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import asyncio
import json
import math
import os
import threading
import time
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Iterable, List, Optional
import numpy as np
import torch
from tensorrt_llm import logger
from .._utils import release_gc
from ..profiler import device_memory_info, host_memory_info
from .llm import LLM, SamplingParams
from .utils import is_directory_empty, print_colored
@dataclass
class PerfItem:
start: float
end: float
num_out_tokens: int = 0
@property
def lantency(self) -> float:
return self.end - self.start
@dataclass
class Report:
num_samples: int
total_latency: float
avg_latency: float
seq_throughput: float
token_throughput: float
ave_tokens_per_sample: float
memory_usage_samples: "MemoryContinuousMonitorThread.RecordList" = field(
default_factory=list)
def display(self):
print_colored("Performance Report:\n", color="bold_green")
print(f"num_samples: {self.num_samples}")
print(f"total_latency: {self.total_latency:.2f}")
print(f"avg_latency: {self.avg_latency:.2f}")
print(f"seq_throughput: {self.seq_throughput:.2f}")
print(f"token_throughput: {self.token_throughput:.2f}")
print(f"average tokens per sample: {self.ave_tokens_per_sample:.2f}")
if self.memory_usage_samples:
print("Memory Usage:\n")
min_record, max_record, average_record = self.memory_usage_samples.get_min(
), self.memory_usage_samples.get_max(
), self.memory_usage_samples.get_average()
print(
f" host memory usage: (min: {min_record[0]}, max: {max_record[0]}, average: {average_record[0]})"
)
print(
f" gpu memory usage: (min: {min_record[1]}, max: {max_record[1]}, average: {average_record[1]})"
)
print_colored("__________________________________\n", color="green")
def save_json(self, path: Path, **kwargs):
with open(path, 'w') as f:
data = self.__dict__.copy()
data.update(kwargs)
data["memory_usage_samples"] = [
asdict(record) for record in self.memory_usage_samples
]
json.dump(data, f)
@dataclass
class Sample:
input_ids: List[int]
output_len: int
class MemoryContinuousMonitorThread(threading.Thread):
''' Monitor the host memory and GPU memory usage. '''
@dataclass
class Record:
time: float
host_memory: float # GiB
gpu_memory: List[float]
def to_dict(self):
return dict(time=self.time,
host_memory=self.host_memory,
gpu_memory=self.gpu_memory)
class RecordList(list):
def __init__(self, *args, **kwargs):
super(MemoryContinuousMonitorThread.RecordList,
self).__init__(*args, **kwargs)
def get_min(self):
return self._get_memory_usage('min')
def get_max(self):
return self._get_memory_usage('max')
def get_average(self):
return self._get_memory_usage('average')
def _get_memory_usage(self, op: str):
host_memory = [record.host_memory for record in self]
gpu_memory = [record.gpu_memory for record in self]
ops = dict(min=np.min, max=np.max, average=np.mean)
theop = ops[op]
return theop(host_memory), [theop(gpu) for gpu in zip(*gpu_memory)]
def __init__(self, sampling_interval: float = 1):
super(MemoryContinuousMonitorThread, self).__init__()
self.sampling_interval = sampling_interval
self._stop_event = threading.Event()
self.memory_samples = MemoryContinuousMonitorThread.RecordList()
def run(self):
while not self._stop_event.is_set():
record = self.monitor()
logger.info(f'record: {record}')
self.memory_samples.append(record)
time.sleep(self.sampling_interval)
def monitor(self) -> "MemoryContinuousMonitorThread.Record":
return self.Record(time.time(), get_host_memory_usage(),
list(get_gpu_memory_usage()))
def stop(self):
self._stop_event.set()
def get_host_memory_usage() -> float:
return host_memory_info(os.getpid())[0] / 1024**3 # GiB
def get_gpu_memory_usage() -> Iterable[float]:
for device in range(torch.cuda.device_count()):
yield device_memory_info(device)[0] / 1024**3
class LLMPerfEvaluator:
@classmethod
def create(cls,
model: str,
samples_path: Path,
num_samples: int = -1,
warmup: int = 100,
batch_size: int = -1,
engine_cache_path: Optional[Path] = None,
memory_monitor_interval: Optional[int] = None,
**kwargs) -> 'LLMPerfEvaluator':
'''
Args:
model: The model name or a local path to the model directory.
samples_path: path to the input data samples
num_samples: number of the heading samples to run, if set to -1, all samples will be used
warmup: number of samples for warmup runs
batch_size: batch size for the runs, if left default, the batch size will be the same as the number of samples
engine_cache_path: path to the engine file, if provided, the engine will save the built engine to the path and reuse it for the next runs
memory_monitor_interval: the interval to monitor the host and GPU memory usage, if set to None, the memory monitor will be disabled
kwargs: the additional arguments are for the LLM constructor
'''
from_cache = False
if engine_cache_path and Path.exists(
engine_cache_path
) and not is_directory_empty(engine_cache_path):
print(f"Loading engine from {engine_cache_path}\n")
from_cache = True
model = engine_cache_path
memory_monitor_thread = None
if memory_monitor_interval is not None:
memory_monitor_thread = MemoryContinuousMonitorThread(
sampling_interval=memory_monitor_interval)
memory_monitor_thread.start()
# TODO[chunweiy]: Fixit, this barely work, the cpp runtime will trigger RuntimeError, which cannot be caught
try:
llm = LLM(model, skip_tokenizer_init=True, **kwargs)
except Exception as e:
logger.error(f"Failed to create LLM with {model} and {kwargs}")
raise e
if engine_cache_path is not None and not from_cache:
print_colored(f"Saving engine to {engine_cache_path}\n", "green")
llm.save(engine_cache_path)
samples: List[Sample] = list(cls.load_dataset(samples_path))
assert len(
samples
) >= num_samples, f"num_samples {num_samples} is too large. The dataset only has {len(samples)} samples."
samples = samples[:num_samples] if num_samples > 0 else samples
return cls(llm,
warmup=warmup,
samples=samples,
max_num_samples=num_samples,
batch_size=batch_size,
memory_monitor_thread=memory_monitor_thread)
def __init__(self,
llm: LLM,
samples: List[Sample],
warmup: int,
max_num_samples: int,
batch_size: int,
memory_monitor_thread: Optional[
MemoryContinuousMonitorThread] = None):
self.llm = llm
self.samples = samples
self.warmup = warmup
self.max_num_samples = max_num_samples
self.perf_items = []
self.batch_size = batch_size if batch_size > 0 else len(self.samples)
self.memory_monitor_thread = memory_monitor_thread
self.start = None
self.end = None
def run(self, end_id: int = -1, beam_width: int = 1) -> Report:
# reset states
self.perf_items = []
sample_offset = 0
sampling_params = SamplingParams(
end_id=end_id,
pad_id=end_id,
beam_width=beam_width,
)
async def lane(num_tasks: int,
sampling_params: SamplingParams,
warmup=False):
nonlocal sample_offset
for i in range(num_tasks):
sample = self.samples[sample_offset]
sample_offset += 1
sampling_params.max_new_tokens = sample.output_len
sampling_params.end_id = -2
sampling_params.pad_id = -2
start = time.time()
output = self.llm.generate_async(
sample.input_ids, sampling_params=sampling_params)
output = await output.aresult()
end = time.time()
perf_item = PerfItem(start=start,
end=end,
num_out_tokens=sum(
beam_output.length
for beam_output in output.outputs))
if not warmup:
self.perf_items.append(perf_item)
if self.warmup > 0:
logger.warning("warming up ...")
for i in range(math.ceil(self.warmup / len(self.samples))):
asyncio.run(
lane(min(self.warmup, len(self.samples)),
sampling_params,
warmup=True))
sample_offset = 0
logger.warning("running ...")
self.start = time.time()
async def run_lanes():
num_tasks = len(self.samples) // self.batch_size
lanes = [
lane(num_tasks, sampling_params) for _ in range(self.batch_size)
]
await asyncio.gather(*lanes)
asyncio.run(run_lanes())
self.end = time.time()
return self._generate_report()
@staticmethod
def load_dataset(path: Path) -> Iterable[Sample]:
with open(path, 'r') as f:
dataset = json.load(f)
for sample in dataset["samples"]:
yield Sample(input_ids=sample["input_ids"],
output_len=sample["output_len"])
def _generate_report(self) -> Report:
num_samples = len(self.perf_items)
total_latency = self.end - self.start
avg_latency = total_latency / num_samples
seq_throughput = num_samples / total_latency
token_throughput = sum(
[perf_item.num_out_tokens
for perf_item in self.perf_items]) / total_latency
total_tokens = sum(
[perf_item.num_out_tokens for perf_item in self.perf_items])
return Report(
num_samples=num_samples,
total_latency=total_latency,
avg_latency=avg_latency,
seq_throughput=seq_throughput,
token_throughput=token_throughput,
ave_tokens_per_sample=total_tokens / num_samples,
memory_usage_samples=self.memory_monitor_thread.memory_samples
if self.memory_monitor_thread else [])
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.llm.__exit__(exc_type, exc_value, traceback)
del self.llm
release_gc()
if self.memory_monitor_thread:
self.memory_monitor_thread.stop()
self.memory_monitor_thread.join()
del self.memory_monitor_thread
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