🐛 fix torch memory profiling

[joerunde]

This PR updates the logic to determine how much memory is allocated on the gpu not by torch. We had previously made an assumption that models would release all memory held for activation weights during a forward pass, but this is currently not the case with bitsandbytes quantized models. It's unclear to us whether that's intended for those models, however this fix is a much more straightforward and understandable calculation.

We also removed the gpu memory utilization limit in the quantization tests because

1. We have much more accurate memory profiling now, so hopefully we shouldn't need it to be set lower, and
2. We wanted to change the file so that the quantization tests would run for this PR before merging

Here's a profile of two forward() passes through meta-llama/Llama-Guard-3-8B-INT8 with bitsandbytes quantization. Notice how tensors are allecated in one forward pass and later freed in the next:

Fixes a bug introduced by #9352

cc @tjohnson31415

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[joerunde]

@chenqianfzh Is this memory usage pattern expected with bitsandbytes quantization?

[chenqianfzh]

@chenqianfzh Is this memory usage pattern expected with bitsandbytes quantization?

In bnb-8bit quantization, some data needed be preserved between forward passes. But it is not the case for other bnb quantization variations.

So the above memory usage pattern is expected in the test of 'meta-llama/Llama-Guard-3-8B-INT8' and
"yec019/fbopt-350m-8bit" in the test cases in tests/quantization/test_bitsandbytes.py.

Hope it helps.

[joerunde]

@chenqianfzh Is this memory usage pattern expected with bitsandbytes quantization?

In bnb-8bit quantization, some data needed be preserved between forward passes. But it is not the case for other bnb quantization variations.

So the above memory usage pattern is expected in the test of 'meta-llama/Llama-Guard-3-8B-INT8' and "yec019/fbopt-350m-8bit" in the test cases in tests/quantization/test_bitsandbytes.py.

Hope it helps.

That does help, thanks!

[tlrmchlsmth]

In bnb-8bit quantization, some data needed be preserved between forward passes. But it is not the case for other bnb quantization variations.

@chenqianfzh Could you explain what data needs to be preserved between forward passes? This is a little surprising to me

[tlrmchlsmth]

LGTM, thanks for the fix!

[chenqianfzh]

In bnb-8bit quantization, some data needed be preserved between forward passes. But it is not the case for other bnb quantization variations.

@chenqianfzh Could you explain what data needs to be preserved between forward passes? This is a little surprising to me

sorry for the late response. I was sick last week.

In bnb 8bit, the tensor of matmul_states of last generation might be used in the current generation and thus use some memory. You can check func _apply_8bit_weight() in file bitsandbytes.py for more details. HTH.