feat: improve telemetry log (#3398)
* fix: redact trackio and data_files * fix: add new orgs to whitelist * feat: add run id to logs for users to easily share * fix: update to add more metrics * fix: add missed experiment tracker * chore: formatting in main
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@@ -153,13 +153,27 @@ class TelemetryCallback(TrainerCallback):
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self.last_report_step = step
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def _extract_last_metrics(self, state: TrainerState) -> dict:
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"""Extract last loss, learning_rate, and grad_norm from log history."""
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"""Extract last loss, learning_rate, grad_norm, and token metrics from log history."""
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if not state.log_history:
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return {"loss": 0, "learning_rate": 0, "grad_norm": 0}
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return {
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"loss": 0,
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"ppl": 0,
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"learning_rate": 0,
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"grad_norm": 0,
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"tokens/total": 0,
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"tokens/trainable": 0,
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"tokens/train_per_sec_per_gpu": 0,
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}
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last_log = state.log_history[-1]
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return {
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"loss": last_log.get("loss", 0),
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"ppl": last_log.get("ppl", 0),
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"learning_rate": last_log.get("learning_rate", 0),
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"grad_norm": last_log.get("grad_norm", 0),
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"tokens/total": last_log.get("tokens/total", 0),
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"tokens/trainable": last_log.get("tokens/trainable", 0),
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"tokens/train_per_sec_per_gpu": last_log.get(
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"tokens/train_per_sec_per_gpu", 0
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),
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}
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@@ -155,6 +155,10 @@ def send_errors(func: Callable) -> Callable:
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},
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)
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LOG.error(
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f"Error captured in telemetry. Run ID: {telemetry_manager.run_id}"
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)
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raise
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return wrapper
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@@ -30,8 +30,8 @@ FIELDS_TO_REDACT = {
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"resume_from_checkpoint",
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"hub_model_id",
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}
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PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_"}
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PATH_INDICATORS = {"path", "dir"}
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PREFIXES_TO_REDACT = {"wandb_", "comet_", "mlflow_", "gradio_", "trackio_", "swanlab_"}
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PATH_INDICATORS = {"path", "dir", "data_files"}
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# pylint: disable=duplicate-code
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RELEVANT_PACKAGES = {
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@@ -31,3 +31,10 @@ organizations:
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- "mistral-community"
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- "llava-hf"
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- "ByteDance-Seed"
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- "ACE-Step"
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- "openbmb"
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- "MiniMaxAI"
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- "stepfun-ai"
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- "internlm"
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- "katanemo"
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- "XiaomiMiMo"
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@@ -1,10 +1,12 @@
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"""helper functions for datasets"""
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import os
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from axolotl.utils.logging import get_logger
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LOG = get_logger(__name__)
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def get_default_process_count():
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if axolotl_dataset_num_proc := os.environ.get("AXOLOTL_DATASET_NUM_PROC"):
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return int(axolotl_dataset_num_proc)
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