Helpful tools and examples for working with flex-attention - Reduce memory usage (#100) · pytorch-labs/attention-gym@a710e18
在深度学习模型训练过程中,在服务器端或者本地pc端,输入nvidia-smi来观察显卡的GPU内存占用率(Memory-Usage),显卡的GPU利用率(GPU-util),然后采用top来查看CPU的线程数(PID数)和利用率(%CPU)。往往会发现很多问题,比如,GPU内存占用率低,显卡利用率低,CPU百分比低等等。接下来仔细分析这些问题和处理办法。 1. GP...
By setting the CUDA_ALLOC_CONF environment variable, users can modify the behavior of the memory allocator to better suit their specific requirements. This can help improve memory usage efficiency, reduce memory fragmentation, and potentially enhance the performance of PyTorch applications running on GPU...
This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. Moreover, it avoids the overhead of copying between gradients and allreduce communication buckets. When gradients are views, detach_() cannot be called on the gradients. If hitting such...
from torch.func import hessian # lets reduce the size in order not to overwhelm Colab. Hessians require # significant memory: Din = 512 Dout = 32 weight = torch.randn(Dout, Din) bias = torch.randn(Dout) x = torch.randn(Din) hess_api = hessian(predict, argnums=2)(weight, bias, x...
parameters(), lr=0.1, momentum=0.9) scheduler = ReduceLROnPlateau(optimizer, 'min') for epoch in range(10): train(...) val_loss = validate(...) # Note that step should be called after validate() scheduler.step(val_loss) print(f"Epoch {epoch} has concluded with lr of {scheduler....
把reducer的autograd_hook函数添加进去每个grad_accumulator_之中,变量index是hook的参数。这个 hook 挂在 autograd graph 之上,在 backward 时负责梯度同步。grad_accumulator 执行完后,autograd_hook 就会运行。 gradAccToVariableMap_ 存了grad_accumulator & index 的对应关系(函数指针和参数张量的对应关系),这样以后在...
DistributedDataParallel用ProcessGroup::broadcast()在初始化期间将模型状态从rank 0 的进程发送到其他进程,并对ProcessGroup::allreduce()梯度求和。 Store.hpp :协助进程组实例的集合服务找到彼此。 1.3 DDP 总体实现 我们把论文和 https://pytorch.org/docs/master/notes/ddp.html 结合起来,看看 DDP 总体实现。
It will reduce memory usage and speed up computations but you won’t be able to backprop (which you don’t want in an eval script).model.eval() will notify all your layers that you are in eval mode, that way, batchnorm or dropout layers will work in eval mode instead of training ...
"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", ...