Create recipe.yaml

recipe.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
+        - MatMulOutput_QK
+        - MatMulOutput_PV
+        # 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.8.self_attn.q_proj
+        - model.layers.8.self_attn.k_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
+        # 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*$"]