Run With Deepspeed Raise Error Gradient Computed Twice For This

Gradient Checking Error Improving Deep Neural Networks You are using an old version of the checkpointing format that is deprecated (we will also silently ignore gradient checkpointing kwargs in case you passed it).please update to the new format on your modeling file. (i’ve checked 0.15.1, 0.16.1, 0.17.0, 0.17.1, 0.18.2, 0.19.1, and 0.20.2) either disabling gradient checkpointing or using deepspeed zero2 will fix this issue.

Gradient Boosting Error Improvement Download Scientific Diagram Can't figure out the exact reason, but i suggest checking two things: 1) check gradient values are exactly the same at the end of the decimal point at all spatial positions. 2) confirm your model has no operation that prevents gradient (less probable). Hello, i’m trying to make a deepspeed version of a code that worked without deepspeed and see if the results can be replicated in deepspeed version. however, it seems our code is not working properly and hence wanted to…. Assertionerror: the parameter 255 has already been reduced. gradient computed twice for this partition. multiple gradient reduction is currently not supported. The root cause of this issue is typically a mismatch between the gradient accumulation settings specified in your deepspeed configuration and those expected by your model.

Week 1 Gradient Checking S Last Exercise Error Improving Deep Neural Assertionerror: the parameter 255 has already been reduced. gradient computed twice for this partition. multiple gradient reduction is currently not supported. The root cause of this issue is typically a mismatch between the gradient accumulation settings specified in your deepspeed configuration and those expected by your model. I tried different versions of deepspeed and accelerate but couldn’t fix the issue. any one has any suggestions? thanks in advance. If you want to calculate ptx loss, then actor will forward twice. in your code, these two loss are executed backward once separately, which will not be any probl. After i applied deepspeed, i could increase the batch size (64 > 128, but oom with 256) of training model so i expected train time would decrease. however, even though i applied deepspeed in my code, the train time is the same. I initially thought that deepspeed code scaling by gas and exposing the scaled value to the client (hf) was the problem. but based yours and @sgugger findings, it seems there is nothing to do if hf is fine with deepspeed.backward () returning the gas scaled loss.