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extend nvfp4 logic to MoEs #75

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Oct 8, 2025
Merged

extend nvfp4 logic to MoEs #75

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15 changes: 14 additions & 1 deletion hf_torchao_vllm/quantize_hf_model_with_llm_compressor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,20 @@ def tokenize(sample):
# * quantize the weights to fp4 with per group 16 via ptq
# * calibrate a global_scale for activations, which will be used to
# quantize activations to fp4 on the fly
recipe = QuantizationModifier(targets="Linear", scheme="NVFP4", ignore=["lm_head"])
recipe = QuantizationModifier(
targets="Linear",
scheme="NVFP4",
ignore=[
"lm_head",
# for Qwen MoE, but ok to just hardcode here for now
# https://github.com/vllm-project/llm-compressor/blob/33ef5f497a9801893764c6a2c880cb1f560067fa/examples/quantizing_moe/qwen_example.py#L10
"re:.*mlp.gate$",
"re:.*mlp.shared_expert_gate$",
# also skip attention and shared expert, to focus on MoE for now
"re:.*self_attn.*",
"re:.*shared_expert.*",
],
)

# Apply quantization.
oneshot(
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33 changes: 23 additions & 10 deletions hf_torchao_vllm/quantize_hf_model_with_torchao.py
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Expand Up @@ -121,14 +121,21 @@ def get_quantization_config(args):
expert_fqn_to_config = {}
# TODO(future PR): this is annoying, I should be able to use a regex here
for layer_idx in range(24):
for expert_idx in range(60):
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.gate_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.up_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.down_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.q_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.k_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.v_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.o_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.gate"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.gate_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.up_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.down_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert_gate"] = None
expert_fqn_to_config[f"lm_head"] = None
module_fqn_to_config = ModuleFqnToConfig({
"_default": None,
"_default": single_config,
**expert_fqn_to_config,
})

return TorchAoConfig(
quant_type=module_fqn_to_config,
)
Expand Down Expand Up @@ -162,12 +169,18 @@ def get_quantization_config(args):
expert_fqn_to_config = {}
# TODO(future PR): this is annoying, I should be able to use a regex here
for layer_idx in range(24):
for expert_idx in range(60):
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.gate_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.up_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.experts.{expert_idx}.down_proj"] = single_config
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.q_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.k_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.v_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.self_attn.o_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.gate"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.gate_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.up_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert.down_proj"] = None
expert_fqn_to_config[f"model.layers.{layer_idx}.mlp.shared_expert_gate"] = None
expert_fqn_to_config[f"lm_head"] = None
module_fqn_to_config = ModuleFqnToConfig({
"_default": None,
"_default": single_config,
**expert_fqn_to_config,
})
return TorchAoConfig(
Expand Down