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https://github.com/NVIDIA/TensorRT-LLM.git
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728cc0044b
* Update TensorRT-LLM --------- Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
201 lines
8.5 KiB
Python
201 lines
8.5 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 json
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import os
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import subprocess
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import time
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from collections import OrderedDict
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import torch
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import tensorrt_llm
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from tensorrt_llm.logger import logger
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from tensorrt_llm.quantization import QuantMode
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def get_compute_cap():
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output = subprocess.check_output(
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['nvidia-smi', "--query-gpu=compute_cap", "--format=csv"])
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_, csv_value, *_ = output.splitlines()
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return str(int(float(csv_value) * 10))
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def get_csv_filename(model, dtype, tp_size, mode, **kwargs):
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sm = get_compute_cap()
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if len(kwargs) == 0:
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kw_pairs = ""
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else:
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kw_pairs = "_" + "_".join([str(k) + str(v) for k, v in kwargs.items()])
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return f'{model}_{dtype}_tp{tp_size}_{mode}{kw_pairs}_sm{sm}.csv'
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def get_engine_name(model, dtype, tp_size, rank):
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return '{}_{}_tp{}_rank{}.engine'.format(model, dtype, tp_size, rank)
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def serialize_engine(engine, path):
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logger.info(f'Serializing engine to {path}...')
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tik = time.time()
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with open(path, 'wb') as f:
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# engine object is already complies with python buffer protocol, no need to
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# convert it to bytearray before write, converting to bytearray consumes lots of memory
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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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class BaseBenchmark(object):
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def __init__(self,
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engine_dir,
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model_name,
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dtype,
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rank,
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world_size,
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serial_build: bool = False):
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self.engine_dir = engine_dir
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self.model_name = model_name
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self.dtype = dtype
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self.runtime_rank = rank
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self.world_size = world_size
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self.engine_model_name = model_name
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self.quant_mode = QuantMode(0)
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self.enable_fp8 = False
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if engine_dir is not None:
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# Read config from engine directory
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config_path = os.path.join(engine_dir, 'config.json')
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with open(config_path, 'r') as f:
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self.config = json.load(f)
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# Sanity checks
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if 'pretrained_config' in self.config: # new build api branch
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config_dtype = self.config['pretrained_config']['dtype']
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assert dtype == config_dtype, f"Engine dtype ({config_dtype}) != Runtime dtype ({dtype})"
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world_size = self.config['pretrained_config']['mapping'][
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'world_size']
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assert world_size == self.world_size, \
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(f'Engine world size ({world_size}) != Runtime world size ({self.world_size})')
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# Load config into self
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for key, value in self.config['pretrained_config'].items():
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setattr(self, key, value)
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self.quant_mode = QuantMode.from_quant_algo(
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quant_algo=self.quantization['quant_algo'],
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kv_cache_quant_algo=self.quantization['kv_cache_quant_algo']
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)
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self.enable_fp8 = self.quant_mode.has_fp8_qdq()
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self.fp8_kv_cache = self.quant_mode.has_fp8_kv_cache()
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for key, value in self.config['build_config'].items():
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setattr(self, key, value)
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for key, value in self.plugin_config.items():
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if "plugin" in key:
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key = "use_" + key
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setattr(self, key, value)
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self.engine_name = f"rank{self.runtime_rank}.engine"
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self.num_kv_heads = self.num_key_value_heads
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self.num_layers = self.num_hidden_layers
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self.num_heads = self.num_attention_heads
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else:
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# Read config from engine directory
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config_path = os.path.join(engine_dir, 'config.json')
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with open(config_path, 'r') as f:
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self.config = json.load(f)
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# Sanity checks
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config_dtype = self.config['builder_config']['precision']
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assert dtype == config_dtype, f"Engine dtype ({config_dtype}) != Runtime dtype ({dtype})"
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world_size = self.config['builder_config']['tensor_parallel']
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assert world_size == self.world_size, \
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(f'Engine world size ({world_size}) != Runtime world size ({self.world_size})')
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# Load config into self
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for key, value in self.config['builder_config'].items():
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if key == "quant_mode":
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self.quant_mode = QuantMode(value)
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elif key in "name":
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self.engine_model_name = value
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else:
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setattr(self, key, value)
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self.enable_fp8 = self.quant_mode.has_fp8_qdq()
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self.fp8_kv_cache = self.quant_mode.has_fp8_kv_cache()
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for key, value in self.config['plugin_config'].items():
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# Same effect as self.use_foo_plugin = config.json["foo_plugin"]
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if "plugin" in key:
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key = "use_" + key
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setattr(self, key, value)
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self.engine_name = get_engine_name(self.engine_model_name,
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self.dtype, self.world_size,
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self.runtime_rank)
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else:
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self.engine_name = get_engine_name(self.engine_model_name,
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self.dtype, self.world_size,
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self.runtime_rank)
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self.runtime_mapping = tensorrt_llm.Mapping(world_size=self.world_size,
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rank=self.runtime_rank,
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tp_size=self.world_size)
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if not serial_build:
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torch.cuda.set_device(self.runtime_rank %
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self.runtime_mapping.gpus_per_node)
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self.csv_filename = "" # lazy init
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def get_report_dict(self, benchmark_profiler=None):
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report_fields = [
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"model_name", "world_size", "num_heads", "num_kv_heads",
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"num_layers", "hidden_size", "vocab_size", "precision",
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"batch_size", "input_length", "output_length", "gpu_peak_mem(gb)",
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"build_time(s)", "tokens_per_sec", "percentile95(ms)",
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"percentile99(ms)", "latency(ms)", "compute_cap"
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]
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report_dict = OrderedDict.fromkeys(report_fields)
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report_dict["model_name"] = self.model_name
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report_dict["world_size"] = self.world_size
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report_dict["precision"] = self.dtype
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report_dict["quantization"] = str(self.quant_mode)
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report_dict["compute_cap"] = "sm" + get_compute_cap()
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return report_dict
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def get_csv_filename(self):
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if len(self.csv_filename) == 0:
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self.csv_filename = get_csv_filename(self.model_name,
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self.dtype,
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self.world_size,
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self.mode,
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fp8linear=int(self.enable_fp8))
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return self.csv_filename
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def print_report_header(self, csv=False, benchmark_profiler=None):
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if csv and self.runtime_rank == 0:
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report_dict = self.get_report_dict(benchmark_profiler)
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line = ",".join(report_dict.keys())
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print(line)
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with open(self.get_csv_filename(), "a") as file:
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file.write(line + "\n")
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def get_config(self):
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raise NotImplementedError
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def prepare_inputs(self, config):
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raise NotImplementedError
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def run(self, inputs, config, benchmark_profiler=None):
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raise NotImplementedError
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def report(self, config, latency):
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raise NotImplementedError
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