task.base->not_done.notify_all(); } } else { // If it's a task initiated from this thread, decrease the counter, but // don't do anything - loop condition will do all checks for us next. if (base_owner == worker_device) { --task.base->outstanding_tasks; // Otherwise send ...
Note: as a side effect our tokens/sec now logs only non-padding tokens. So yes the tokens/sec we see in our logs will decrease but it will also now be more representative of meaningful throughput (and you won't have to listen to me complaining about misleading tokens/sec anymore). Test...
In contrast, the Constant method (where the constant is set to 1), while not the best performer at 20k epochs, shows a significant error decrease at 100k epochs, eventually becoming the most effective among the six methods. This error reduction between 20k and 100k epochs under the Constant ...
In our experiments, loss of plasticity is accompanied by a decrease in the average effective rank of the network (right panel of Extended Data Fig.3c). This phenomenon in itself is not necessarily a problem. After all, it has been shown that gradient-based optimization seems to favour low-r...
Here are some potential solutions but not sure: 1) increase batch size 2) decrease grad_clip 3) disable use_ff=True in ./Models/interpretable_diffusion/gaussian_diffusion.py/Diffusion-TS if your data is irregular) disable amp (see issues lucidrains/denoising-diffusion-pytorch#61), but this ...
PyTorch Version 1.12.1 The experimental models in this section are RetinaNet [10], Fcos [26], and ATSS [27]. To evaluate the effectiveness of the regression loss, the RetinaNet model was used to validate the method on the PASCAL VOC and Visdrone datasets, and further validation was conducte...
In our experiments, loss of plasticity is accompanied by a decrease in the average effective rank of the network (right panel of Extended Data Fig.3c). This phenomenon in itself is not necessarily a problem. After all, it has been shown that gradient-based optimization seems to favour low-...
When the loss of the test set does not decrease in three consecutive periods, the training is completed. In our experiments, we do not set the valida- tion set separately and perform validation directly on the test set, so there may be the risk of overfitting and the Fig. 3 Experimental...
the scale factor may decrease under 1 as an attempt to bring gradients to a number representable in the fp16 dynamic range. While one may expect the scale to always be above 1, our GradScaler does NOT make this guarantee to maintain performance. If you encounter NaNs in your loss or grad...
https://pytorch.org/docs/stable/distributed.html#launch-utilityfor further instructions warnings.warn( WARNING:torch.distributed.run: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal ...