| advance: null | |
| approach: post_training_dynamic_quant | |
| bf16_ops_list: [] | |
| calib_iteration: 1 | |
| calib_sampling_size: 100 | |
| framework: pytorch | |
| op: | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.0.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.1.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.2.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.3.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.4.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.attention.q_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.attention.k_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.attention.v_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.attention.out_lin | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.ffn.lin1 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - distilbert.transformer.layer.5.ffn.lin2 | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - pre_classifier | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |
| ? !!python/tuple | |
| - classifier | |
| - Linear | |
| : weight: | |
| dtype: int8 | |
| scheme: sym | |
| granularity: per_channel | |
| algorithm: minmax | |
| bit: 7.0 | |
| activation: | |
| dtype: uint8 | |
| scheme: asym | |
| granularity: per_tensor | |
| algorithm: minmax | |