mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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d8b408e6dc
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
519 lines
19 KiB
Python
519 lines
19 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 time
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from pathlib import Path
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from typing import List
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import torch
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import torch.multiprocessing as mp
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import transformers
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from weight import load_from_hf
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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 (ChatGLM6BHeadModel, smooth_quantize,
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weight_only_quantize)
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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 = "chatglm-6b"
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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 find_engines(dir: Path,
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model_name: str = "*",
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dtype: str = "*",
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tp_size: str = "*",
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rank: str = "*") -> List[Path]:
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template = f"{model_name}_{dtype}_tp{tp_size}_rank{rank}.engine"
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return list(dir.glob(template))
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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('--model_dir', type=str, default="pyTorchModel")
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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=['float32', 'float16', 'bfloat16'])
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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(
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'--log_level',
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type=str,
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default='verbose',
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choices=['verbose', 'info', 'warning', 'error', 'internal_error'])
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parser.add_argument('--max_batch_size', type=int, default=8)
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parser.add_argument('--max_input_len', type=int, default=1024)
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parser.add_argument('--max_output_len', type=int, default=1024)
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parser.add_argument('--max_beam_width', type=int, default=1)
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parser.add_argument(
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'--use_gpt_attention_plugin',
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nargs='?',
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const='float16',
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default='float16',
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choices=['float32', 'float16', 'bfloat16', False],
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help=
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"Activates attention plugin. You can specify the plugin dtype or leave blank to use the model dtype."
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)
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parser.add_argument(
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'--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='float16',
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choices=['float32', 'float16', 'bfloat16', False],
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help=
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"Activates GEMM plugin. You can specify the plugin dtype or leave blank to use the model dtype."
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)
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parser.add_argument(
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'--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='float16',
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choices=['float32', 'float16', 'bfloat16', False],
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help=
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"Activates layernorm plugin. You can specify the plugin dtype or leave blank to use the model dtype.",
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)
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parser.add_argument('--gather_all_token_logits',
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action='store_true',
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default=False)
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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('--builder_opt', type=int, default=None)
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parser.add_argument(
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'--output_dir',
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type=Path,
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default='trtModel',
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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(
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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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# Arguments related to the quantization of the model.
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parser.add_argument(
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'--use_smooth_quant',
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default=False,
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action="store_true",
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help=
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'Use the SmoothQuant method to quantize activations and weights for the various GEMMs.'
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'See --per_channel and --per_token for finer-grained quantization options.'
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)
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parser.add_argument(
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'--use_weight_only',
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default=False,
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action="store_true",
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help='Quantize weights for the various GEMMs to INT4/INT8.'
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'See --weight_only_precision to set the precision')
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parser.add_argument(
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'--weight_only_precision',
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const='int8',
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type=str,
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nargs='?',
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default='int8',
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choices=['int8', 'int4'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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parser.add_argument(
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'--per_channel',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor for the GEMM\'s result. '
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'per_channel instead uses a different static scaling factor for each channel. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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'--per_token',
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default=False,
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action="store_true",
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help=
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'By default, we use a single static scaling factor to scale activations in the int8 range. '
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'per_token chooses at run time, and for each token, a custom scaling factor. '
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'The latter is usually more accurate, but a little slower.')
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parser.add_argument(
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'--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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'--random_seed',
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type=int,
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default=None,
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help=
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'Seed to use when initializing the random number generator for torch.')
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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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'--enable_fp8',
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default=False,
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action='store_true',
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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 fp8 quantization for KV'
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)
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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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'--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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parser.add_argument(
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'--use_custom_all_reduce',
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action='store_true',
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help=
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'Activates latency-optimized algorithm for all-reduce instead of NCCL.')
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args = parser.parse_args(args)
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logger.set_level(args.log_level)
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args.apply_query_key_layer_scaling = False # always False in TRT-LLM
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args.bias = True
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args.hidden_act = 'gelu'
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args.multi_block_mode = False
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args.multi_query_mode = False # always False in ChatGLM-6B
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plugins_args = [
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'use_gpt_attention_plugin',
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'use_gemm_plugin',
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'use_layernorm_plugin',
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]
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for plugin_arg in plugins_args:
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if getattr(args, plugin_arg) is None:
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logger.info(
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f"{plugin_arg} set, without specifying a value. Using {args.dtype} automatically."
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)
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setattr(args, plugin_arg, args.dtype)
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assert args.model_dir is not None
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with open(Path(args.model_dir) / "config.json", "r") as f:
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js = json.loads(f.read())
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assert js["_name_or_path"] == "THUDM/" + MODEL_NAME
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assert args.max_input_len < js["max_sequence_length"]
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args.eos_token_id = js["eos_token_id"]
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args.ffn_hidden_size = js["inner_hidden_size"]
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args.gmask_token_id = js["gmask_token_id"]
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args.hidden_size = js["hidden_size"]
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args.layernorm_epsilon = js["layernorm_epsilon"]
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args.mask_token_id = js["mask_token_id"]
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args.max_seq_length = min(args.max_input_len + args.max_output_len,
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js["max_sequence_length"])
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args.num_heads = js["num_attention_heads"]
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args.num_kv_heads = js["num_attention_heads"]
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args.num_layers = js["num_layers"]
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args.pad_token_id = js["pad_token_id"]
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args.use_cache = js["use_cache"]
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args.vocab_size = js["vocab_size"]
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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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assert not (
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args.use_smooth_quant and args.use_weight_only
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), "You cannot enable both SmoothQuant and INT8 weight-only together."
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if args.use_smooth_quant:
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args.quant_mode = QuantMode.use_smooth_quant(args.per_token,
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args.per_channel)
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elif args.use_weight_only:
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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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if args.fp8_kv_cache:
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assert (
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args.use_gpt_attention_plugin or args.use_inflight_batching
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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.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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# Initialize Module
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args.mapping = Mapping(
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world_size=args.world_size,
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rank=rank,
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tp_size=args.world_size,
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)
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trtllm_model = ChatGLM6BHeadModel(args=args)
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if args.use_smooth_quant:
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trtllm_model = smooth_quantize(trtllm_model, args.quant_mode)
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elif args.use_weight_only:
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trtllm_model = weight_only_quantize(trtllm_model, args.quant_mode)
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if args.model_dir is not None:
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hf_model = transformers.AutoModel.from_pretrained(
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args.model_dir, trust_remote_code=True).cpu()
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trtllm_model = load_from_hf(
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trtllm_model,
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hf_model,
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mapping=args.mapping,
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dtype=args.dtype,
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max_seq_length=args.max_seq_length,
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)
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del hf_model
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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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network.plugin_config.set_gemm_plugin(dtype=args.use_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.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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# Quantization plugins.
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if args.use_smooth_quant:
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network.plugin_config.set_smooth_quant_gemm_plugin(dtype=args.dtype)
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network.plugin_config.set_layernorm_quantization_plugin(
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dtype=args.dtype)
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network.plugin_config.set_quantize_tensor_plugin()
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network.plugin_config.set_quantize_per_token_plugin()
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elif args.use_weight_only:
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network.plugin_config.set_weight_only_quant_matmul_plugin(
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dtype=args.dtype)
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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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args.use_custom_all_reduce)
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with net_guard(network):
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# Prepare
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network.set_named_parameters(trtllm_model.named_parameters())
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# Forward
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inputs = trtllm_model.prepare_inputs(
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max_batch_size=args.max_batch_size,
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max_input_len=args.max_input_len,
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max_new_tokens=args.max_output_len,
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use_cache=True,
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max_beam_width=args.max_beam_width,
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)
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trtllm_model(*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 = args.output_dir / 'config.json'
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builder.save_config(builder_config, config_path)
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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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args.output_dir.mkdir(parents=True, exist_ok=True)
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timing_cache_file = args.output_dir / "model.cache"
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timing_cache = timing_cache_file
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builder = Builder()
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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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builder_config = builder.create_builder_config(
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precision=args.dtype,
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timing_cache=timing_cache,
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tensor_parallel=args.world_size,
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int8=(args.quant_mode.has_act_or_weight_quant()
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or args.quant_mode.has_int8_kv_cache()),
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fp8=args.enable_fp8,
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strongly_typed=args.strongly_typed,
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opt_level=args.builder_opt,
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apply_query_key_layer_scaling=args.apply_query_key_layer_scaling,
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eos_token_id=args.eos_token_id,
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gather_all_token_logits=args.gather_all_token_logits,
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hidden_act=args.hidden_act,
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hidden_size=args.hidden_size,
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max_batch_size=args.max_batch_size,
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max_input_len=args.max_input_len,
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max_num_tokens=args.max_output_len + args.max_input_len,
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max_output_len=args.max_output_len,
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max_position_embeddings=args.max_seq_length,
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multi_query_mode=args.multi_query_mode,
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name=MODEL_NAME,
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num_heads=args.num_heads,
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num_kv_heads=args.num_heads,
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num_layers=args.num_layers,
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pad_token_id=args.pad_token_id,
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paged_kv_cache=args.paged_kv_cache,
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parallel_build=args.parallel_build,
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quant_mode=int(args.quant_mode),
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remove_input_padding=args.remove_input_padding,
|
|
vocab_size=args.vocab_size,
|
|
)
|
|
|
|
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:
|
|
timing_cache = builder_config.trt_builder_config.get_timing_cache(
|
|
)
|
|
|
|
serialize_engine(engine, args.output_dir / engine_name)
|
|
|
|
if rank == 0:
|
|
ok = builder.save_timing_cache(builder_config, timing_cache_file)
|
|
assert ok, "Failed to save timing cache."
|
|
|
|
|
|
def run_build(args=None):
|
|
args = parse_arguments(args)
|
|
|
|
if args.random_seed is not None:
|
|
torch.manual_seed(args.random_seed)
|
|
|
|
logger.set_level(args.log_level)
|
|
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()
|