mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-13 22:18:36 +08:00
279 lines
8.4 KiB
Python
279 lines
8.4 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 time
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from functools import partial
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from typing import Literal, Optional, Tuple, Union
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# isort: off
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import torch
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import tensorrt as trt
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# isort: on
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try:
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import psutil
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except ImportError:
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psutil = None
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try:
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import pynvml
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except ImportError:
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pynvml = None
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import traceback
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from tensorrt_llm.logger import logger
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from ._common import _is_building
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if psutil is None:
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logger.warning(
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"A required package 'psutil' is not installed. Will not "
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"monitor the host memory usages. Please install the package "
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"first, e.g, 'pip install psutil'."
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)
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if pynvml is None:
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logger.warning(
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"A required package 'pynvml' is not installed. Will not "
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"monitor the device memory usages. Please install the package "
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"first, e.g, 'pip install nvidia-ml-py>=12'."
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)
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class Timer:
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def __init__(self):
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self._start_times = {}
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self._total_elapsed_times = {}
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def start(self, tag):
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self._start_times[tag] = time.time()
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def stop(self, tag) -> float:
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elapsed_time = time.time() - self._start_times[tag]
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if tag not in self._total_elapsed_times:
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self._total_elapsed_times[tag] = 0
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self._total_elapsed_times[tag] += elapsed_time
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return elapsed_time
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def elapsed_time_in_sec(self, tag) -> float:
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if tag not in self._total_elapsed_times:
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return None
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return self._total_elapsed_times[tag]
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def reset(self, tag=None) -> None:
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if tag is None:
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self._start_times.clear()
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self._total_elapsed_times.clear()
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else:
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self._start_times.pop(tag, None)
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self._total_elapsed_times.pop(tag, None)
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def summary(self):
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logger.info("Profile Results")
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for tag, elapsed_time in self._total_elapsed_times.items():
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logger.info(f" - {tag.ljust(30, '.')}: {elapsed_time:.6f} (sec)")
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_default_timer = Timer()
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def start(tag):
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_default_timer.start(tag)
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def stop(tag):
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return _default_timer.stop(tag)
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def elapsed_time_in_sec(tag):
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return _default_timer.elapsed_time_in_sec(tag)
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def reset(tag=None):
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_default_timer.reset(tag=tag)
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def summary():
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_default_timer.summary()
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MemUnitType = Literal["GiB", "MiB", "KiB"]
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class PyNVMLContext:
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def __enter__(self):
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if pynvml is not None:
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pynvml.nvmlInit()
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def __exit__(self, type, value, traceback):
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if pynvml is not None:
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pynvml.nvmlShutdown()
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if pynvml is not None:
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with PyNVMLContext():
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_device_get_memory_info_fn = partial(
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pynvml.nvmlDeviceGetMemoryInfo,
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version=pynvml.nvmlMemory_v2,
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)
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def host_memory_info(pid: Optional[int] = None) -> Tuple[int, int, int]:
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if psutil is not None:
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process = psutil.Process(pid)
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# USS reports the amount of memory that would be freed if the process
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# was terminated right now.
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# https://psutil.readthedocs.io/en/latest/index.html#psutil.Process.memory_full_info
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vmem = psutil.virtual_memory()
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total_mem = vmem.total
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free_mem = vmem.available
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alloc_mem = process.memory_full_info().uss
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return alloc_mem, free_mem, total_mem
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return 0, 0, 0 # used, free, total
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def device_memory_info(device: Optional[Union[torch.device, int]] = None) -> Tuple[int, int, int]:
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if pynvml is not None:
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if device is None:
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device = torch.cuda.current_device()
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index = device.index if isinstance(device, torch.device) else device
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with PyNVMLContext():
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handle = pynvml.nvmlDeviceGetHandleByIndex(index)
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mem_info = _device_get_memory_info_fn(handle)
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return mem_info.used, mem_info.free, mem_info.total
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return 0, 0, 0 # used, free, total
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def bytes_to_target_unit(mem_bytes: int, unit: MemUnitType) -> float:
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units = {"GiB": 1 << 30, "MiB": 1 << 20, "KiB": 1 << 10}
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_rename_map = {"GB": "GiB", "MB": "MiB", "KB": "KiB"}
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if unit not in units:
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unit = _rename_map[unit]
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return float(mem_bytes) / units[unit]
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def _format(mem_bytes: int, unit: MemUnitType) -> str:
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mem_usage = bytes_to_target_unit(mem_bytes, unit)
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return f"{mem_usage:.4f} ({unit})"
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def _print_mem_message(msg: str, tag: Optional[str] = None):
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if tag:
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msg = f"{tag} - {msg}"
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logger.info(f"[MemUsage] {msg}")
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def print_host_memory_usage(tag: Optional[str] = None, unit: MemUnitType = "GiB"):
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if psutil is None:
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return
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alloc_mem, _, _ = host_memory_info()
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msg = f"Allocated Host Memory {_format(alloc_mem, unit)}"
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_print_mem_message(msg, tag)
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def print_device_memory_usage(
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tag: Optional[str] = None,
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unit: MemUnitType = "GiB",
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device: Optional[Union[torch.device, int]] = None,
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):
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alloc_mem, _, _ = device_memory_info(device)
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msg = f"Allocated Device Memory {_format(alloc_mem, unit)}"
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_print_mem_message(msg, tag)
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def print_memory_usage(
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tag: Optional[str] = None,
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unit: MemUnitType = "GiB",
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device: Optional[Union[torch.device, int]] = None,
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):
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alloc_host_mem, _, _ = host_memory_info()
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alloc_device_mem, _, _ = device_memory_info(device=device)
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msg = (
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f"Allocated Memory: Host {_format(alloc_host_mem, unit)} "
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f"Device {_format(alloc_device_mem, unit)}"
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)
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_print_mem_message(msg, tag)
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@_is_building
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def check_gpt_mem_usage(
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engine,
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kv_dtype,
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use_gpt_attention_plugin,
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paged_kv_cache,
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max_batch_size,
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max_beam_width,
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max_seq_len,
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local_num_kv_heads,
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head_size,
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num_layers,
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) -> int:
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# Get the amount of memory
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runtime = trt.Runtime(logger.trt_logger)
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# 1. TensorRT engine activation memory
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activation_size = 0
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try:
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cuda_engine = runtime.deserialize_cuda_engine(engine)
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assert cuda_engine is not None
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activation_size = cuda_engine.device_memory_size_v2 / 1024 / 1024
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del cuda_engine
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except Exception:
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logger.warning(f"Exception when deserializing engine: {traceback.format_exc()}")
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logger.warning("Activation memory size will be regarded as 0.")
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logger.info(f"Activation memory size: {activation_size:.2f} MiB")
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# 2. Weights
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weights_size = bytes_to_target_unit(engine.nbytes, "MiB")
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logger.info(f"Weights memory size: {weights_size:.2f} MiB")
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# 3. Estimated max KV Cache size
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kv_cache_size = (
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max_batch_size
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* max_beam_width
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* 2
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* local_num_kv_heads
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* max_seq_len
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* head_size
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* num_layers
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* kv_dtype.itemsize
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)
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# without plugin, we need two set of kv cache buffers,
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# one for inputs, and the other for outputs.
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if not use_gpt_attention_plugin:
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kv_cache_size *= 2
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kv_cache_size = bytes_to_target_unit(kv_cache_size, "MiB")
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logger.info(f"Max KV Cache memory size: {kv_cache_size:.2f} MiB")
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# Estimated total amount of memory
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est_memory_size = activation_size + weights_size + kv_cache_size
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logger.info(f"Estimated max memory usage on runtime: {est_memory_size:.2f} MiB")
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_, _, total_mem = device_memory_info(torch.cuda.current_device())
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total_mem = bytes_to_target_unit(total_mem, "MiB")
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if est_memory_size > total_mem:
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logger.warning(
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f"Engine is successfully built, but GPU Memory ({total_mem:.2f} MB)"
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" may not be enough when running inference on max shape."
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)
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if paged_kv_cache:
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logger.warning(
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"Since paged_kv_cache is enabled, the max KV Cache "
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"memory size is a estimate for very extreme cases, "
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"it's possible that most cases won't meet OOM."
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)
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else:
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logger.warning(
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"Enabling `--paged_kv_cache` could help reduce the GPU memory usage on runtime."
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)
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return est_memory_size
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