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[WIP]Refine Quantizer 2#2039

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wenhuach21 wants to merge 14 commits into
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refine_calib
Open

[WIP]Refine Quantizer 2#2039
wenhuach21 wants to merge 14 commits into
mainfrom
refine_calib

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Description

Please briefly describe your main changes, the motivation.

Type of Change

Bug fix

Related Issues

Fixes or relates to #

Checklist Before Submitting

  • My code has been tested locally.
  • Documentation has been updated as needed.
  • New or updated tests are included where applicable.
  • The CUDA CI has passed. You can trigger it by commenting /azp run Unit-Test-CUDA-AutoRound.

@wenhuach21 wenhuach21 changed the title [WIP]Refine calib [WIP]Refine Quantizer 2 Jul 10, 2026
@wenhuach21

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issue repeated log

/home/wenhuach/miniforge3/envs/autoround/bin/python /home/wenhuach/auto-round/auto_round/main.py /models/Qwen3-0.6B --tasks lambada_openai --algs awq,auto-round --seqlen 512
2026-07-10 17:01:26 INFO main.py L292: start to quantize /models/Qwen3-0.6B
2026-07-10 17:01:26 INFO config.py L53: enable_opt_rtn is turned on, set --disable_opt_rtn for higher speed at the cost of accuracy.
2026-07-10 17:01:26 INFO config.py L53: enable_opt_rtn is turned on, set --disable_opt_rtn for higher speed at the cost of accuracy.

@wenhuach21

wenhuach21 commented Jul 10, 2026

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2 no loss print

2026-07-10 17:02:13 INFO mappings.py L560: AWQ resolved 84 smooth-balance mappings (3 per block × 28 blocks).
2026-07-10 17:02:13 INFO mappings.py L423: Using registered AWQ mappings for Qwen3ForCausalLM.
2026-07-10 17:02:13 INFO mappings.py L560: AWQ resolved 84 smooth-balance mappings (3 per block × 28 blocks).
2026-07-10 17:02:13 INFO base.py L201: AWQ: resolved 84 mappings across 28 blocks.
Quantizing model.layers.0: 0%| | 0/28 [00:00<?, ?it/s]2026-07-10 17:02:52 INFO device.py L1450: 'peak_ram': 2.54GB, 'peak_vram': 1.25GB
Quantizing model.layers.1: 4%|▌ | 1/28 [00:39<17:35, 39.10s/it]2026-07-10 17:03:24 INFO device.py L1450: 'peak_ram': 2.57GB, 'peak_vram': 1.37GB
Quantizing model.layers.2: 7%|█▏ | 2/28 [01:10<15:04, 34.80s/it]

3 need to check gradient accumulate step passing correctly

4 refine zero_shot


@torch.no_grad()
def quantize_block(self, ctx) -> dict:
def quantize_block(self, block, fp_inputs, input_others, fp_outputs, q_inputs, block_ctx) -> dict:

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q_inputs default none

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