[BUG] AWQ quantization fail for GLM-4.5-Air

[avtc]

1. Looks like AWQ does not honor the layer_modules_strict=False when certain modules are not placed on every layer.

Stacktrace:

  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/module_looper.py", line 1156, in loop
    return self._loop_impl(fail_safe=fail_safe, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
    return func(*args, **kwargs)
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/module_looper.py", line 1301, in _loop_impl
    processor.layer_quantize(module, cur_layer_device, named_childs)
    ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/awq_processor.py", line 346, in layer_quantize
    module_config: List[Dict] = self.gptq_model.awq_get_modules_for_scaling(
                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        module, input_feat, self.module_kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/models/base.py", line 1400, in awq_get_modules_for_scaling
    inp = input_feat[block[0]]
          ~~~~~~~~~~^^^^^^^^^^
KeyError: 'mlp.shared_experts.gate_proj'

2. It also does not handle dynamic exclusions well (when certain modules excluded from quantization), for example I have excluded self_attn:

  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/models/base.py", line 1021, in quantize
    result = module_looper.loop(
        backend=backend,
        fail_safe=self.quantize_config.fail_safe,
    )
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/module_looper.py", line 1156, in loop
    return self._loop_impl(fail_safe=fail_safe, **kwargs)
           ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
    return func(*args, **kwargs)
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/module_looper.py", line 1301, in _loop_impl
    processor.layer_quantize(module, cur_layer_device, named_childs)
    ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/looper/awq_processor.py", line 346, in layer_quantize
    module_config: List[Dict] = self.gptq_model.awq_get_modules_for_scaling(
                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
        module, input_feat, self.module_kwargs
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/home/ubuntu/venvs/gptqmodelt/lib/python3.13t/site-packages/gptqmodel/models/base.py", line 1400, in awq_get_modules_for_scaling
    inp = input_feat[block[0]]
          ~~~~~~~~~~^^^^^^^^^^
KeyError: 'self_attn.q_proj'

[Qubitium]

@avtc Good catch. AWQ support is beta quality and it actually has not been folded into the true life cycle of gpt-qmodel v5.0. Right now awq has it's own mini life cycle. We definitely want to refractor so both awq share the life cycle of gptq and reap all the benefits.

[Qubitium]

@avtc Try again on main. I just merged AWQ MoE fix.

[avtc]

1. Looks like it work with GLM-4.5-Air, with empty dynamic, at least it passed 6 layers and proceeding, with sample=1, I am checking with 4x3090.

2. But with this dynamic it throws:

dynamic = {
        r"-:model.embed_tokens.weight": {},
        r"-:.*shared_experts": {},
        r"-:.*shared_head": {},
        r"-:lm_head.weight": {},
        r"-:.*mlp.down": {},
        r"-:.*mlp.gate": {},
        r"-:.*mlp.up": {},
        r"-:.*post_attention_layernorm": {},
        r"-:.*self_attn": {},
        r"-:.*norm.weight": {},
        r"-:.*enorm": {},
        r"-:.*hnorm": {},
        r"-:.*eh_proj": {},
        r"-:.*input_layernorm": {},
    }

The log:
_log_glm-4.5-air-awq.txt

[avtc]

@Qubitium
The result of AWQ quantized model with dynamic: {} does not look like it quantized expert modules.
The size of original BF16 padded is 223Gb, size of quantized int4g32 is 216Gb.

And vllm fails to load:

utor.py:631] WorkerProc failed to start.
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631] Traceback (most recent call last):
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/v1/executor/multiproc_executor.py", line 605, in worker_main
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     worker = WorkerProc(*args, **kwargs)
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/v1/executor/multiproc_executor.py", line 460, in __init__
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     self.worker.load_model()
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/v1/worker/gpu_worker.py", line 233, in load_model
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     self.model_runner.load_model(eep_scale_up=eep_scale_up)
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/v1/worker/gpu_model_runner.py", line 2894, in load_model
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     self.model = model_loader.load_model(
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]                  ^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/model_loader/base_loader.py", line 55, in load_model
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     self.load_weights(model, model_config)
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/model_loader/default_loader.py", line 300, in load_weights
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     loaded_weights = model.load_weights(
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]                      ^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/models/glm4_moe.py", line 724, in load_weights
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     return loader.load_weights(weights)
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/models/utils.py", line 328, in load_weights
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     autoloaded_weights = set(self._load_module("", self.module, weights))
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/models/utils.py", line 282, in _load_module
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     yield from self._load_module(
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/models/utils.py", line 255, in _load_module
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     loaded_params = module_load_weights(weights)
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]   File "/home/ubuntu/git/avtc/vllm/vllm/model_executor/models/glm4_moe.py", line 572, in load_weights
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]     param = params_dict[name_mapped]
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631]             ~~~~~~~~~~~^^^^^^^^^^^^^
(Worker_TP4 pid=498775) ERROR 11-07 20:05:59 [multiproc_executor.py:631] KeyError: 'layers.7.mlp.experts.w2_weight'

Index of layers 0 and 1:

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    "model.embed_tokens.weight": "model-00001-of-00055.safetensors",
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[Qubitium]

@avtc You're right, the whole dynamic has not been thoroughly tested/validated with the latest awq changes. Should be fixed next week

[avtc]

But even with empty dynamic it does not quantize expert modules.
I have used quantize_method="awq", format = "gemv"

[Qubitium]

But even with empty dynamic it does not quantize expert modules

Did the quant logs show it was quantizting the expert modules but the saved weights did not? I want to make sure the we are not talking tabout 2 different bugs here. If it skips quantization, when you quant, it should not show up in the cli terminal. But if it shows error_loss and etc rows of data during quantization and but failed to save, that's a different bug.

[avtc]

_log_glm-4.5-air-awq-first-3-layers.txt
@Qubitium please check the log of first three layers.

[Qubitium]

_log_glm-4.5-air-awq-first-3-layers.txt @Qubitium please check the log of first three layers.

The logs are showing the expert modules are not getting quantized. Need to check if this glm 4.5 air related, or all moe related. It's the weekend so I don't think it will be fixed until Monday but I will try to find time to take look tomorrow.

[avtc]

it's not a blocker or something