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Kimi-k2 calib+export #655

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42c46d4
update kimi-k2
jingyu-ml Dec 5, 2025
33da2bc
update kimi-k2
jingyu-ml Dec 5, 2025
16e435e
update kimi-k2
jingyu-ml Dec 5, 2025
647755c
update kimi-k2
jingyu-ml Dec 5, 2025
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12 changes: 12 additions & 0 deletions examples/llm_ptq/README.md
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Expand Up @@ -131,9 +131,21 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
| QwQ | ✅ | - | - | - | ✅ |
| T5 | ✅ | ✅ | ✅ | ✅ | - |
| Whisper | ✅ | ❌ | ❌ | ❌ | - |
| Kimi-K2-Thinking-BF16 | ✅ | ❌ | ❌ | ❌ | ✅ |

> *This is a subset of the models supported. For the full list please check the [TensorRT-LLM support matrix](https://nvidia.github.io/TensorRT-LLM/reference/precision.html#support-matrix)*

> We recommend upcasting Kimi-K2-Thinking from INT4 to BF16 before running quantization.
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@cjluo-nv cjluo-nv Dec 5, 2025

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Is it a recommendation or it's something we have to do? An alterantive is to up cast the in4 to BF16 during calibration like we did with DS.

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@jingyu-ml jingyu-ml Dec 5, 2025

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But there’s no INT4 support in PyTorch, as we discussed. People have to use vLLM if they want INT4. Me and Zhiyu are looking into the vLLM calibration of this model


```python
from transformers import AutoModelForCausalLM
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@cjluo-nv cjluo-nv Dec 5, 2025

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could you move these to the examples_utils.py like: https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/examples/llm_ptq/example_utils.py#L303

from transformers.utils.quantization_config import CompressedTensorsConfig

model = AutoModelForCausalLM.from_pretrained("moonshotai/Kimi-K2-Thinking", torch_dtype="auto", device_map = "auto", local_files_only = True, trust_remote_code = True, quantization_config = CompressedTensorsConfig(run_compressed=False))

# And then save it with save_pretrained
```

> *<sup>1.</sup>The w4a8_awq is an experimental quantization scheme that may result in a higher accuracy penalty.* \
> *<sup>2.</sup>For some models, there is only support for exporting quantized checkpoints.* \
> *<sup>3.</sup>W4A8_AWQ is only available on some models but not all* \
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2 changes: 2 additions & 0 deletions examples/llm_ptq/hf_ptq.py
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Expand Up @@ -80,6 +80,7 @@
"w4a8_nvfp4_fp8": mtq.W4A8_NVFP4_FP8_CFG,
"w4a8_mxfp4_fp8": mtq.W4A8_MXFP4_FP8_CFG,
"nvfp4_mlp_only": mtq.NVFP4_MLP_ONLY_CFG,
"nvfp4_experts_only": mtq.NVFP4_EXPERTS_ONLY_CFG,
}

KV_QUANT_CFG_CHOICES = {
Expand Down Expand Up @@ -121,6 +122,7 @@ def auto_quantize(
"fp8_pb_wo",
"w4a8_mxfp4_fp8",
"nvfp4_mlp_only",
"nvfp4_experts_only",
]
for qformat in qformat_list
), "One or more quantization formats provided are not supported for unified checkpoint export"
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20 changes: 20 additions & 0 deletions modelopt/torch/quantization/config.py
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Expand Up @@ -623,6 +623,25 @@
"algorithm": "max",
}

NVFP4_EXPERTS_ONLY_CFG = {
"quant_cfg": {
"*mlp.experts*weight_quantizer": {
"num_bits": (2, 1),
"block_sizes": {-1: 16, "type": "dynamic", "scale_bits": (4, 3)},
"enable": True,
"pass_through_bwd": True,
},
"*mlp.experts*input_quantizer": {
"num_bits": (2, 1),
"block_sizes": {-1: 16, "type": "dynamic", "scale_bits": (4, 3)},
"enable": True,
"pass_through_bwd": True,
},
**_default_disabled_quantizer_cfg,
},
"algorithm": "max",
}

choices: set[str] = {
"FP8_2D_BLOCKWISE_WEIGHT_ONLY_CFG",
"FP8_AFFINE_KV_CFG",
Expand Down Expand Up @@ -652,6 +671,7 @@
"NVFP4_MLP_WEIGHT_ONLY_CFG",
"MXFP4_MLP_WEIGHT_ONLY_CFG",
"NVFP4_MLP_ONLY_CFG",
"NVFP4_EXPERTS_ONLY_CFG",
}

BiasType = Literal["static", "dynamic"]
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