| test_stage: | |
| obcq_modifiers: | |
| LogarithmicEqualizationModifier: | |
| mappings: [ | |
| [["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"], | |
| [["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"], | |
| ] | |
| QuantizationModifier: | |
| ignore: | |
| # These operations don't make sense to quantize | |
| - LlamaRotaryEmbedding | |
| - LlamaRMSNorm | |
| - SiLUActivation | |
| - MatMulOutput_QK | |
| - MatMulOutput_PV | |
| # Skip quantizing the layers with the most sensitive activations | |
| # - model.layers.2.mlp.down_proj | |
| # - model.layers.59.mlp.down_proj | |
| # - model.layers.58.mlp.down_proj | |
| # - model.layers.22.mlp.down_proj | |
| # - model.layers.20.mlp.down_proj | |
| post_oneshot_calibration: true | |
| scheme_overrides: | |
| # Enable channelwise quantization for better accuracy | |
| Linear: | |
| weights: | |
| num_bits: 8 | |
| symmetric: true | |
| strategy: channel | |
| MatMulLeftInput_QK: | |
| input_activations: | |
| num_bits: 8 | |
| symmetric: true | |
| MatMulLeftInput_PV: | |
| input_activations: | |
| num_bits: 8 | |
| symmetric: true | |
| # For the embeddings, only weight-quantization makes sense | |
| Embedding: | |
| input_activations: null | |
| weights: | |
| num_bits: 8 | |
| symmetric: false | |
| SparseGPTModifier: | |
| sparsity: 0.5 | |
| block_size: 128 | |
| sequential_update: true | |
| quantize: true | |
| percdamp: 0.01 | |
| mask_structure: "0:0" | |
| targets: ["re:model.layers.\\d*$"] | |