代码语言:javascript 复制 import torch import torch.nn as nn import torch.nn.functional as F device = "cuda" if torch.cuda.is_available() else "cpu" # Example Usage: query, key, value = torch.randn(2, 3, 8, device=device), torch.randn(2, 3, 8, device=device), torch.randn(2, ...
See :ref:`npu-memory-management` for more details about NPU memory management.""" if device is None: device = torch_npu.npu.current_device() device = _get_device_index(device) if stream is None: stream = torch_npu.npu.current_stream(device) ...
# Need to be done once, after model initialization (or load) model = model.to(memory_format=torch.channels_last) # Replace with your model # Need to be done for every input input = input.to(memory_format=torch.channels_last) # Replace with your input output = model(input) 然而,并非...
下载Jupyter 笔记本:memory_format_tutorial.ipynb Sphinx-Gallery 生成的图库 前向模式自动微分(Beta) 原文:pytorch.org/tutorials/intermediate/forward_ad_usage.html 译者:飞龙 协议:CC BY-NC-SA 4.0 注意 点击这里下载完整示例代码 本教程演示了如何使用前向模式自动微分来计算方向导数(或等效地,雅可比向量积)。
For example, these two functions can measure the peak allocated memory usage of each iteration in a training loop. Args: device (torch.device or int, optional): selected device. Returns statistic for the current device, given by :func:`~torch.cuda.current_device`, if :attr:`device` is ...
"set_per_process_memory_fraction", "empty_cache", "memory_stats", "memory_stats_as_nested_dict", "reset_accumulated_memory_stats", "reset_peak_memory_stats", "reset_max_memory_allocated", "reset_max_memory_cached", "memory_allocated", ...
pin_memory(bool, optional) – 锁页内存,创建DataLoader时,设置pin_memory=True,则意味着生成的Tensor数据最开始是属于内存中的锁页内存,这样将内存的Tensor转义到GPU的显存就会更快一些。 drop_last(bool, optional) – 如果数据集大小不能被batch size整除,则设置为True后可删除最后一个不完整的batch。如果设为...
A good performance metric for a CUDA kernel is the Effective Memory Bandwidth. It is useful for you to measure this metric whenever you are writing/optimizing a CUDA kernel. Following script shows how we can measure the effective bandwidth of CUDA uniform_ kernel. import ...
To further improve its effectiveness, this allocator was tuned for the specific memory usage patterns of deep learning. For example, it rounds up allocations to multiples of 512 bytes to avoid fragmentation issues. Moreover, it maintains a distinct pool of memory for every CUDA stream (work queu...
MAC(memory access cost):内存使用量,用来评价模型在运行时的内存占用情况。 FLOPS(Floating-point Operations Per Second):每秒浮点运算次数,理解为计算速度,衡量硬件性能的指标。估算电脑的执行效能。这里的“浮点运算”,实际上包括了所有涉及小数的运算。目前大部分的处理器有一个专门用来处理浮点运算的“浮点运算器”...