mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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675 lines
27 KiB
Python
675 lines
27 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 math
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import os
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import time
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from pathlib import Path
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from typing import Union
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import onnx
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# isort: off
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import torch
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import torch.multiprocessing as mp
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import tensorrt as trt
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# isort: on
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from onnx import TensorProto, helper
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from transformers import AutoModelForCausalLM, FalconConfig
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from weight import (get_scaling_factors, load_from_awq_falcon,
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load_from_hf_checkpoint, load_from_hf_falcon)
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import tensorrt_llm
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from tensorrt_llm._utils import str_dtype_to_trt
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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.profiler import check_gpt_mem_usage
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from tensorrt_llm.quantization import QuantMode
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MODEL_NAME = 'falcon'
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def trt_dtype_to_onnx(dtype):
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if dtype == trt.float16:
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return TensorProto.DataType.FLOAT16
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elif dtype == trt.float32:
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return TensorProto.DataType.FLOAT
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elif dtype == trt.int32:
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return TensorProto.DataType.INT32
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else:
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raise TypeError("%s is not supported" % dtype)
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def to_onnx(network, path):
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inputs = []
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for i in range(network.num_inputs):
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network_input = network.get_input(i)
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inputs.append(
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helper.make_tensor_value_info(
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network_input.name, trt_dtype_to_onnx(network_input.dtype),
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list(network_input.shape)))
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outputs = []
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for i in range(network.num_outputs):
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network_output = network.get_output(i)
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outputs.append(
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helper.make_tensor_value_info(
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network_output.name, trt_dtype_to_onnx(network_output.dtype),
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list(network_output.shape)))
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nodes = []
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for i in range(network.num_layers):
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layer = network.get_layer(i)
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layer_inputs = []
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for j in range(layer.num_inputs):
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ipt = layer.get_input(j)
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if ipt is not None:
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layer_inputs.append(layer.get_input(j).name)
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layer_outputs = [
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layer.get_output(j).name for j in range(layer.num_outputs)
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]
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nodes.append(
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helper.make_node(str(layer.type),
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name=layer.name,
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inputs=layer_inputs,
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outputs=layer_outputs,
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domain="com.nvidia"))
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onnx_model = helper.make_model(helper.make_graph(nodes,
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'attention',
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inputs,
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outputs,
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initializer=None),
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producer_name='NVIDIA')
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onnx.save(onnx_model, path)
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def get_engine_name(model, dtype, tp_size, pp_size, rank):
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if pp_size == 1:
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return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)
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return '{}_{}_tp{}_pp{}_rank{}.engine'.format(model, dtype, tp_size,
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pp_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(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 load_falcon_config(model_dir: Union[str, Path]) -> FalconConfig:
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""" Helper utility to load FalconConfig.
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A pretrained checkpoint from modeling_RW.py has a different structure
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and is not compatible with `transformers.FalconConfig` and
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`transformers.FalconModel`. We need to manually set the config values.
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"""
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config = FalconConfig.from_pretrained(model_dir)
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if config.model_type not in ['RefinedWebModel', 'RefinedWeb']:
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return config
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if config.model_type == 'RefinedWeb':
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# Case 1. Falcon-40B / Falcon-40B-instruct
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# https://huggingface.co/tiiuae/falcon-40b/blob/main/config.json
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config.num_hidden_layers = config.n_layer
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config.num_attention_heads = config.n_head
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config.num_kv_heads = config.n_head_kv
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config.new_decoder_architecture = True
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elif config.model_type == 'RefinedWebModel':
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# Case 2. Falcon-7B / Falcon-7B-instruct
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# https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
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config.num_hidden_layers = config.n_layer
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config.num_attention_heads = config.n_head
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config.num_kv_heads = 1 if config.multi_query else config.n_head
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config.new_decoder_architecture = False
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else:
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raise ValueError("Shouldn't reach here.")
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config.model_type = 'falcon'
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return config
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size', type=int, default=1)
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parser.add_argument('--tp_size', type=int, default=1)
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parser.add_argument('--pp_size', type=int, default=1)
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parser.add_argument('--model_dir', type=str, default=None)
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parser.add_argument('--quant_ckpt_path', type=str, default=None)
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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', 'bfloat16', 'float16'])
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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='The path of to read timing cache from, will be ignored if the '
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'file does not exist')
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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=65024)
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parser.add_argument('--n_layer', type=int, default=36)
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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=64)
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parser.add_argument('--n_kv_head', type=int, default=None)
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parser.add_argument('--mlp_hidden_size', type=int, default=None)
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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('--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', 'bfloat16', 'float32'])
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parser.add_argument('--bias', action='store_true')
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parser.add_argument('--parallel_attention',
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action='store_true',
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help='Use Falcon parallel attention.')
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parser.add_argument('--new_decoder_architecture',
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action='store_true',
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help='Use the new Falcon decoder architecture. '
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'If enabled, --parallel_attention will be ignored.')
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parser.add_argument(
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'--alibi',
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action='store_true',
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help='Use ALiBi positional encoding. If disabled, RoPE will be used.')
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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('--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', 'bfloat16', 'float32'])
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parser.add_argument(
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'--use_layernorm_plugin',
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nargs='?',
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const=None,
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type=str,
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default=False,
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choices=['float16', 'float32', 'bfloat16'],
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help="Activates layernorm plugin. You can specify the plugin dtype or "
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"leave blank to use the model dtype.")
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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(
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'--multi_block_mode',
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default=False,
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action='store_true',
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help=
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'Split long kv sequence into multiple blocks (applied to generation MHA kernels). \
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It is beneifical when batchxnum_heads cannot fully utilize GPU.'
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)
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parser.add_argument('--visualize', default=False, action='store_true')
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parser.add_argument('--load_by_shard',
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action='store_true',
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help='Load a pretrained model shard-by-shard.')
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parser.add_argument('--enable_debug_output',
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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=str,
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default='engine_outputs',
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help='The path to save the serialized engine files, timing cache '
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'file and model configs')
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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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'--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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# Arguments related to the quantization of the model.
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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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help='Use FP8 Linear layer for Attention QKV/Dense and MLP.')
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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 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='By default, we use dtype for KV cache. fp8_kv_cache chooses int8 '
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'quantization for KV')
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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=128,
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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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'--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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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('--group_size',
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type=int,
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default=128,
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help='Group size used in GPTQ/AWQ quantization.')
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parser.add_argument(
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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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type=str,
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default='int4_awq',
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choices=['int4_awq'],
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help=
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'Define the precision for the weights when using weight-only quantization.'
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'You must also use --use_weight_only for that argument to have an impact.'
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)
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args = parser.parse_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.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. "
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f"Setting to default '{args.use_gpt_attention_plugin}'")
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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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assert (math.log2(args.tokens_per_block).is_integer()
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), "tokens_per_block must be power of 2"
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if args.enable_context_fmha or args.enable_context_fmha_fp32_acc:
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assert (args.tokens_per_block >=
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128), "Context fMHA requires >= 128 tokens per block"
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args.quant_mode = QuantMode(0)
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if args.use_weight_only:
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assert args.enable_fp8 is False, "FP8 and Weight-only cannot be activated simultaneously!"
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if args.weight_only_precision == 'int4_awq':
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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=args.per_group)
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if args.fp8_kv_cache:
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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.model_dir is not None:
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hf_config = load_falcon_config(args.model_dir)
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args.n_embd = hf_config.hidden_size
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args.n_head = hf_config.num_attention_heads
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args.n_kv_head = hf_config.num_kv_heads
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args.n_layer = hf_config.num_hidden_layers
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args.vocab_size = hf_config.vocab_size
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args.alibi = hf_config.alibi
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args.bias = hf_config.bias
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# Falcon variants.
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args.parallel_attention = hf_config.parallel_attn
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args.new_decoder_architecture = hf_config.new_decoder_architecture
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# FalconConfig sets num_kv_heads by num_heads if not provided, even
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# though multi-query attention case. We here manually correct the
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# value of number of K/V heads.
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if not hf_config.new_decoder_architecture and hf_config.multi_query:
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args.n_kv_head = 1
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else:
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args.n_kv_head = args.n_kv_head or args.n_head
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assert (args.n_head % args.n_kv_head) == 0, \
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"MQA/GQA requires the number of heads to be divisible by the number "\
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"of K/V heads."
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assert args.n_kv_head % args.tp_size == 0 \
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or args.tp_size % args.n_kv_head == 0, \
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"MQA/GQA requires either the number of K/V heads to be divisible by "\
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"the tensor parallelism size OR the tensor parallelism size to be "\
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"divisible by the number of K/V heads."
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assert args.pp_size * args.tp_size == args.world_size
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if not args.use_gpt_attention_plugin and not args.alibi:
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args.use_gpt_attention_plugin = args.dtype
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logger.warning(
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f"RoPE does not support without GPT attention plugin. Set by "
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f"use_gpt_attention_plugin={args.dtype}.")
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logger.info(' Build Arguments '.center(100, '='))
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for k, v in vars(args).items():
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logger.info(f' - {k.ljust(30, ".")}: {v}')
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logger.info('=' * 100)
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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: str, rank: int,
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args: argparse.Namespace) -> trt.IHostMemory:
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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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dtype = str_dtype_to_trt(args.dtype)
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# Initialize Module
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mapping = Mapping(
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world_size=args.world_size,
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rank=rank,
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tp_size=args.tp_size,
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pp_size=args.pp_size,
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)
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assert args.n_layer % args.pp_size == 0, \
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f"num_layers {args.n_layer} must be a multiple of pipeline "\
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f"parallelism size {args.pp_size}"
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tensorrt_llm_falcon = tensorrt_llm.models.FalconForCausalLM(
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num_layers=args.n_layer,
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num_heads=args.n_head,
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num_kv_heads=args.n_kv_head,
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hidden_size=args.n_embd,
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vocab_size=args.vocab_size,
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max_position_embeddings=args.n_positions,
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dtype=dtype,
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quant_mode=args.quant_mode,
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bias=args.bias,
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use_alibi=args.alibi,
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logits_dtype=args.logits_dtype,
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mapping=mapping,
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parallel_attention=args.parallel_attention,
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new_decoder_architecture=args.new_decoder_architecture)
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quantize_kwargs = {}
|
|
if args.use_weight_only and args.weight_only_precision == 'int4_awq':
|
|
quantize_kwargs = {
|
|
"group_size": args.group_size,
|
|
"zero": False,
|
|
"pre_quant_scale": True,
|
|
"exclude_modules": [],
|
|
}
|
|
elif args.enable_fp8 or args.fp8_kv_cache:
|
|
logger.info(f'Loading scaling factors from '
|
|
f'{args.quantized_fp8_model_path}')
|
|
quant_scales = get_scaling_factors(args.quantized_fp8_model_path,
|
|
num_layers=args.n_layer,
|
|
quant_mode=args.quant_mode)
|
|
quantize_kwargs = {"quant_scales": quant_scales}
|
|
tensorrt_llm_falcon = quantize_model(tensorrt_llm_falcon, args.quant_mode,
|
|
**quantize_kwargs)
|
|
|
|
if args.quant_ckpt_path:
|
|
load_from_awq_falcon(tensorrt_llm_falcon=tensorrt_llm_falcon,
|
|
quant_ckpt_path=args.quant_ckpt_path,
|
|
mapping=mapping,
|
|
dtype=args.dtype)
|
|
elif args.model_dir is not None:
|
|
logger.info(f'Loading HF Falcon ... from {args.model_dir}')
|
|
tik = time.time()
|
|
if not args.load_by_shard:
|
|
hf_falcon = AutoModelForCausalLM.from_pretrained(
|
|
args.model_dir, trust_remote_code=True)
|
|
load_from_hf_falcon(tensorrt_llm_falcon,
|
|
hf_falcon,
|
|
mapping,
|
|
dtype=args.dtype)
|
|
del hf_falcon
|
|
else:
|
|
load_from_hf_checkpoint(tensorrt_llm_falcon,
|
|
args.model_dir,
|
|
mapping,
|
|
dtype=args.dtype)
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'HF Falcon loaded. Total time: {t}')
|
|
|
|
# Module -> Network
|
|
network = builder.create_network()
|
|
network.trt_network.name = engine_name
|
|
if args.use_gpt_attention_plugin:
|
|
network.plugin_config.set_gpt_attention_plugin(
|
|
dtype=args.use_gpt_attention_plugin)
|
|
if args.use_gemm_plugin:
|
|
if not args.enable_fp8:
|
|
network.plugin_config.set_gemm_plugin(dtype=args.use_gemm_plugin)
|
|
else:
|
|
logger.info(
|
|
"Gemm plugin does not support FP8. Disabled Gemm plugin.")
|
|
if args.use_layernorm_plugin:
|
|
network.plugin_config.set_layernorm_plugin(
|
|
dtype=args.use_layernorm_plugin)
|
|
|
|
# Quantization plugins.
|
|
assert not (args.enable_context_fmha and args.enable_context_fmha_fp32_acc)
|
|
if args.enable_context_fmha:
|
|
network.plugin_config.set_context_fmha(ContextFMHAType.enabled)
|
|
if args.enable_context_fmha_fp32_acc:
|
|
network.plugin_config.set_context_fmha(
|
|
ContextFMHAType.enabled_with_fp32_acc)
|
|
if args.multi_block_mode:
|
|
network.plugin_config.enable_mmha_multi_block_mode()
|
|
|
|
if args.per_group:
|
|
network.plugin_config.set_weight_only_groupwise_quant_matmul_plugin(
|
|
dtype=args.dtype)
|
|
|
|
if args.world_size > 1:
|
|
network.plugin_config.set_nccl_plugin(args.dtype,
|
|
args.use_custom_all_reduce)
|
|
if args.remove_input_padding:
|
|
network.plugin_config.enable_remove_input_padding()
|
|
if args.paged_kv_cache:
|
|
network.plugin_config.enable_paged_kv_cache(args.tokens_per_block)
|
|
with net_guard(network):
|
|
# Prepare
|
|
network.set_named_parameters(tensorrt_llm_falcon.named_parameters())
|
|
inputs = tensorrt_llm_falcon.prepare_inputs(
|
|
max_batch_size=args.max_batch_size,
|
|
max_input_len=args.max_input_len,
|
|
max_new_tokens=args.max_output_len,
|
|
use_cache=True,
|
|
max_beam_width=args.max_beam_width,
|
|
max_num_tokens=args.max_num_tokens)
|
|
tensorrt_llm_falcon(*inputs)
|
|
if args.enable_debug_output:
|
|
# mark intermediate nodes' outputs
|
|
for k, v in tensorrt_llm_falcon.named_network_outputs():
|
|
v = v.trt_tensor
|
|
v.name = k
|
|
network.trt_network.mark_output(v)
|
|
v.dtype = dtype
|
|
if args.visualize:
|
|
model_path = os.path.join(args.output_dir, 'test.onnx')
|
|
to_onnx(network.trt_network, model_path)
|
|
|
|
tensorrt_llm.graph_rewriting.optimize(network)
|
|
|
|
engine = None
|
|
|
|
# Network -> Engine
|
|
engine = builder.build_engine(network, builder_config)
|
|
if rank == 0:
|
|
config_path = os.path.join(args.output_dir, 'config.json')
|
|
builder.save_config(builder_config, config_path)
|
|
|
|
return engine
|
|
|
|
|
|
def build(rank, args):
|
|
torch.cuda.set_device(rank % args.gpus_per_node)
|
|
if not os.path.exists(args.output_dir):
|
|
os.makedirs(args.output_dir)
|
|
|
|
# when doing serializing build, all ranks share one engine
|
|
builder = Builder()
|
|
|
|
cache = None
|
|
for cur_rank in range(args.world_size):
|
|
# skip other ranks if parallel_build is enabled
|
|
if args.parallel_build and cur_rank != rank:
|
|
continue
|
|
# NOTE: 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_or_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.tp_size,
|
|
pipeline_parallel=args.pp_size,
|
|
num_layers=args.n_layer,
|
|
num_heads=args.n_head,
|
|
num_kv_heads=args.n_kv_head,
|
|
hidden_size=args.n_embd,
|
|
vocab_size=args.vocab_size,
|
|
alibi=args.alibi,
|
|
parallel_build=args.parallel_build,
|
|
new_decoder_architecture=args.new_decoder_architecture,
|
|
max_position_embeddings=args.n_positions,
|
|
max_batch_size=args.max_batch_size,
|
|
max_beam_width=args.max_beam_width,
|
|
max_input_len=args.max_input_len,
|
|
max_output_len=args.max_output_len,
|
|
max_num_tokens=args.max_num_tokens,
|
|
quant_mode=args.quant_mode,
|
|
strongly_typed=args.strongly_typed,
|
|
int8=int8_trt_flag,
|
|
opt_level=args.builder_opt)
|
|
engine_name = get_engine_name(MODEL_NAME, args.dtype, args.tp_size,
|
|
args.pp_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}'
|
|
|
|
local_num_kv_heads = (args.n_kv_head + args.world_size -
|
|
1) // args.world_size
|
|
kv_dtype = str_dtype_to_trt(args.dtype)
|
|
if args.quant_mode.has_int8_kv_cache():
|
|
kv_dtype = str_dtype_to_trt('int8')
|
|
elif args.quant_mode.has_fp8_kv_cache():
|
|
kv_dtype = str_dtype_to_trt('fp8')
|
|
check_gpt_mem_usage(
|
|
engine=engine,
|
|
kv_dtype=kv_dtype,
|
|
use_gpt_attention_plugin=args.use_gpt_attention_plugin,
|
|
paged_kv_cache=args.paged_kv_cache,
|
|
max_batch_size=args.max_batch_size,
|
|
max_beam_width=args.max_beam_width,
|
|
max_input_len=args.max_input_len,
|
|
max_output_len=args.max_output_len,
|
|
local_num_kv_heads=local_num_kv_heads,
|
|
head_size=args.n_embd / args.n_head,
|
|
num_layers=args.n_layer)
|
|
|
|
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))
|
|
del engine
|
|
|
|
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."
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_arguments()
|
|
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 '
|
|
f'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}')
|