Memory keeps increasing. There is mem leak between tunings. Collaborator naromero77amd commented Oct 22, 2024 @hliuca Do you know which version of rocBLAS you are using? Collaborator naromero77amd commented Oct 22, 2024 Can you please try one more test with your workload? Run TunableOp...
Contributor yanboliang commented Oct 31, 2022 Dive into this, I found there are two parts contribute to the memory allocation: Every compiled fn occupies some memory, which is the same as native Pytorch. Re-compile after each iteration(which is not expected) causes memory increasing. I'm d...
TheEMAdoesn'trequireretainingthelastNdatapoints,makingitquitememoryefficient.EMAinStableDiffusionStablediffusionusesanExponentialMovingAverageofthemodel'sweightstoimprovequalityofresultingimagesandavoidoverfittingtothemostrecentlytrainedimages.AsyncEMAEMAisindependentwithunettraining,itonlykeepsweightswhichwillbeusedbefore...
box.feature_extractor def forward(self, features, proposals, targets=None): losses = {} # TODO rename x to roi_box_features, if it doesn't increase memory consumption # 这里的box即下面的ROIBoxHead类,它的输入features即FPN得到的feature,proposals #为RPN输出(即经过nms,去除scores小的部分,经过...
Memory usage keeps increasing by 50 MB with each step. Reserved memory looks somewhat fine and stays around 5-12 GB. But virtual memory inflates to well above 400 GB after 7500 steps. Virtual memory usage shouldn't be actually allocated but it keeps itself as allocated even tough Linux dete...
TransfoXLLMHeadModel - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (fully pre-trained), Three OpenAI GPT-2 PyTorch models (torch.nn.Module) with pre-trained weights (in the modeling_gpt2.py file): GPT2Model...
In 2.2, if the sdp_kernel context manager must be used, use the memory efficient or math kernel if on Windows.with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): torch.nn.functional.scaled_dot_product_attention(q,k,v)...
(is_increasing and max_diff > 100 * 1024), msg=f"memory usage is increasing, {str(last_rss)}", ) def test_custom_module_input_op_ids(self): class MyFunc(torch.autograd.Function): @staticmethod def forward(ctx, x): ctx.save_for_backward(x) return x @staticmethod def backw...
The fact that we're forming whole batches from the start also means that we can reduce the number of allocations and use a better memory layout for the batch parts.Because of that we also cannot simply use the PyTorch's DataLoader, instead we need to use it as a mere wrapper. But ...
This will reduce peak memory yet greatly slow down inference due to not fully parallelizing the prompt encoding. So, we recommend this flag purely for debugging. Advanced Usage Multi-Strategy A recent blogpost from Character.ai revealed the company’s strategies for bringing down LLM inference ...