pytorch

eac20a45 - [ao] Added table visualization capability to ModelReportVisualizer (#81973)

[ao] Added table visualization capability to ModelReportVisualizer (#81973) Summary: This adds the capability to visualize the table of information in the ModelReportVisualizer. This allows the user to filter based on module name pattern match or feature name pattern match and the implemented method `generate_table_visualization` prints out the table in a string format that is easy to parse. Expected Usage ``` mod_rep_visualizer.generate_table_visualization() ``` Can also pass in optional filters as well if needed. The tests for this were just visual inspection for two reasons: 1.) This method does not return anything, it just generates the visualization 2.) All the data to create the table visualization is gotten from `generate_filtered_tables` which is already tested, so testing all that for this again would be redundant. Example Printed Output ``` Tensor Level Information idx layer_fqn input_activation_global_max input_activation_global_min input_weight_channel_axis input_weight_threshold outlier_detection_channel_axis outlier_detection_ratio_threshold outlier_detection_reference_percentile weight_global_max weight_global_min ----- ------------- ----------------------------- ----------------------------- --------------------------- ------------------------ -------------------------------- ----------------------------------- ---------------------------------------- ------------------- ------------------- 1 block1.linear 1.9543 -1.33414 1 0.5 1 3.5 0.95 0.380521 -0.568476 2 block2.linear 1.81486 0 1 0.5 1 3.5 0.95 0.521438 -0.0256195 Channel Level Information idx layer_fqn channel constant_batch_counts input_activation_per_channel_max input_activation_per_channel_min input_weight_channel_comparison_metrics input_weight_equalization_recommended outlier_detection_batches_used outlier_detection_is_sufficient_batches outlier_detection_percentile_ratios outliers_detected weight_per_channel_max weight_per_channel_min ----- ------------- --------- ----------------------- ---------------------------------- ---------------------------------- ----------------------------------------- --------------------------------------- -------------------------------- ----------------------------------------- ------------------------------------- ------------------- ------------------------ ------------------------ 1 block1.linear 0 0 1.9543 -1.33414 0.956912 True 1 True 1.77489 False 0.300502 -0.568476 2 block1.linear 1 0 1.14313 -0.756184 1.04378 True 1 True 2.07887 False 0.336131 -0.261025 3 block1.linear 2 0 0.653274 -0.937748 1.10837 True 1 True 1.00712 False 0.380521 -0.183536 4 block2.linear 0 0 1.81486 0 0.542731 True 1 True 1.78714 False 0.13552 -0.0256195 5 block2.linear 1 0 1.72578 0 0.505475 True 1 True 1.40475 False 0.485536 0.352621 6 block2.linear 2 0 1.7284 0 0.909304 True 1 True 1.40392 False 0.521438 0.0906605 ``` Test Plan: Visual Test Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/81973 Approved by: https://github.com/jerryzh168