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Create quantize.yaml

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  1. quantize.yaml +41 -0
quantize.yaml ADDED
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+ test_stage:
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+ obcq_modifiers:
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+ LogarithmicEqualizationModifier:
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+ mappings: [
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+ [["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
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+ [["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
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+ ]
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+ QuantizationModifier:
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+ ignore:
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+ # These operations don't make sense to quantize
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+ - LlamaRotaryEmbedding
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+ - LlamaRMSNorm
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+ - SiLUActivation
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+ - MatMulOutput_QK
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+ - MatMulOutput_PV
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+ # Skip quantizing the layers with the most sensitive activations
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+ - model.layers.1.mlp.down_proj
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+ - model.layers.30.mlp.down_proj
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+ - model.layers.31.mlp.down_proj
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+ - model.layers.28.mlp.down_proj
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+ - model.layers.29.mlp.down_proj
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+ post_oneshot_calibration: true
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+ scheme_overrides:
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+ Linear:
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+ weights:
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+ num_bits: 8
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+ symmetric: true
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+ strategy: channel
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+ MatMulLeftInput_QK:
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+ input_activations:
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+ num_bits: 8
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+ symmetric: true
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+ MatMulLeftInput_PV:
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+ input_activations:
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+ num_bits: 8
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+ symmetric: true
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+ Embedding:
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+ input_activations: null
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+ weights:
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+ num_bits: 8
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+ symmetric: false