Create LogarithmicEqualizationModifier.yaml

LogarithmicEqualizationModifier.yaml

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+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
+      # Skip quantizing the BMMs
+      - QuantizableMatMul
+      # Skip quantizing the layers with the most sensitive activations
+      - model.layers.21.mlp.down_proj
+      - model.layers.7.mlp.down_proj
+      - model.layers.2.mlp.down_proj
+      - model.layers.20.mlp.down_proj
+      - model.layers.19.mlp.down_proj
+      post_oneshot_calibration: true
+      scheme_overrides:
+        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*$"]