab_size=args.vocab_size,
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hidden_act=args.hidden_act,
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max_position_embeddings=args.n_positions,
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rotary_dim=args.rotary_dim,
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dtype=kv_dtype,
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logits_dtype=args.logits_dtype,
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mapping=mapping,
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quant_mode=args.quant_mode)
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quantize_kwargs = {}
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if args.use_weight_only and args.per_group:
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assert args.weight_only_precision == 'int4'
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quantize_kwargs = {
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"group_size": 128,
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"zero": False,
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"pre_quant_scale": True,
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"exclude_modules": [],
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}
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tensorrt_llm_gpt = quantize_model(tensorrt_llm_gpt, args.quant_mode,
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**quantize_kwargs)
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if args.model_dir is not None:
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assert hf_gpt is not None, f'Could not load weights from hf_gpt model as it is not loaded yet.'
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if args.enable_fp8:
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gptj_scaling_factors = get_scaling_factors(
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args.quantized_fp8_model_path, args.n_layer, args.quant_mode)
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else:
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gptj_scaling_factors = None
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if args.use_weight_only and args.weight_only_precision == 'int4' and args.per_group:
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load_from_awq_gpt_j(tensorrt_llm_gpt,
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awq_gpt_j=hf_gpt,
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ft_model_dir=args.ft_model_dir,
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config=awq_gptj_config,
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mapping=mapping,
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fp16=(args.dtype == 'float16'))
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else:
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load_from_hf_gpt_j(tensorrt_llm_gpt,
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hf_gpt,
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fp16=(args.dtype == 'float16'),
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scaling_factors=gptj_scaling_factors)
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elif args.ft_model_dir is not None:
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load_from_bin_gpt_j(tensorrt_llm_gpt, args.ft_model_dir, rank,
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args.world_size, args.dtype)
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# Module -> Network
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network = builder.create_network()
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network.trt_network.name = engine_name
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if args.use_gpt_attention_plugin:
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network.plugin_config.set_gpt_attention_plugin(
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dtype=args.use_gpt_attention_plugin)
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if args.use_gemm_plugin:
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if not args.enable_fp8:
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network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin)
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else:
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logger.info(
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"Gemm plugin does not support FP8. Disabled Gemm plugin.")
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if args.use_layernorm_plugin:
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network.plugin_config.set_layernorm_plugin(
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dtype=args.use_layernorm_plugin)
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assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc)
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if args.enable_context_fmha:
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network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
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if args.enable_context_fmha_fp32_acc:
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network.plugin_config.set_context_fmha(
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ContextFMHAType.enabled_with_fp32_acc)
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if args.use_weight_only:
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if args.per_group:
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network.plugin_config.set_weight_only_groupwise_quant_matmul_plugin(
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dtype='float16')
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else:
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network.plugin_config.set_weight_only_quant_matmul_plugin(
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dtype='float16')
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if args.world_size > 1:
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network.plugin_config.set_nccl_plugin(args.dtype)
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if args.remove_input_padding:
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network.plugin_config.enable_remove_input_padding()
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if args.paged_kv_cache:
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network.plugin_config.enable_paged_kv_cache(args.tokens_per_block)
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with net_guard(network):
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# Prepare
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network.set_named_parameters(tensorrt_llm_gpt.named_parameters())
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# Forward
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inputs = tensorrt_llm_gpt.prepare_inputs(
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args.max_batch_size,
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args.max_input_len,
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args.max_output_len,
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True,
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args.max_beam_width,
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max_num_tokens=args.max_num_tokens)
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tensorrt_llm_gpt(*inputs)
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tensorrt_llm.graph_rewriting.optimize(network)
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engine = None
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# Network -> Engine
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engine = builder.build_engine(network, builder_config)
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if rank == 0:
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config_path = os.path.join(args.output_dir, 'config.json')
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builder.save_config(builder_config, config_path)
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tensorrt_llm.tools.cleanup(network, tensorrt_llm_gpt)
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return engine
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def build(rank, args):
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torch.cuda.set_device(rank % args.gpus_per_node)
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tensorrt_llm.logger.set_level(args.log_level)
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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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# when doing serializing build, all ranks share one engine
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builder = Builder()
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cache = None
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for cur_rank in range(args.world_size):
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# skip other ranks if parallel_build is enabled
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if args.parallel_build and cur_rank != rank:
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continue
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|
# NOTE(nkorobov): when only int8 kv cache is used together with paged kv cache no int8 tensors are exposed to TRT
|
|
int8_trt_flag = args.quant_mode.has_act_and_weight_quant() or (
|
|
not args.paged_kv_cache and args.quant_mode.has_int8_kv_cache())
|
|
|
|
builder_config = builder.create_builder_config(
|
|
name=MODEL_NAME,
|
|
precision=args.dtype,
|
|
timing_cache=args.timing_cache if cache is None else cache,
|
|
tensor_parallel=args.world_size, # TP only
|
|
parallel_build=args.parallel_build,
|
|
num_layers=args.n_layer,
|
|
num_heads=args.n_head,
|
|
hidden_size=args.n_embd,
|
|
vocab_size=args.vocab_size,
|
|
hidden_act=args.hidden_act,
|
|
max_position_embeddings=args.n_positions,
|
|
max_batch_size=args.max_batch_size,
|
|
max_input_len=args.max_input_len,
|
|
max_output_len=args.max_output_len,
|
|
max_num_tokens=args.max_num_tokens,
|
|
fp8=args.enable_fp8,
|
|
int8=int8_trt_flag,
|
|
quant_mode=args.quant_mode,
|
|
strongly_typed=args.strongly_typed)
|
|
|
|
engine_name = get_engine_name(MODEL_NAME, args.dtype, args.world_size,
|
|
cur_rank)
|
|
engine = build_rank_engine(builder, builder_config, engine_name,
|
|
cur_rank, args)
|
|
assert engine is not None, f'Failed to build engine for rank {cur_rank}'
|
|
|
|
if cur_rank == 0:
|
|
# Use in-memory timing cache for multiple builder passes.
|
|
if not args.parallel_build:
|
|
cache = builder_config.trt_builder_config.get_timing_cache()
|
|
|
|
serialize_engine(engine, os.path.join(args.output_dir, engine_name))
|
|
|
|
if rank == 0:
|
|
ok = builder.save_timing_cache(
|
|
builder_config, os.path.join(args.output_dir, "model.cache"))
|
|
assert ok, "Failed to save timing cache."
|
|
|
|
|
|
def run_build(args=None):
|
|
args = parse_arguments(args)
|
|
tik = time.time()
|
|
if args.parallel_build and args.world_size > 1 and \
|
|
torch.cuda.device_count() >= args.world_size:
|
|
logger.warning(
|
|
f'Parallelly build TensorRT engines. Please make sure that all of the {args.world_size} GPUs are totally free.'
|
|
)
|
|
mp.spawn(build, nprocs=args.world_size, args=(args, ))
|
|
else:
|
|
args.parallel_build = False
|
|
logger.info('Serially build TensorRT engines.')
|
|
build(0, args)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Total time of building all {args.world_size} engines: {t}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_build()
|