BUG: Mixed-precision configuration not working with STATIC quantization

[sasha-hailo]

Dear LLMC team,
I've been trying to run mixed-precision PTQ quantization using RTN.
I suspect there's a bug, as the non-default settings in mix_bits are ignored.

My understanding of the code:

• In method get_act_qparams() of rtn.py, the values of qmax / qmin / scales / zeros are determined using the default quantizer bit precision
• These values are registered as buf_act_<xxx> buffers, for all modules / layers.
• During inference time, in method a_qdq() of rtn.py, though the aquantizer object of each layer is configured correctly, it blindly loads from buffer the registered quantization parameters qmin / qmax / scales / zeros, and uses them, instead of the actual values it should support.

What do you think?
Thanks in advance!

[Harahan]

There's no get_act_qparams() in rtn.py. You can print the bit-width of each linear to check the code.

PS: This function hasn't update for a long time. If you confirm there's a bug, please feel free to contact me anytime.

[sasha-hailo]

Hi @Harahan,
Thank you for your response.
It turns out that a lot of changes have been made since my issue report (in this commit).
The functionality I was referring to as get_act_qparams() now resides in register_act_qparams() in file base_blockwise_quantization.py.

The bug, unfortunately, persists.

The "mechanism" is the same: function register_act_qparams() uses a single quantizer object (self.aquantizer) to determine the quantization parameters of all layers - and this quantizer is configured with the default settings. It determines the scale & zero point settings (w.r.t. incorrect bit width), and registers them via buf_act_scales / buf_act_zeros.

Note that the correct per-layer quantization configurations are loaded when executing deploy() function,
But they have no effect, as they are using the incorrect scale & zero-point values determined in the previous stage!

To sum it up: I think that the core issue that causes the [suspected] bug is that the calibration stage & function register_act_qparams() are unaware of the configured mixed-precision, and work with the default quantization config.
This code probably works well in dynamic quantization, but not in a static quantization scenario.
I also suspect that the same issue can happen with other quantization methods.

Can you please look into it?
Thanks in advance!

[sasha-hailo]

P.S.
An unrelated question:
I also noticed that the commit I mentioned above added some limited support to additional quantization granularity, via functions get_matmul_in_block(), get_softmax_in_block(), get_act_fn_in_block().
Do you plan to extend this support to the more common LLM models like Qwen & LLama?
(This could be really cool)

[Harahan]

It depends on whether we encounter such a need or it will be used in our research. So, not sure.

[sasha-hailo]

Did you succeed in reproducing the mix_bits problem I reported?
I believe the issue should be reopened as a bug...

[Harahan]

I'm sorry, but we do not have enough time to do this. If you are sure there's a bug, post the log/evidence and reopen the issue.

[sasha-hailo]

LLMC_RTN_W8A8_MixedA16_Bug.txt
LLMC_RTN_W8A8.txt

I'm pretty sure this is a bug.
And I now suspect that the issue affects not only RTN, but nearly any method based on static quantization.
Can you please reopen the issue? I don't think I have the permissions for this.

Please find attached 2 logs of LLMC with an RTN configuration.
One log refers to a configuration without mix-bit, the other with mix-bit.
If you compare the two files, you can see that

• The outputs of both runs are identical (same PPL score), hinting that the mix_bit configuration had no effect.
• The mix_bit configuration of the deployed model is correct (see circa line 2458 in the log)
  ==> the bug is not at the deployment stage, but at the calibration stage (see my explanation in earlier messages).

[Harahan]

I reopen the issue. Since we currently don't have the requirement for the static quantization, the bug may be fixed a long time later. You'd best try other settings.

[nelaturuharsha]

Hi,

I wanted to start using this library for a couple of things, but just to confirm, this bug affects situations where:
Static quantization is applied layer-wise (with the intention to have different layers/components at different bit-widths)

Can it be confirmed that it does not apply when I would like to more or less have the same bit-width for all components of the model or different for activations/weights?

[sasha-hailo]

To the best of my understanding,
If the quantization configuration is the same for all layers of the model, the bug does not apply.