Wire total_weights into ML Diagnostics metrics - #474
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- Wire total_weights into telemetry: Pass the existing `num_model_parameters` calculation into `record_scalar_metrics(..., total_weights=num_model_parameters)` across all trainers (Stable Diffusion, SDXL, Flux, Wan, and DreamBooth). This populates the total_weights card in Google Cloud ML Diagnostics. - Remove the redundant `metric_types` import and `if/else` branching. Standardize `_METRICS_TO_MANAGED` directly on canonical string literals, matching the SDK's internal representation.
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This pull request simplifies metric reporting by removing the dependency on metric_types from google_cloud_mldiagnostics and directly mapping internal metrics to canonical string names. It also removes the gradient norm metric and updates several trainers (including base_wan, dreambooth, flux, sdxl, and stable_diffusion) to explicitly pass total_weights=num_model_parameters when recording scalar metrics. Corresponding updates were made to the documentation and tests. There are no review comments, so I have no feedback to provide.
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num_model_parameterscalculation intorecord_scalar_metrics(..., total_weights=num_model_parameters)across all trainers (Stable Diffusion, SDXL, Flux, Wan, and DreamBooth). This populates the total_weights card in Google Cloud ML Diagnostics.metric_typesimport andif/elsebranching. Standardize_METRICS_TO_MANAGEDdirectly on canonical string literals, matching the SDK's internal representation.