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tqdm writes with carriage returns to a stream with no line discipline against the structlog writer, so a bar lands mid-record and the JSON line stops being parseable. The bars are quieted rather than disabled wherever something reads them: datasets and our own conversion bars are redirected to a null stream so the counter the UI status poller reads stays alive, the training worker keeps its bars countable for the same reason, and the export worker keeps drawing them because its dialog streams them. HF's stdout callbacks are dropped and what they carried is republished structurally: a trainer summary event with runtime, samples and steps per second, total_flos and memory, s_per_step and tok_per_s on training_progress, and throttled evaluation progress.
85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""training_progress must carry trainer speed, not just step and loss.
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Dropping HF's tqdm bar and per-step print removes the only place training
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throughput appeared ("1.84s/it" on the bar, "train_tokens_per_second" in the raw
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dict). Both were raw stdout rather than structured, so the replacement is to put
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the number on the structured line: throughput measured over the interval between
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two logged lines, and the run average on the first one.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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_BACKEND = Path(__file__).resolve().parent.parent
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if str(_BACKEND) not in sys.path:
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sys.path.insert(0, str(_BACKEND))
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def _throughput(step, prev_step, elapsed, prev_elapsed, tokens, prev_tokens):
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"""The calculation from TrainingManager._log_training_progress."""
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s_per_step = tok_per_s = None
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if elapsed is not None and prev_elapsed is not None and prev_step >= 0:
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d_time = elapsed - prev_elapsed
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d_steps = step - prev_step
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if d_time > 0 and d_steps > 0:
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s_per_step = round(d_time / d_steps, 3)
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if tokens is not None and prev_tokens is not None and tokens > prev_tokens:
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tok_per_s = round((tokens - prev_tokens) / d_time, 1)
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return s_per_step, tok_per_s
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def test_the_first_line_reports_no_throughput():
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# elapsed_seconds is wall time since the worker started, so it also covers the
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# imports, the model download and load and the dataset build; and on a resumed run
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# the step and token counters predate this process. Neither is a training rate.
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assert _throughput(4, -1, 8.0, None, 4000, None) == (None, None)
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assert _throughput(1010, -1, 20.0, None, 4_000_000, None) == (None, None)
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def test_later_lines_report_the_interval_not_the_average():
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# 10 steps and 20000 tokens in the 20s since the last line, after a slow start.
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s_per_step, tok_per_s = _throughput(30, 20, 120.0, 100.0, 60000, 40000)
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assert s_per_step == 2.0
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assert tok_per_s == 1000.0
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def test_matches_the_tqdm_number_it_replaces():
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# The bar showed "1.84s/it"; one step in 1.84s must report the same.
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s_per_step, _ = _throughput(19, 18, 38.34, 36.50, None, None)
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assert s_per_step == 1.84
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def test_missing_token_counts_still_give_seconds_per_step():
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s_per_step, tok_per_s = _throughput(30, 20, 120.0, 100.0, None, None)
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assert s_per_step == 2.0
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assert tok_per_s is None
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def test_no_elapsed_yields_nothing_rather_than_dividing_by_zero():
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assert _throughput(30, 20, None, None, 1, 0) == (None, None)
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def test_a_repeated_or_backwards_step_yields_nothing():
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assert _throughput(20, 20, 120.0, 100.0, 60000, 40000) == (None, None)
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assert _throughput(19, 20, 120.0, 100.0, 60000, 40000) == (None, None)
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def test_a_stalled_clock_yields_nothing():
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assert _throughput(30, 20, 100.0, 100.0, 60000, 40000) == (None, None)
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def test_token_counter_that_did_not_move_still_gives_seconds_per_step():
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s_per_step, tok_per_s = _throughput(30, 20, 120.0, 100.0, 40000, 40000)
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assert s_per_step == 2.0
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assert tok_per_s is None
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def test_the_emitter_passes_both_fields():
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text = (_BACKEND / "core/training/training.py").read_text(encoding = "utf-8")
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assert "s_per_step = s_per_step," in text
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assert "tok_per_s = tok_per_s," in text
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