mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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e06f537e08
* Update TensorRT-LLM --------- Co-authored-by: erenup <ping.nie@pku.edu.cn> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
458 lines
19 KiB
Python
458 lines
19 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 copy
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import os
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import time
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from concurrent.futures import ProcessPoolExecutor, wait
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from importlib.machinery import SourceFileLoader
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from multiprocessing import get_context
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from typing import Dict, Union
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import safetensors
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import torch
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from tensorrt_llm._common import check_max_num_tokens
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from tensorrt_llm._utils import str_dtype_to_torch
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from tensorrt_llm.builder import BuildConfig, Builder
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from tensorrt_llm.graph_rewriting import optimize
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from tensorrt_llm.logger import logger
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from tensorrt_llm.models import MODEL_MAP, PretrainedConfig, PretrainedModel
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from tensorrt_llm.network import net_guard
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from tensorrt_llm.plugin import PluginConfig, add_plugin_argument
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from tensorrt_llm.quantization.mode import QuantMode
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from tensorrt_llm.runtime.engine import Engine, EngineConfig
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from tensorrt_llm.version import __version__
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--checkpoint_dir', type=str, default=None)
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parser.add_argument('--model_config', type=str, default=None)
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parser.add_argument('--build_config', type=str, default=None)
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parser.add_argument('--model_cls_file', type=str, default=None)
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parser.add_argument('--model_cls_name', type=str, default=None)
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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(
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'--profiling_verbosity',
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type=str,
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default='layer_names_only',
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choices=['layer_names_only', 'detailed', 'none'],
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help=
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'The profiling verbosity for the generated TRT engine. Set to detailed can inspect tactic choices and kernel parameters.'
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)
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parser.add_argument(
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'--output_dir',
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type=str,
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default='engine_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('--workers',
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type=int,
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default='1',
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help='The number of workers for building in parallel')
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parser.add_argument('--max_batch_size', type=int, default=1)
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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('--max_num_tokens', type=int, default=None)
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parser.add_argument(
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'--max_prompt_embedding_table_size',
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'--max_multimodal_len',
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type=int,
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default=0,
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help=
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'Setting to a value > 0 enables support for prompt tuning or multimodal input.'
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)
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parser.add_argument(
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'--gather_all_token_logits',
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action='store_true',
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default=False,
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help='Enable both gather_context_logits and gather_generation_logits')
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parser.add_argument('--gather_context_logits',
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action='store_true',
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default=False,
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help='Gather context logits')
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parser.add_argument('--gather_generation_logits',
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action='store_true',
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default=False,
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help='Gather generation logits')
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parser.add_argument('--strongly_typed', action='store_true', default=False)
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parser.add_argument('--builder_opt', type=int, default=None)
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parser.add_argument('--logits_dtype',
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type=str,
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default=None,
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choices=['float16', 'float32'])
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parser.add_argument('--weight_only_precision',
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type=str,
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default=None,
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choices=['int8', 'int4'])
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plugin_config_parser = parser.add_argument_group("plugin_config")
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add_plugin_argument(plugin_config_parser)
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args = parser.parse_args()
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if args.gather_all_token_logits:
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args.gather_context_logits = True
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args.gather_generation_logits = True
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return args
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def build_model(model: PretrainedModel, build_config: BuildConfig) -> Engine:
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builder = Builder()
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builder_config = builder.create_builder_config(
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precision=model.config.dtype,
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int8=model.config.quant_mode.has_act_or_weight_quant()
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or model.config.quant_mode.has_int8_kv_cache(),
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strongly_typed=build_config.strongly_typed,
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opt_level=build_config.builder_opt,
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profiling_verbosity=build_config.profiling_verbosity,
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quant_mode=model.config.quant_mode,
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lora_target_modules=model.config.lora_target_modules if hasattr(
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model.config, 'lora_target_modules') else [],
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hf_modules_to_trtllm_modules=model.config.lora_target_modules
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if hasattr(model.config, 'hf_modules_to_trtllm_modules') else [],
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trtllm_modules_to_hf_modules=model.config.lora_target_modules
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if hasattr(model.config, 'trtllm_modules_to_hf_modules') else [],
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)
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network = builder.create_network()
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network.plugin_config = build_config.plugin_config
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use_weight_only = model.config.quant_mode.is_weight_only()
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per_group = model.config.quant_mode.has_per_group_scaling()
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use_smooth_quant = model.config.quant_mode.has_act_and_weight_quant()
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disable_weight_only_quant_plugin = model.config.disable_weight_only_quant_plugin if hasattr(
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model.config, 'disable_weight_only_quant_plugin') else False
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if use_weight_only and not disable_weight_only_quant_plugin:
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if per_group:
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network.plugin_config.set_plugin(
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"weight_only_groupwise_quant_matmul_plugin", model.config.dtype)
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else:
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network.plugin_config.set_plugin("weight_only_quant_matmul_plugin",
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model.config.dtype)
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if use_smooth_quant:
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network.plugin_config.set_smooth_quant_plugins()
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nccl_plugin = model.config.dtype if model.config.mapping.world_size > 1 else None
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network.plugin_config.set_nccl_plugin(
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nccl_plugin, network.plugin_config.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(model.named_parameters())
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# Forward
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inputs = model.prepare_inputs(
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max_batch_size=build_config.max_batch_size,
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max_input_len=build_config.max_input_len,
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max_seq_len=build_config.max_input_len +
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build_config.max_output_len,
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use_cache=True,
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max_beam_width=build_config.max_beam_width,
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max_num_tokens=build_config.max_num_tokens,
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prompt_embedding_table_size=build_config.
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max_prompt_embedding_table_size,
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max_draft_len=model.config.max_medusa_token_len if hasattr(
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model.config, 'max_medusa_token_len') else 0,
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gather_context_logits=build_config.gather_context_logits,
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gather_generation_logits=build_config.gather_generation_logits,
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lora_target_modules=model.config.lora_target_modules if hasattr(
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model.config, 'lora_target_modules') else [])
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model(**inputs)
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optimize(network)
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# Network -> Engine
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engine = builder.build_engine(network, builder_config)
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engine_config = EngineConfig(model.config, build_config, __version__)
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return Engine(engine_config, engine)
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def build(build_config: BuildConfig,
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rank: int = 0,
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ckpt_dir: str = None,
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model_config: Union[str, PretrainedConfig] = None,
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weights=None,
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model_cls=None,
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**kwargs) -> Engine:
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if ckpt_dir is not None:
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model_config = PretrainedConfig.from_json_file(
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os.path.join(ckpt_dir, 'config.json'))
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else:
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assert model_config is not None
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if isinstance(model_config, PretrainedConfig):
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model_config = model_config
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else:
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model_config = PretrainedConfig.from_json_file(model_config)
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logits_dtype = kwargs.pop('logits_dtype', None)
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if logits_dtype is not None:
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model_config.logits_dtype = logits_dtype
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model_config.use_prompt_tuning = build_config.max_prompt_embedding_table_size > 0
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weight_only_precision = kwargs.pop('weight_only_precision', None)
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if model_config.quant_mode == QuantMode(
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0) and weight_only_precision is not None:
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if weight_only_precision == 'int4':
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model_config.quant_mode = QuantMode.use_weight_only(
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use_int4_weights=True)
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model_config.quant_kwargs['quant_algo'] = 'W4A16'
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else:
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model_config.quant_mode = QuantMode.use_weight_only(
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use_int4_weights=False)
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model_config.quant_kwargs['quant_algo'] = 'W8A16'
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assert rank < model_config.mapping.world_size
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architecture = model_config.architecture
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if model_cls is None:
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if architecture not in MODEL_MAP:
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raise RuntimeError(
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f'Unsupported model architecture: {architecture}')
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model_cls = MODEL_MAP[architecture]
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rank_config = copy.deepcopy(model_config)
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rank_config.set_rank(rank)
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model = model_cls.from_config(rank_config)
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if ckpt_dir is not None:
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weights = {}
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with safetensors.safe_open(os.path.join(ckpt_dir,
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f'rank{rank}.safetensors'),
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framework='pt',
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device='cpu') as f:
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for key in f.keys():
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weights[key] = f.get_tensor(key)
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if weights is not None:
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preprocess_weights(weights, rank_config)
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model.load(weights)
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if model.config.quant_kwargs[
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'quant_algo'] == 'FP8' or model.config.quant_kwargs[
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'kv_cache_quant_algo'] == 'FP8':
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build_config.strongly_typed = True
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return build_model(model, build_config)
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def preprocess_weights(
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weights: Dict[str, torch.Tensor],
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model_config: PretrainedConfig) -> Dict[str, torch.Tensor]:
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quant_algo = model_config.quant_kwargs['quant_algo']
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# INT4_AWQ
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if quant_algo == 'W4A16_AWQ':
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preprocessor = torch.ops.trtllm.preprocess_weights_for_mixed_gemm
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for name, param in weights.items():
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if 'weight' in name and param.dtype == torch.int8:
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weights[name] = preprocessor(param.T.contiguous(),
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torch.quint4x2).view(torch.float16)
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if 'weights_scaling_factor' in name:
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weights[name] = param.T.contiguous().to(
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str_dtype_to_torch(model_config.dtype))
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if 'prequant_scaling_factor' in name:
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weights[name] = param.reshape(1, -1)
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if model_config.mapping.tp_rank > 0:
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if 'attention.dense.bias' in name or 'mlp.proj.bias' in name:
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weights[name] = torch.zeros_like(param)
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# FP8
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elif quant_algo == 'FP8':
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for name, param in weights.items():
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if name.endswith('weight') and param.dtype == torch.int8:
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weights[name] = param.view(torch.float8_e4m3fn)
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# Weight only 4bit
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elif quant_algo == 'W4A16':
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for name in list(weights):
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if any([
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_name in name for _name in [
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'qkv.weight', 'dense.weight', 'fc.weight',
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'proj.weight', 'gate.weight'
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]
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]) and weights[name].dtype != torch.int8:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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weights[name].t().contiguous(), torch.quint4x2)
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weights[name] = processed_torch_weights
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weights[name.replace(
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'.weight', '.per_channel_scale')] = torch_weight_scales
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# Weight only 8bit
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elif quant_algo == 'W8A16':
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for name in list(weights):
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if any([
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_name in name for _name in [
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'qkv.weight', 'dense.weight', 'fc.weight',
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'proj.weight', 'gate.weight'
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]
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]) and weights[name].dtype != torch.int8:
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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weights[name].t().contiguous(), torch.int8)
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weights[name] = processed_torch_weights
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weights[name.replace(
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'.weight', '.per_channel_scale')] = torch_weight_scales
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# If layer_norm bias is None. (For MPT)
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if model_config.architecture == 'MPTForCausalLM':
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update_dict = {}
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for name, param in weights.items():
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if 'input_layernorm.weight' in name and name.replace(
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'weight', 'bias') not in weights:
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update_dict[name.replace('weight',
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'bias')] = torch.zeros_like(param)
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if 'post_layernorm.weight' in name and name.replace(
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'weight', 'bias') not in weights:
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update_dict[name.replace('weight',
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'bias')] = torch.zeros_like(param)
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if 'ln_f.weight' in name and name.replace('weight',
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'bias') not in weights:
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update_dict[name.replace('weight',
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'bias')] = torch.zeros_like(param)
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weights.update(update_dict)
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if model_config.mapping.is_last_pp_rank(
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) and 'lm_head.weight' not in weights:
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weights["lm_head.weight"] = weights[
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"transformer.vocab_embedding.weight"].clone()
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# Parallel block rowlinear should not have duplicate bias.
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if model_config.architecture == 'GPTJForCausalLM':
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if model_config.mapping.tp_rank > 0:
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for name, param in weights.items():
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if 'attention.dense.bias' in name or 'mlp.proj.bias' in name:
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weights[name] = torch.zeros_like(param)
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def build_and_save(rank, gpu_id, ckpt_dir, build_config, output_dir, log_level,
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model_config, model_cls, **kwargs):
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torch.cuda.set_device(gpu_id)
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logger.set_level(log_level)
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engine = build(build_config,
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rank,
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ckpt_dir,
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model_config,
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model_cls=model_cls,
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**kwargs)
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engine.save(output_dir)
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def parallel_build(ckpt_dir_or_model_config: str,
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build_config: BuildConfig,
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output_dir: str,
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workers: int = 1,
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log_level: str = 'info',
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model_cls=None,
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**kwargs):
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ckpt_dir = ckpt_dir_or_model_config
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if ckpt_dir_or_model_config.lower().endswith('.json'):
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model_config = PretrainedConfig.from_json_file(ckpt_dir_or_model_config)
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ckpt_dir = None
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else:
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model_config = PretrainedConfig.from_json_file(
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os.path.join(ckpt_dir_or_model_config, 'config.json'))
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if workers == 1:
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for rank in range(model_config.mapping.world_size):
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build_and_save(rank, rank % workers, ckpt_dir, build_config,
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output_dir, log_level, model_config, model_cls,
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**kwargs)
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else:
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with ProcessPoolExecutor(mp_context=get_context('spawn'),
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max_workers=workers) as p:
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futures = [
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p.submit(build_and_save, rank, rank % workers, ckpt_dir,
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build_config, output_dir, log_level, model_config,
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model_cls, **kwargs)
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for rank in range(model_config.mapping.world_size)
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]
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wait(futures)
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def main():
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args = parse_arguments()
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logger.set_level(args.log_level)
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tik = time.time()
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if not os.path.exists(args.output_dir):
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os.makedirs(args.output_dir)
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model_cls = None
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if args.model_cls_file is not None:
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assert args.model_cls_name is not None
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loader = SourceFileLoader('models', args.model_cls_file)
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mod = loader.load_module()
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model_cls = getattr(mod, args.model_cls_name)
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workers = min(torch.cuda.device_count(), args.workers)
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plugin_config = PluginConfig.from_arguments(args)
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if args.build_config is None:
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args.max_num_tokens = check_max_num_tokens(
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max_num_tokens=args.max_num_tokens,
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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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remove_input_padding=args.remove_input_padding,
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enable_context_fmha=True
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if args.context_fmha == "enable" else False,
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tokens_per_block=args.tokens_per_block)
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build_config = BuildConfig.from_dict(
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{
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'max_input_len': args.max_input_len,
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'max_output_len': args.max_output_len,
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'max_batch_size': args.max_batch_size,
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'max_beam_width': args.max_beam_width,
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'max_num_tokens': args.max_num_tokens,
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'max_prompt_embedding_table_size':
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args.max_prompt_embedding_table_size,
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'gather_context_logits': args.gather_context_logits,
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'gather_generation_logits': args.gather_generation_logits,
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'strongly_typed': args.strongly_typed,
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'builder_opt': args.builder_opt,
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'profiling_verbosity': args.profiling_verbosity,
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},
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plugin_config=plugin_config)
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else:
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build_config = BuildConfig.from_json_file(args.build_config,
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plugin_config=plugin_config)
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source = args.checkpoint_dir if args.checkpoint_dir is not None else args.model_config
|
|
kwargs = {
|
|
'logits_dtype': args.logits_dtype,
|
|
'weight_only_precision': args.weight_only_precision
|
|
}
|
|
parallel_build(source, build_config, args.output_dir, workers,
|
|
args.log_level, model_cls, **kwargs)
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Total time of building all engines: {t}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|