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[1/2] Add a chat JSONL output mode to prepare_megatron_data_blend - #2791
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Tokenized .bin/.idx blends carry no loss mask, so --data_paths training computes the loss on every token. output_format: chat_jsonl writes the same weighted sources as train.jsonl / validation.jsonl chat data for distill.py --sft --sft_hf_dataset, which can restrict the loss to assistant tokens (--sft_loss_mode assistant). target_samples, shuffle and seed control the row budget, in-order selection and the mix. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
Replace build_qad_blend.py with data_blend.yaml for the chat JSONL mode of prepare_megatron_data_blend (#2791), which produces byte-identical train/validation files. Explain the MMLU-ProX gain under the results. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
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Review comments at @modelopt/torch/utils/plugins/prepare_megatron_data_blend.py:
- Around line 287-291: Update the cached part identity in the blend-building
flow so cached JSONL parts are not reused when selection settings change.
Include both shuffle and content_field in the part filename, or validate both
settings from a sidecar before _read_jsonl results are reused.
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CHANGELOG.rstexamples/dataset/MEGATRON_DATA_PREP.mdmodelopt/torch/utils/plugins/prepare_megatron_data_blend.pytests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py
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Changes requested: cached parts can silently produce data that does not match the current configuration.
Needs action:
- Fix cache identity and completeness tracking in
prepare_megatron_data_blend.py; add rerun tests for changedfiles/shuffleand removal oftarget_samples(see inline comments). - Honor zero-row weighted allocations and validate budgets/weights in
prepare_chat_data_blend; add an unequal-weight regression test. - Move the Bridge import to module scope, or document why this optional dependency must remain local.
No action needed:
- Existing test assertions remain intact; the new tests cover filtering, failures, splitting, and atomic writes.
- Tests were inspected, not executed.
| part = parts_dir / f"{index:02d}_{dataset}_{source.get('config')}_{label}.jsonl" | ||
| got = _read_jsonl(part) | ||
| if got is None or (want is not None and len(got) < want): | ||
| got = _take_chat_rows(source, output_dir, want, shuffle) |
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Bot comment.
The cache filename omits inputs that determine the rows: notably files, content_field, and shuffle. Changing a files source from one JSONL file to another in the same repository, or switching shuffle: true to false, reuses the old part whenever its length is sufficient. The outputs then contain the old selection while config.yaml records the new configuration. Include these selection inputs in a cache fingerprint (or validate stored metadata before reuse), and test a changed configuration in the same output directory.
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Fixed in f4a96f6. Each source's cache file is now named by a hash of every input that selects its rows (hf_dataset, config, split, files, content_field, shuffle) plus whether the selection was capped, so a changed configuration selects afresh instead of reusing the old part. New tests rerun the same output_dir with a changed shuffle and a changed split and check that both sources are streamed again.
| else max(1, round(int(target) * float(source["weight"]) / weight_sum)) | ||
| ) | ||
| label = source.get("split") or "files" | ||
| dataset = source["hf_dataset"].replace("/", "--") |
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max(1, ...) contradicts the documented round(target_samples * weight / sum(weights)) allocation and forces low/zero-weight sources into the blend. With target 100 and weights 999:1, the documented counts are 100 and 0, but this writes 101 rows and unnecessarily requires the second source to load successfully. Preserve zero allocations and skip those sources without calling _take_chat_rows (which currently also consumes one row for want=0). Validate positive budgets and finite, nonnegative weights with a positive total; cover unequal weights that round to zero.
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Fixed in f4a96f6. The budget is now exactly round(target_samples * weight / sum(weights)); a source that rounds to 0 rows is skipped without being loaded. target_samples must be a positive integer, and weights must be finite, nonnegative and not all zero; invalid configs fail before any source is read. New tests cover 999:1 weights (org/b is never streamed) and five invalid budgets.
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| part = parts_dir / f"{index:02d}_{dataset}_{source.get('config')}_{label}.jsonl" | ||
| got = _read_jsonl(part) | ||
| if got is None or (want is not None and len(got) < want): | ||
| got = _take_chat_rows(source, output_dir, want, shuffle) | ||
| _write_jsonl(part, got) | ||
| rows.extend(got[:want]) |
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[IMPORTANT Compatibility] The cached part key omits shuffle, so flipping that knob silently reuses rows selected under the other setting.
The part name is {index}_{dataset}_{config}_{label}.jsonl. split, config and index changes invalidate it; shuffle does not. A rebuild only happens when the cached part is unreadable or shorter than want, and a part built with shuffle: true is neither.
Concretely, and this is the likely path given the documented workflow (your own usage example and the docs both set shuffle: false):
- Run with the default
shuffle: true— or just interrupt it; the part is still written per-source. - Edit the config to
shuffle: falseand rerun, expecting "the first rows of each split in order". - Line 288 finds
len(got) >= want, skips_take_chat_rowsentirely, and you silently get the buffer-shuffled rows. Nothing in the output says the data doesn't matchconfig.yamlsitting next to it.
Same class of problem for a files source whose files list changed while hf_dataset and index stayed the same — label is the constant string "files".
Folding the selection-affecting knobs into the key is enough and keeps the diff small:
label = source.get("split") or "files"
dataset = source["hf_dataset"].replace("/", "--")
# Keys that change which rows get selected, so a config edit rebuilds instead of
# silently reusing rows picked under the old setting.
key = hashlib.sha1(
json.dumps(
[shuffle, source.get("config"), label, source.get("files")], sort_keys=True
).encode()
).hexdigest()[:8]
part = parts_dir / f"{index:02d}_{dataset}_{label}_{key}.jsonl"(An import hashlib at the top; json is already imported.) A sidecar JSON per part recording the same knobs, compared on load, works equally well if you prefer readable part names.
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## main #2791 +/- ##
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+ Coverage 71.97% 78.14% +6.16%
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Files 648 648
Lines 71791 72308 +517
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+ Hits 51674 56507 +4833
+ Misses 20117 15801 -4316
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| if "<extra_id_" in text: | ||
| turns = [ | ||
| {"role": _CHAT_ROLES[role], "content": content.strip()} | ||
| for role, content in _CHAT_TURN.findall(text) | ||
| if content.strip() | ||
| ] | ||
| # Markers that parse to no turns make the row malformed, not plain text: falling through | ||
| # would train the raw <extra_id_*> delimiters as assistant content. | ||
| return {"messages": turns} if turns else None |
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[IMPORTANT Algorithm] A turn whose role label isn't User/Assistant/System is dropped from the middle of the conversation, and the row is still kept.
_CHAT_TURN requires the role alternation immediately after the marker, so no match starts at an unrecognized label — while the preceding turn's content still terminates at that marker via the (?=<extra_id_\d+>|\Z) lookahead. For <extra_id_1>User\nq\n<extra_id_1>Tool\n{tool output}\n<extra_id_1>Assistant\na, findall yields [("User", "q\n"), ("Assistant", "a")] — the tool block vanishes entirely and the row passes the if turns check.
Why it matters: the surviving row is coherent-looking but mislabeled. The assistant answer is now trained conditioned on context that was removed, which is exactly the kind of row you want out of an assistant-only-loss blend, and nothing in the output or the (N rejected) count signals it happened. The agentic/tool-calling Nemotron SFT configs are the realistic trigger.
This is the same hazard the comment on lines 184-186 already guards against for the "markers parse to nothing" case — the consistent stance for an unrecognized label is to drop the row, not the turn. Detect the mismatch by checking that the matched turns account for all the markers:
if "<extra_id_" in text:
matches = _CHAT_TURN.findall(text)
turns = [
{"role": _CHAT_ROLES[role], "content": content.strip()}
for role, content in matches
if content.strip()
]
# Markers that parse to no turns make the row malformed, not plain text: falling through
# would train the raw <extra_id_*> delimiters as assistant content. An unparsed marker
# means a role we do not map (e.g. Tool), whose content would silently drop out of the
# middle of the conversation — drop the whole row instead.
if not turns or len(matches) != text.count("<extra_id_"):
return None
return {"messages": turns}(The trailing bare-delimiter case your test covers, ...world\n<extra_id_1>, would need that last marker excluded from the count — e.g. compare against text.rstrip() ending in a marker, or count only markers followed by a known label.)
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Fixed in 4359544. A text row is now dropped unless every <extra_id_N> marker carries a known role; only a bare marker at the very end is allowed without one, so the trailing-delimiter case still parses. test_to_chat_handles_all_row_shapes adds the User / Tool / Assistant case and keeps the trailing-delimiter one. None of the 12,000 text rows in the tutorial blend is affected (it still rebuilds byte-identical).
| into turns; any other ``text`` row becomes one assistant turn, so all of it is trained. | ||
| """ | ||
| if content_field == "messages": | ||
| return {"messages": row["messages"]} if row.get("messages") else None |
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[IMPORTANT Algorithm] messages rows are passed through raw, so chat mode and tokenizing mode disagree on reasoning_content and tool_calls for the same source config.
The tokenizing branch normalizes every messages source — line 155 passes reasoning_content="inline", which routes through prepare_messages_for_chat_template (modelopt/torch/utils/dataset_utils.py:60) and rewrites each assistant turn's CoT as a <think>…</think> prefix on content, plus parses JSON-string tool_calls.arguments into dicts. The chat branch here emits row["messages"] verbatim into the JSONL.
Consequence for the Nemotron Post-Training v3 configs, which this mode is aimed at and which carry reasoning_content on assistant turns (see the reasoning_content section of MEGATRON_DATA_PREP.md): Megatron-Bridge calls apply_chat_template on the row at load time, and a template that doesn't know the field just skips it — the reasoning traces are dropped from the training data. Blend the same YAML through the two output formats and you get materially different supervision, with nothing reporting the difference. The tool_calls case is worse than a drop: the v3 templates iterate arguments with Jinja |items, which raises on a JSON string.
Reusing the existing helper keeps the two modes consistent and costs one line:
if content_field == "messages":
if not row.get("messages"):
return None
# Match the tokenizing path (reasoning_content="inline" at _prepare_sources): inline the
# CoT so it survives any chat template, and normalize tool_calls for the v3 templates.
return {"messages": prepare_messages_for_chat_template(row["messages"], reasoning_content="inline")}with from modelopt.torch.utils.dataset_utils import prepare_messages_for_chat_template at the top.
Note this changes the bytes of the blend, so it will break the sha256 match against the published QAD run quoted in the PR description for any source that actually has these fields. If that equivalence is the thing being protected and the omission is deliberate, it needs to be a stated choice — a comment here saying chat mode intentionally forwards messages unmodified, and a line in the docs warning that reasoning_content is not inlined in this mode — rather than silence.
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Addressed in 4359544. _to_chat now runs messages through prepare_messages_for_chat_template, the same helper the tokenizing mode uses, so JSON-string tool_calls arguments are always normalized to dicts. Reasoning is a new reasoning_content setting (native / inline / strip) that defaults to native, deliberately unlike the tokenizing mode's hard-coded inline: in the tokenizing mode the chat template is applied while tokenizing, but this output goes to Megatron-Bridge, which applies the model's own template at load time. For templates that render reasoning_content themselves (the Nemotron 3 templates do: {% if message.reasoning_content is defined ... %} → <think>…</think>), inlining would rewrite the trace into content and change how the template lays it out. The docs and docstring now say to set inline for a template that ignores the field. New test_to_chat_prepares_messages_like_the_tokenizing_mode covers all three settings and the tool-call normalization. The tutorial blend in #2720 (20,800 rows with reasoning_content) still rebuilds byte-identical.
| if chat is None or not is_text_only_chat_example(chat): | ||
| rejected += 1 | ||
| if want is not None and rejected > 10 * want: | ||
| break | ||
| continue | ||
| rows.append(chat) | ||
| if want is not None and len(rows) >= want: | ||
| break | ||
| except Exception as e: # e.g. a gated split, or a schema the streaming reader cannot cast | ||
| raise RuntimeError(f"{name}: failed to load") from e | ||
| if want is not None and len(rows) < want: | ||
| raise RuntimeError(f"{name}: only {len(rows)}/{want} usable rows") |
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[SUGGESTION] The give-up rule at line 222 breaks out of the loop, then line 230 reports the result as only {len(rows)}/{want} usable rows — the same message a genuinely exhausted source produces. The two causes call for different fixes (lower target_samples/reweight vs. a source whose rows the filter mostly rejects), so distinguishing them saves a debugging round trip:
if want is not None and len(rows) < want:
reason = (
f"gave up after {rejected} rejected rows"
if rejected > 10 * want
else "source exhausted"
)
raise RuntimeError(f"{name}: only {len(rows)}/{want} usable rows ({reason})")Also note the 10 * want budget scales with the per-source share, so a low-weight source in a large blend gets a proportionally small allowance — fine, just worth being aware of when a source with a low text-only ratio is added at small weight.
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Done in 4359544: the error now ends in (source exhausted) or (gave up after N rejected rows).
| dataset = load_dataset( | ||
| source["hf_dataset"], source.get("config"), split=source["split"], streaming=True | ||
| ) | ||
| yield from dataset.shuffle(seed=42) if shuffle else dataset |
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[SUGGESTION] The shuffle seed is hardcoded to 42 while the config's seed (used at line 293 for the cross-source mix and the validation hold-out) is ignored here. So seed does not fully determine the output: two runs with different seed values share the same per-source row selection, and the docstring's "seeds the cross-source mix and the validation hold-out" is accurate only because the per-source part is seeded by a different, invisible constant.
Threading the configured seed through makes seed mean what a reader expects:
yield from dataset.shuffle(seed=seed) if shuffle else datasetwith seed passed down from prepare_chat_data_blend through _take_chat_rows. If you do this, include it in the part cache key (see the separate comment on line 286) so changing seed rebuilds the parts.
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Done in 4359544. The stream shuffle uses the configured seed, and the seed is part of the cache key when shuffle is on, so a new seed re-draws rows (covered by the seed case of test_chat_blend_reselects_after_a_settings_change).
| into turns; any other ``text`` row becomes one assistant turn, so all of it is trained. | ||
| """ | ||
| if content_field == "messages": | ||
| return {"messages": row["messages"]} if row.get("messages") else None |
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[IMPORTANT Compatibility] messages rows are passed through verbatim, so the chat path silently loses reasoning_content and leaves tool_calls unnormalized — unlike the tokenizing path in the same file.
_prepare_sources tokenizes messages sources with reasoning_content="inline" (and therefore normalize_tool_calls=True via prepare_messages_for_chat_template). Here the raw row dict is written straight to JSONL, so:
- CoT is dropped. The doc example added in this PR uses
nvidia/Nemotron-SFT-Math-v3withcontent_field: messages— a v3 dataset whose assistant turns carryreasoning_contentalongsidecontent. Megatron-Bridge renders these rows with the model's chat template; a template that doesn't know the field discards the trace. So the same YAML yields<think>…</think>traces underoutput_format: megatronand answer-only data underchat_jsonl, with nothing in the output telling the user. For a QAD/distillation blend that's a quiet accuracy change, not a formatting detail. tool_callswith JSON-stringargumentsbreak Nemotron v3 Jinja templates (|itemsover a string) — exactly whatnormalize_tool_calls=Trueexists to fix inmodelopt/torch/utils/dataset_utils.py.
Reusing the existing helper keeps one source of truth for chat-row preparation:
from modelopt.torch.utils.dataset_utils import prepare_messages_for_chat_template
if content_field == "messages":
messages = row.get("messages")
if not messages:
return None
# Match the tokenizing path: inline CoT and normalize tool_call arguments.
return {"messages": prepare_messages_for_chat_template(messages, reasoning_content="inline")}If leaving it raw is deliberate (e.g. Bridge is expected to handle reasoning_content natively), please say so in the doc section and the docstring, because the divergence from the tokenizing default is otherwise invisible.
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Addressed in 4359544. _to_chat now runs messages through prepare_messages_for_chat_template, the same helper the tokenizing mode uses, so JSON-string tool_calls arguments are always normalized to dicts. Reasoning is a new reasoning_content setting (native / inline / strip) that defaults to native, deliberately unlike the tokenizing mode's hard-coded inline: in the tokenizing mode the chat template is applied while tokenizing, but this output goes to Megatron-Bridge, which applies the model's own template at load time. For templates that render reasoning_content themselves (the Nemotron 3 templates do: {% if message.reasoning_content is defined ... %} → <think>…</think>), inlining would rewrite the trace into content and change how the template lays it out. The docs and docstring now say to set inline for a template that ignores the field. New test_to_chat_prepares_messages_like_the_tokenizing_mode covers all three settings and the tool-call normalization. The tutorial blend in #2720 (20,800 rows with reasoning_content) still rebuilds byte-identical.
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Claude review — [1/2] Add a chat JSONL output mode to prepare_megatron_data_blend
Reviewed all 4 changed files (436 additions): the full modelopt/torch/utils/plugins/prepare_megatron_data_blend.py diff plus surrounding context, the new tests, the docs section, and the changelog entry. Scope was the whole PR — the trigger comment carried no focus instructions.
Findings: CRITICAL: 0 · IMPORTANT: 4 · SUGGESTION: 4
The design is sound — atomic part writes, fail-loud on short or unreadable sources, reusing Megatron-Bridge's own is_text_only_chat_example as the filter so the output matches what the consumer accepts, and the mutual key rejection between the two modes are all the right calls. The tests are genuinely good: the atomic-write test interrupting json.dumps mid-part and the truncated-part rebuild test both target real failure modes rather than happy paths.
The four IMPORTANT findings share one theme: the new mode diverges from the tokenizing mode, or from its own config, without saying so. Each is a silent-wrong-data path rather than a crash, which is what raises them above suggestions — a blend built from the wrong rows trains to completion and only shows up as unexplained accuracy.
-
content_fieldis ignored unless it is"messages"(line 175) — the fallback hardcodesrow.get("text"). The tokenizing mode accepts any column name, socontent_field: contenthere reads the wrong column. Where a dataset carries both that column and a rawtext, the blend is built fromtextwith no warning at all. Validate alongside the existing config checks. -
reasoning_contentandtool_callsare not normalized (line 174) — the tokenizing branch runs everymessagessource throughprepare_messages_for_chat_template(reasoning_content="inline")(line 155); this branch forwardsrow["messages"]verbatim. For the Nemotron v3 configs this mode targets, CoT traces get silently dropped by the chat template, and JSON-stringtool_calls.argumentsraise in the v3 Jinja|items. Same YAML, two formats, materially different supervision. Worth noting this one interacts with the sha256 equivalence quoted under Testing — if the omission is deliberate to preserve it, that needs to be a stated choice in a comment and the docs. -
An unrecognized role label drops a turn but keeps the row (lines 178-186) —
_CHAT_TURNcan't start a match at e.g.<extra_id_1>Tool, yet the preceding turn still terminates at that marker. The tool block vanishes from the middle of the conversation and the row passesif turns, so the assistant answer is trained conditioned on context that was removed. This is the same hazard the comment on lines 184-186 already guards against for the parse-to-nothing case; the consistent stance is to drop the row. -
The part cache key omits
shuffle(lines 286-291) — a part built under the defaultshuffle: trueis neither unreadable nor short, so switching toshuffle: false(which both your usage example and the docs use) silently reuses buffer-shuffled rows, and theconfig.yamlwritten next to the output then misdescribes the data. Same for a changedfileslist, sincelabelis the constant"files".
The suggestions cover the give-up-vs-exhausted error message collision, the hardcoded shuffle(seed=42) that makes the config's seed not fully determine the output, .jsonl.gz members failing in the files branch where the tokenizing path handles them, and the new top-level datasets import (already covered by the existing plugin guard — noted only to keep it deliberate).
Risk: low-to-moderate, and well contained. The tokenizing path and the default output are untouched, and the new mode is opt-in behind output_format, so nothing existing regresses. The exposure is entirely within the new mode, where the failure signature is a quietly wrong blend rather than an error — findings 1 and 4 in particular are reachable from the exact config shape the PR documents. Backward compatibility is handled as described: rejecting the chat-only keys in the tokenizing path is the right trade and is called out in the description. The [1/2] split is clean — this lands standalone with its own tests, ahead of #2720.
🤖 Generated with Claude Code
| if "files" in source: | ||
| for path in _download_files(source, output_dir): | ||
| with open(path, encoding="utf-8") as f: | ||
| yield from (json.loads(line) for line in f if line.strip()) |
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[IMPORTANT Compatibility] A single malformed JSONL line in a files: source aborts the whole chat blend, where the tokenizing path deliberately skips it.
_iter_rows does json.loads(line) inside _take_chat_rows's try, so one bad line raises JSONDecodeError, gets caught by except Exception, and becomes RuntimeError: <name>: failed to load — with no indication that only one record was bad.
That matters because the datasets this repo tells users to configure with files: are exactly the ones known to contain bad records. megatron_preprocess_data.encode carries this comment and a [WARN] Skipping malformed JSONL record path for that reason:
E.g. Nemotron-SFT-Agentic-v2 / tool_calling has one record padded with 239MB of NUL bytes.
and MEGATRON_DATA_PREP.md:159 configures nvidia/Nemotron-Agentic-v1 / tool_calling.jsonl through files:. So a user who switches a working tokenizing YAML to output_format: chat_jsonl gets an unrecoverable failure on data the other mode handles, and the cached-part resume doesn't help since the part is never written.
Suggest matching the tokenizing path — warn and skip, so only genuinely unreadable sources stop the run:
def _read_lines(path: str) -> Iterator[dict[str, Any]]:
with open(path, encoding="utf-8") as f:
for line in f:
if not line.strip():
continue
try:
yield json.loads(line)
except ValueError as e:
print(f"[WARN] Skipping malformed JSONL record ({len(line):,} bytes): {e}", flush=True)and call it from the files branch of _iter_rows. A skipped record then just counts toward the normal short-source check.
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Fixed in 4359544. The files reader now warns and skips a malformed record, as megatron_preprocess_data does, so only an unreadable source stops the run. Covered by test_chat_blend_reads_gzip_files_and_skips_malformed_records.
Cache each source's rows under a hash of the inputs that select them (dataset, config, split, files, content_field, shuffle) and of whether the selection was capped, so a changed configuration or a removed target_samples selects afresh instead of reusing stale rows. Skip sources whose weight rounds to zero rows, and validate target_samples and the weights. Note why the Megatron-Bridge import stays local. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
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Claude review — [1/2] Add a chat JSONL output mode to prepare_megatron_data_blend
Reviewed all 4 changed files (747 additions): the full modelopt/torch/utils/plugins/prepare_megatron_data_blend.py diff plus surrounding context, the new tests, the docs section, and the changelog entry. Also read the tokenizing mode in megatron_preprocess_data.py and prepare_messages_for_chat_template to check parity between the two modes. Scope was the whole PR — the trigger comment carried no focus instructions.
Findings: CRITICAL: 0 · IMPORTANT: 2 · SUGGESTION: 3
Previous round resolved
All four IMPORTANT findings from the earlier review are genuinely fixed, not papered over:
content_fieldis now validated againstmessages/textup front, so a third column name fails loudly instead of silently readingtext.messagesgo throughprepare_messages_for_chat_templatewith areasoning_contentsetting that is itself validated againstREASONING_CONTENT_MODES, and the parametrized test asserts all three modes plustool_callsnormalization.- An unknown role marker now drops the row via the
rolelesscounter, with the trailing-delimiter exception handled separately. - The part cache key is a sha256 over
hf_dataset/config/split/files/content_fieldplusshuffle,seed,reasoning_contentand the newcappedflag — which also closes the subtler case the earlier review did not raise, where droppingtarget_sampleswould have reused a capped part._chat_budgetsvalidates weights and the budget, and zero-rounded sources are skipped with a message. Each of these has a test that pins it.
This round
Both remaining IMPORTANT findings are parity gaps with the tokenizing mode in the messages / text conversion — the same class as before, where the new mode quietly produces different supervision from the same YAML.
-
An empty known-role turn is cut out of the middle of a conversation while the row is kept (lines 196-209).
turnsfilters onif content.strip()butrolelesscountslen(matches), which includes those empty matches — so they cost nothing against the guard the previous round added for unknown roles. AUser q1/ emptyAssistant/User q2/Assistant a2row yields two consecutiveuserturns with the first response removed. The worse variant is a row that parses to no assistant turn at all: it passesif turns,is_text_only_chat_exampledoes not judge role structure, and under--sft_loss_mode assistantit contributes zero unmasked label tokens — atmicro_batch_size: 1that makes the microbatch loss 0/0, so it takes the QAD run down rather than just wasting a sample. The empty leadingSystemblock that the tests cover is the benign case and should stay allowed. -
A row's
toolscolumn is dropped (lines 186-191). The tokenizing mode reads it and passes it toapply_chat_template(megatron_preprocess_data.py:229-232); this branch emits the messages only. The branch deliberately preservestool_callsargument normalization for the Nemotron v3 templates, then discards thetoolslist those same templates render into the system prompt — so a blend fromnvidia/Nemotron-Agentic-v1/tool_calling.jsonl, whichMEGATRON_DATA_PREP.md:396documents for exactly those fields, trains calls against a schema never in context. Either carrytoolsthrough, or reject sources whose rows have it — rejecting would match how this PR handles the other mode's keys.
The suggestions are minor: the except Exception around the loop also wraps conversion so a _to_chat bug is mislabeled "failed to load"; files parts all share the _files_ label so the digest is the only disambiguator and parts/ is never pruned; and the output_format check in prepare_megatron_data_blend is unreachable from main().
Risk: low, and well contained. The tokenizing path and the default output are untouched and the new mode is opt-in behind output_format, so nothing existing regresses — I checked the two blend YAMLs in the repo and neither sets any of the newly rejected keys, so the stricter tokenizing-mode validation breaks no committed config. Both findings sit inside the new text / messages conversion, and finding 1 is the only one that can fail a run rather than quietly degrade it. The end-to-end sha256 equivalence against the published #2720 run is strong evidence for the paths that blend exercises; neither finding contradicts it, since both concern row shapes that blend evidently does not contain. The [1/2] split is clean: this lands standalone with its own tests ahead of #2720, and at 267 source insertions it is well inside the PR size budget.
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Claude review — [1/2] Add a chat JSONL output mode to prepare_megatron_data_blend
Reviewed all 4 changed files (747 additions): the full modelopt/torch/utils/plugins/prepare_megatron_data_blend.py diff plus surrounding context, the new tests, the docs section, and the changelog entry. Also read the tokenizing mode in megatron_preprocess_data.py and prepare_messages_for_chat_template to check parity between the two modes. Scope was the whole PR — the trigger comment carried no focus instructions.
Findings: CRITICAL: 0 · IMPORTANT: 2 · SUGGESTION: 3
Previous round resolved
All four IMPORTANT findings from the earlier review are genuinely fixed, not papered over:
content_fieldis now validated againstmessages/textup front, so a third column name fails loudly instead of silently readingtext.messagesgo throughprepare_messages_for_chat_templatewith areasoning_contentsetting that is itself validated againstREASONING_CONTENT_MODES, and the parametrized test asserts all three modes plustool_callsnormalization.- An unknown role marker now drops the row via the
rolelesscounter, with the trailing-delimiter exception handled separately. - The part cache key is a sha256 over
hf_dataset/config/split/files/content_fieldplusshuffle,seed,reasoning_contentand the newcappedflag — which also closes the subtler case the earlier review did not raise, where droppingtarget_sampleswould have reused a capped part._chat_budgetsvalidates weights and the budget, and zero-rounded sources are skipped with a message. Each of these has a test that pins it.
This round
Both remaining IMPORTANT findings are parity gaps with the tokenizing mode in the messages / text conversion — the same class as before, where the new mode quietly produces different supervision from the same YAML.
-
An empty known-role turn is cut out of the middle of a conversation while the row is kept (lines 196-209).
turnsfilters onif content.strip()butrolelesscountslen(matches), which includes those empty matches — so they cost nothing against the guard the previous round added for unknown roles. AUser q1/ emptyAssistant/User q2/Assistant a2row yields two consecutiveuserturns with the first response removed. The worse variant is a row that parses to no assistant turn at all: it passesif turns,is_text_only_chat_exampledoes not judge role structure, and under--sft_loss_mode assistantit contributes zero unmasked label tokens — atmicro_batch_size: 1that makes the microbatch loss 0/0, so it takes the QAD run down rather than just wasting a sample. The empty leadingSystemblock that the tests cover is the benign case and should stay allowed. -
A row's
toolscolumn is dropped (lines 186-191). The tokenizing mode reads it and passes it toapply_chat_template(megatron_preprocess_data.py:229-232); this branch emits the messages only. The branch deliberately preservestool_callsargument normalization for the Nemotron v3 templates, then discards thetoolslist those same templates render into the system prompt — so a blend fromnvidia/Nemotron-Agentic-v1/tool_calling.jsonl, whichMEGATRON_DATA_PREP.md:396documents for exactly those fields, trains calls against a schema never in context. Either carrytoolsthrough, or reject sources whose rows have it — rejecting would match how this PR handles the other mode's keys.
The suggestions are minor: the except Exception around the loop also wraps conversion so a _to_chat bug is mislabeled "failed to load"; files parts all share the _files_ label so the digest is the only disambiguator and parts/ is never pruned; and the output_format check in prepare_megatron_data_blend is unreachable from main().
Risk: low, and well contained. The tokenizing path and the default output are untouched and the new mode is opt-in behind output_format, so nothing existing regresses — I checked the two blend YAMLs in the repo and neither sets any of the newly rejected keys, so the stricter tokenizing-mode validation breaks no committed config. Both findings sit inside the new text / messages conversion, and finding 1 is the only one that can fail a run rather than quietly degrade it. The end-to-end sha256 equivalence against the published #2720 run is strong evidence for the paths that blend exercises; neither finding contradicts it, since both concern row shapes that blend evidently does not contain. The [1/2] split is clean: this lands standalone with its own tests ahead of #2720, and at 267 source insertions it is well inside the PR size budget.
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The source looks good: the cache-key and allocation issues from earlier rounds are all fixed. I have a few small inline comments, plus a suggestion to trim the tests.
Tests: consolidating them keeps the same coverage
Most of the new tests were added one per bot finding, and several of them check the same behaviour. The patch below takes the file from 19 test functions (32 cases) to 11 (25 cases), net −187 lines:
- Cache invalidation:
does_not_reuse_parts_of_another_selection,reselects_after_a_settings_changeanddoes_not_reuse_a_capped_part_without_a_budgetall check that a changed cache-key field re-streams the source. They become one parametrized test with cases split / files / shuffle / seed / reasoning_content / no_budget. The "run twice to prove it's cached" step is already covered byreuses_and_rebuilds_cached_parts. to_chat_prepares_messages_like_the_tokenizing_mode(3 cases): cut to two asserts into_chat_handles_all_row_shapes. Tool-call normalization andstripare already tested intests/unit/torch/utils/test_dataset_utils.py; only thenativedefault is specific to this mode.- No usable rows:
fails_on_a_source_without_usable_rowsis folded into the_take_chat_rowstest as thewant=Nonecase. - Zero-weight sources:
skips_sources_rounded_to_zero_rowsis folded into themain()end-to-end test, which now uses 3:1:0 weights and so also checks the weighted row counts. filessources:reads_files_sourcesandreads_gzip_files_and_skips_malformed_recordsare merged into one end-to-end test with a plain file, a.gzfile and a malformed line.- Config rejection: the budget, tokenizer and
content_fieldrejection tests become one parametrized test. A_source(name, **fields)helper replaces the 10-line inline source dicts. writes_parts_atomically: it patchedjson.dumpsto fail on call #75. That number silently depends on_part_pathalso callingjson.dumps, so the test can break on unrelated changes. It is replaced by a direct_write_jsonltest that writes a row which can't be serialized and checks that no file appears.weight: nancase: it never testedmath.isfinite, becausenan >= 0is already False. I removed that check and the test still passed, so the patch usesinfinstead.
What I ran: I applied the patch on 4359544 in nvcr.io/nvidia/nemo:26.08. The original file gives 32 passed and the patched file 25 passed. I then removed 11 safeguards from the source one at a time (reasoning_content / seed / capped in the cache key, zero-weight skip, want=None exhausted check, atomic write, gzip opener, malformed-line skip, unknown-role check, native default, isfinite). For each one, at least one patched test failed.
Patch: git apply on top of 4359544
diff --git a/tests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py b/tests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py
index b3f0fdd0f7..aaed260943 100644
--- a/tests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py
+++ b/tests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py
@@ -129,14 +129,25 @@ def _image_chat(i: int) -> dict:
}
+def _source(name: str, **fields) -> dict:
+ source = {
+ "hf_dataset": f"org/{name}",
+ "split": "train",
+ "content_field": "messages",
+ "weight": 1,
+ }
+ source.update(fields)
+ return {k: v for k, v in source.items() if v is not None}
+
+
def _read_jsonl(path: Path) -> list[dict]:
return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines()]
@pytest.fixture
def chat_blend(tmp_path: Path, monkeypatch):
- """Two equal-weight ``messages`` sources of 100 rows each; returns (config writer, stream counts)."""
- calls = {"org/a": 0, "org/b": 0}
+ """Sources of 100 ``messages`` rows each; returns (config writer, stream counts per source)."""
+ calls = Counter()
def iter_rows(source, output_dir, shuffle, seed):
calls[source["hf_dataset"]] += 1
@@ -151,15 +162,7 @@ def chat_blend(tmp_path: Path, monkeypatch):
"target_samples": 100,
"shuffle": False,
"seed": 0,
- "sources": [
- {
- "hf_dataset": f"org/{n}",
- "split": "train",
- "content_field": "messages",
- "weight": 1,
- }
- for n in ("a", "b")
- ],
+ "sources": [_source("a"), _source("b")],
}
config.update(overrides)
path = tmp_path / "config.yaml"
@@ -173,6 +176,11 @@ def test_to_chat_handles_all_row_shapes():
msgs = [{"role": "user", "content": "hi"}]
assert blend_module._to_chat({"messages": msgs}, "messages") == {"messages": msgs}
assert blend_module._to_chat({"messages": []}, "messages") is None
+ # Unlike the tokenizing mode, reasoning_content stays on the message by default (native).
+ reasoning = [{"role": "assistant", "content": "4", "reasoning_content": "2+2"}]
+ assert blend_module._to_chat({"messages": reasoning}, "messages")["messages"] == reasoning
+ inline = blend_module._to_chat({"messages": reasoning}, "messages", "inline")
+ assert inline["messages"] == [{"role": "assistant", "content": "<think>\n2+2\n</think>\n4"}]
expected = {
"messages": [
@@ -182,9 +190,8 @@ def test_to_chat_handles_all_row_shapes():
}
text = "<extra_id_0>System\n<extra_id_1>User\nhello\n<extra_id_1>Assistant\nworld\n"
assert blend_module._to_chat({"text": text}, "text") == expected
- # Any marker ends a turn, so a trailing or label marker is not trained as content.
- text = "<extra_id_1>User\nhello\n<extra_id_1>Assistant\nworld\n<extra_id_1>"
- assert blend_module._to_chat({"text": text}, "text") == expected
+ # Any marker ends a turn, so a trailing marker is not trained as content.
+ assert blend_module._to_chat({"text": text + "<extra_id_1>"}, "text") == expected
assert blend_module._to_chat({"text": "def f(): pass"}, "text") == {
"messages": [{"role": "assistant", "content": "def f(): pass"}]
}
@@ -196,47 +203,26 @@ def test_to_chat_handles_all_row_shapes():
assert blend_module._to_chat({"text": text}, "text") is None
-@pytest.mark.parametrize(
- ("mode", "assistant"),
- [
- ("native", {"role": "assistant", "content": "4", "reasoning_content": "2+2"}),
- ("inline", {"role": "assistant", "content": "<think>\n2+2\n</think>\n4"}),
- ("strip", {"role": "assistant", "content": "4"}),
- ],
-)
-def test_to_chat_prepares_messages_like_the_tokenizing_mode(mode, assistant):
- messages = [
- {"role": "user", "content": "2+2?"},
- {"role": "assistant", "content": "4", "reasoning_content": "2+2"},
- {
- "role": "assistant",
- "content": "",
- "tool_calls": [{"function": {"name": "add", "arguments": '{"a": 2, "b": 2}'}}],
- },
- ]
- chat = blend_module._to_chat({"messages": messages}, "messages", mode)
- assert chat["messages"][1] == assistant
- # JSON-string tool-call arguments become dicts, as the Nemotron v3 chat templates expect.
- assert chat["messages"][2]["tool_calls"][0]["function"]["arguments"] == {"a": 2, "b": 2}
-
-
-def test_take_chat_rows_skips_non_text_rows_and_gives_up(tmp_path: Path, monkeypatch):
- source = {"hf_dataset": "org/a", "split": "train", "content_field": "messages"}
+def test_take_chat_rows_skips_non_text_rows_and_fails_without_enough(tmp_path: Path, monkeypatch):
+ source = _source("a")
rows = [_chat(0), _image_chat(1), _chat(2), _chat(3)]
monkeypatch.setattr(blend_module, "_iter_rows", lambda *a: iter(rows))
got = blend_module._take_chat_rows(source, tmp_path, 3, False, 0, "native")
assert [c["messages"][0]["content"] for c in got] == ["q0", "q2", "q3"]
- monkeypatch.setattr(
- blend_module, "_iter_rows", lambda *a: iter([_image_chat(i) for i in range(50)])
- )
+ images = [_image_chat(i) for i in range(50)]
+ monkeypatch.setattr(blend_module, "_iter_rows", lambda *a: iter(images))
with pytest.raises(RuntimeError, match=r"only 0/2 usable rows \(gave up after 21 rejected"):
blend_module._take_chat_rows(source, tmp_path, 2, False, 0, "native")
+ # Without a budget, an all-rejected source must not drop out of the blend silently.
+ with pytest.raises(RuntimeError, match=r"only 0/any usable rows \(source exhausted"):
+ blend_module._take_chat_rows(source, tmp_path, None, False, 0, "native")
def test_chat_blend_splits_weighted_rows(tmp_path: Path, chat_blend, monkeypatch):
- write_config, _ = chat_blend
- config_path = write_config()
+ write_config, calls = chat_blend
+ sources = [_source("a", weight=3), _source("b"), _source("c", weight=0)]
+ config_path = write_config(sources=sources)
monkeypatch.setattr(
sys, "argv", ["prepare_megatron_data_blend.py", "--config", str(config_path)]
)
@@ -244,8 +230,10 @@ def test_chat_blend_splits_weighted_rows(tmp_path: Path, chat_blend, monkeypatch
out = tmp_path / "blend"
assert len(_read_jsonl(out / "validation.jsonl")) == 5
rows = _read_jsonl(out / "train.jsonl") + _read_jsonl(out / "validation.jsonl")
- # Equal weights: each source contributes its first 50 rows, q0..q49.
- assert Counter(r["messages"][0]["content"] for r in rows) == {f"q{i}": 2 for i in range(50)}
+ # 3:1 weights: the first 75 rows of org/a and 25 of org/b; org/c rounds to zero and is skipped.
+ expected = Counter({f"q{i}": 1 for i in range(75)}) + Counter(f"q{i}" for i in range(25))
+ assert Counter(r["messages"][0]["content"] for r in rows) == expected
+ assert calls["org/c"] == 0
assert (out / "config.yaml").read_bytes() == config_path.read_bytes()
@@ -264,186 +252,64 @@ def test_chat_blend_reuses_and_rebuilds_cached_parts(tmp_path: Path, chat_blend)
assert (tmp_path / "blend" / "train.jsonl").read_text(encoding="utf-8") == first
-@pytest.mark.parametrize(
- "change",
- [
- {"shuffle": True},
- {
- "sources": [
- {
- "hf_dataset": f"org/{n}",
- "split": "test",
- "content_field": "messages",
- "weight": 1,
- }
- for n in ("a", "b")
- ]
- },
- ],
- ids=["shuffle", "split"],
-)
-def test_chat_blend_does_not_reuse_parts_of_another_selection(chat_blend, change):
- write_config, calls = chat_blend
- blend_module.prepare_chat_data_blend(write_config())
- blend_module.prepare_chat_data_blend(write_config(**change))
- assert calls == {"org/a": 2, "org/b": 2}
-
-
@pytest.mark.parametrize(
("first", "second"),
[
- ({"shuffle": True, "seed": 0}, {"shuffle": True, "seed": 1}),
- ({}, {"reasoning_content": "inline"}),
+ ({}, {"sources": [_source("a", split="test")]}),
(
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "files": ["a.jsonl"],
- "content_field": "messages",
- "weight": 1,
- }
- ]
- },
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "files": ["b.jsonl"],
- "content_field": "messages",
- "weight": 1,
- }
- ]
- },
+ {"sources": [_source("a", split=None, files=["a.jsonl"])]},
+ {"sources": [_source("a", split=None, files=["b.jsonl"])]},
),
+ ({}, {"shuffle": True}),
+ ({"shuffle": True, "seed": 0}, {"shuffle": True, "seed": 1}),
+ ({}, {"reasoning_content": "inline"}),
+ ({}, {"target_samples": None}), # a capped part must not stand in for every row
],
- ids=["seed", "reasoning_content", "files"],
+ ids=["split", "files", "shuffle", "seed", "reasoning_content", "no_budget"],
)
-def test_chat_blend_reselects_after_a_settings_change(chat_blend, first, second):
+def test_chat_blend_reselects_after_a_selection_change(chat_blend, first, second):
write_config, calls = chat_blend
blend_module.prepare_chat_data_blend(write_config(**first))
- blend_module.prepare_chat_data_blend(write_config(**first)) # cached
blend_module.prepare_chat_data_blend(write_config(**second))
assert calls["org/a"] == 2
-def test_chat_blend_fails_on_a_source_without_usable_rows(chat_blend, monkeypatch):
- write_config, _ = chat_blend
-
- def iter_rows(source, output_dir, shuffle, seed):
- make_row = _chat if source["hf_dataset"] == "org/a" else _image_chat
- yield from (make_row(i) for i in range(100))
-
- monkeypatch.setattr(blend_module, "_iter_rows", iter_rows)
- # Without target_samples, an all-rejected source must not drop out of the blend silently.
- with pytest.raises(
- RuntimeError, match=r"org/b train: only 0/any usable rows \(source exhausted"
- ):
- blend_module.prepare_chat_data_blend(write_config(target_samples=None))
-
-
-def test_chat_blend_does_not_reuse_a_capped_part_without_a_budget(tmp_path: Path, chat_blend):
- write_config, calls = chat_blend
- assert blend_module.prepare_chat_data_blend(write_config()) == {"validation": 5, "train": 95}
- # Every usable row of both sources, not the 50 per source cached by the capped run.
- sizes = blend_module.prepare_chat_data_blend(write_config(target_samples=None))
- assert sizes == {"validation": 10, "train": 190}
- assert calls == {"org/a": 2, "org/b": 2}
-
-
-def test_chat_blend_skips_sources_rounded_to_zero_rows(tmp_path: Path, chat_blend):
- write_config, calls = chat_blend
- config = write_config(
- sources=[
- {"hf_dataset": "org/a", "split": "train", "content_field": "messages", "weight": 999},
- {"hf_dataset": "org/b", "split": "train", "content_field": "messages", "weight": 1},
- ]
- )
- assert blend_module.prepare_chat_data_blend(config) == {"validation": 5, "train": 95}
- assert calls == {"org/a": 1, "org/b": 0}
-
-
@pytest.mark.parametrize(
("overrides", "match"),
[
({"target_samples": 0}, "positive integer"),
({"target_samples": 1.5}, "positive integer"),
- (
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "split": "train",
- "content_field": "messages",
- "weight": -1,
- }
- ]
- },
- "nonnegative",
- ),
- (
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "split": "train",
- "content_field": "messages",
- "weight": 0,
- }
- ]
- },
- "not all zero",
- ),
- (
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "split": "train",
- "content_field": "messages",
- "weight": float("nan"),
- }
- ]
- },
- "finite",
- ),
+ ({"sources": [_source("a", weight=-1)]}, "nonnegative"),
+ ({"sources": [_source("a", weight=0)]}, "not all zero"),
+ ({"sources": [_source("a", weight=float("inf"))]}, "finite"),
+ ({"tokenizer": "x"}, "does not use"),
+ ({"target_tokens": 1000}, "does not use"),
+ ({"reasoning_content": "drop"}, "reasoning_content must be one of"),
+ # A row with both columns must not silently be read from ``text``.
+ ({"sources": [_source("a", content_field="conversations")]}, "messages or text"),
],
)
-def test_chat_blend_rejects_invalid_budgets(chat_blend, overrides, match):
+def test_chat_blend_rejects_invalid_configs(chat_blend, overrides, match):
write_config, calls = chat_blend
with pytest.raises(ValueError, match=match):
blend_module.prepare_chat_data_blend(write_config(**overrides))
- assert calls == {"org/a": 0, "org/b": 0}
+ assert not calls
-def test_chat_blend_writes_parts_atomically(tmp_path: Path, chat_blend, monkeypatch):
+def test_tokenized_blend_rejects_chat_options(chat_blend):
write_config, _ = chat_blend
- real_dumps = blend_module.json.dumps
- calls = {"n": 0}
-
- def flaky_dumps(obj, *args, **kwargs):
- calls["n"] += 1
- if calls["n"] == 75: # partway through the second source's part
- raise KeyboardInterrupt
- return real_dumps(obj, *args, **kwargs)
-
- monkeypatch.setattr(blend_module.json, "dumps", flaky_dumps)
- with pytest.raises(KeyboardInterrupt):
- blend_module.prepare_chat_data_blend(write_config())
- parts = tmp_path / "blend" / "parts"
- assert not list(parts.glob("org--b_*.jsonl")) # only the interrupted .tmp file was written
- (part,) = parts.glob("org--a_*.jsonl")
- assert len(_read_jsonl(part)) == 50
+ with pytest.raises(ValueError, match="Tokenized blends do not use"):
+ blend_module.prepare_megatron_data_blend(write_config())
-def test_chat_blend_fails_on_a_bad_source(tmp_path: Path, chat_blend, monkeypatch):
+def test_chat_blend_fails_on_a_bad_short_or_tiny_source(tmp_path: Path, chat_blend, monkeypatch):
write_config, _ = chat_blend
- real_iter_rows = blend_module._iter_rows
+ with pytest.raises(RuntimeError, match="org/a train: only 100/250 usable rows"):
+ blend_module.prepare_chat_data_blend(write_config(target_samples=500))
+ with pytest.raises(RuntimeError, match="Too few rows"):
+ blend_module.prepare_chat_data_blend(write_config(target_samples=10))
def iter_rows(source, output_dir, shuffle, seed):
- if source["hf_dataset"] == "org/b":
- yield from real_iter_rows(source, output_dir, shuffle, seed)
- return
yield from (_chat(i) for i in range(10)) # yields some rows, then the reader fails
raise TypeError("cannot cast")
@@ -453,86 +319,33 @@ def test_chat_blend_fails_on_a_bad_source(tmp_path: Path, chat_blend, monkeypatc
assert not (tmp_path / "blend" / "train.jsonl").exists()
-def test_chat_blend_fails_on_a_short_or_tiny_blend(chat_blend):
- write_config, _ = chat_blend
- with pytest.raises(RuntimeError, match="org/a train: only 100/250 usable rows"):
- blend_module.prepare_chat_data_blend(write_config(target_samples=500))
- with pytest.raises(RuntimeError, match="Too few rows"):
- blend_module.prepare_chat_data_blend(write_config(target_samples=10))
+def test_write_jsonl_is_atomic(tmp_path: Path):
+ path = tmp_path / "part.jsonl"
+ with pytest.raises(TypeError):
+ blend_module._write_jsonl(path, [_chat(0), {"bad": object()}])
+ assert not path.exists()
def test_chat_blend_reads_files_sources(tmp_path: Path, monkeypatch):
- jsonl_path = tmp_path / "chat.jsonl"
- jsonl_path.write_text("".join(json.dumps(_chat(i)) + "\n" for i in range(40)), encoding="utf-8")
- monkeypatch.setattr(huggingface_hub, "hf_hub_download", Mock(return_value=str(jsonl_path)))
- config_path = tmp_path / "config.yaml"
- config_path.write_text(
- yaml.safe_dump(
- {
- "output_dir": str(tmp_path / "blend"),
- "output_format": "chat_jsonl",
- "sources": [
- {
- "hf_dataset": "org/a",
- "files": ["data/chat.jsonl"],
- "content_field": "messages",
- "weight": 1,
- }
- ],
- }
- ),
- encoding="utf-8",
- )
- # No target_samples: every usable row, in file order before mixing.
- assert blend_module.prepare_chat_data_blend(config_path) == {"validation": 2, "train": 38}
-
-
-@pytest.mark.parametrize(
- ("overrides", "match"),
- [
- ({"tokenizer": "x"}, "does not use"),
- ({"target_tokens": 1000}, "does not use"),
- ({"reasoning_content": "drop"}, "reasoning_content must be one of"),
- # A row with both columns must not silently be read from ``text``.
- (
- {
- "sources": [
- {
- "hf_dataset": "org/a",
- "split": "train",
- "content_field": "conversations",
- "weight": 1,
- }
- ]
- },
- "content_field messages or text",
- ),
- ],
-)
-def test_chat_blend_rejects_tokenizer_options(chat_blend, overrides, match):
- write_config, _ = chat_blend
- with pytest.raises(ValueError, match=match):
- blend_module.prepare_chat_data_blend(write_config(**overrides))
-
-
-def test_tokenized_blend_rejects_chat_options(chat_blend):
- write_config, _ = chat_blend
- with pytest.raises(ValueError, match="Tokenized blends do not use"):
- blend_module.prepare_megatron_data_blend(write_config())
-
-
-def test_chat_blend_reads_gzip_files_and_skips_malformed_records(tmp_path: Path, monkeypatch):
plain, packed = tmp_path / "a.jsonl", tmp_path / "b.jsonl.gz"
lines = [json.dumps(_chat(i)) for i in range(20)]
- plain.write_text(
- "\n".join([*lines[:10], '{"messages": [', *lines[10:]]) + "\n", encoding="utf-8"
- )
+ # A malformed record is skipped with a warning, as in the tokenizing reader.
+ plain.write_text("\n".join([*lines[:10], '{"messages": [', *lines[10:]]) + "\n", "utf-8")
with gzip.open(packed, "wt", encoding="utf-8") as f:
f.writelines(json.dumps(_chat(i)) + "\n" for i in range(20, 40))
paths = {"data/a.jsonl": str(plain), "data/b.jsonl.gz": str(packed)}
monkeypatch.setattr(
huggingface_hub, "hf_hub_download", lambda repo_id, filename, **kwargs: paths[filename]
)
- source = {"hf_dataset": "org/a", "files": list(paths), "content_field": "messages"}
- rows = list(blend_module._iter_rows(source, tmp_path / "blend", False, 0))
- assert [r["messages"][0]["content"] for r in rows] == [f"q{i}" for i in range(40)]
+ config_path = tmp_path / "config.yaml"
+ config = {
+ "output_dir": str(tmp_path / "blend"),
+ "output_format": "chat_jsonl",
+ "sources": [_source("a", split=None, files=list(paths))],
+ }
+ config_path.write_text(yaml.safe_dump(config), encoding="utf-8")
+ # No target_samples: every usable row.
+ assert blend_module.prepare_chat_data_blend(config_path) == {"validation": 2, "train": 38}
+ out = tmp_path / "blend"
+ rows = _read_jsonl(out / "train.jsonl") + _read_jsonl(out / "validation.jsonl")
+ assert sorted(r["messages"][0]["content"] for r in rows) == sorted(f"q{i}" for i in range(40))| __all__ = ["prepare_chat_data_blend", "prepare_megatron_data_blend"] | ||
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||
| # A turn of a ``text`` row in the NeMo chat format: ``<extra_id_1>User\n...``. Any marker ends the | ||
| # turn, so a trailing delimiter or a label marker is not kept as content. |
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The comment says a label marker is not kept as content, which suggests SteerLM-style rows (<extra_id_1>Assistant\n<extra_id_2>quality:4,...\nanswer) are handled. In fact the roleless check below drops those rows entirely; I confirmed this with a probe. If dropping them is intended, could the comment say so? Otherwise the label line needs stripping, not counting.
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Dropping those rows is intended. Without SteerLM-specific parsing, the label line would otherwise be trained as assistant content. In 4ecf574 the comment now says so explicitly, and a SteerLM-style row is a test case.
| # A turn of a ``text`` row in the NeMo chat format: ``<extra_id_1>User\n...``. Any marker ends the | ||
| # turn, so a trailing delimiter or a label marker is not kept as content. | ||
| _CHAT_TURN = re.compile( | ||
| r"<extra_id_\d+>(User|Assistant|System)\n?(.*?)(?=<extra_id_\d+>|\Z)", re.DOTALL |
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Nit: because \n? is optional, <extra_id_1>Userguide\nhi parses as a user turn whose content is guide\nhi. Requiring \n (or using (?=\n|<extra_id_|\Z) after the role) would reject those rows instead.
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Fixed in 4ecf574. The role must now be followed by a newline, another marker or the end, so <extra_id_1>Userguide\nhi has no role and the row is dropped. Added as a test case.
| if not all(math.isfinite(w) and w >= 0 for w in weights) or sum(weights) <= 0: | ||
| raise ValueError(f"Source weights must be finite, nonnegative and not all zero: {weights}.") | ||
| target = config.get("target_samples") | ||
| if target is None: |
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With no target_samples, _take_chat_rows collects a whole Hugging Face split in a Python list before writing it. That's fine for small files sources, but on something like Nemotron-Pretraining-SFT-v1 it will run out of memory. Could we require target_samples when any source is an HF split (or stream rows to the part file instead of collecting them), and mention this in the docs?
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Fixed in 4ecf574. target_samples is now required when any source is a Hugging Face split, checked before anything is read or written. Documented in the docstring and MEGATRON_DATA_PREP.md. Leaving it out still works for files sources. I didn't stream parts to disk: the final mix holds every row in memory anyway, so that alone wouldn't bound memory.
| ``<output_dir>/parts``, keyed by the settings that select its rows, so an interrupted run | ||
| resumes. | ||
| """ | ||
| config = load_config(config_path) |
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Minor asymmetry: prepare_megatron_data_blend rejects chat configs, but the public prepare_chat_data_blend never checks output_format, so it also runs on a config that doesn't set output_format: chat_jsonl. The format is now checked in three places (main, here, and the tokenizing function). One dispatcher, or the same check here, would make the two entry points consistent.
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Fixed in 4ecf574. Each public function now rejects a config meant for the other: prepare_chat_data_blend requires output_format: chat_jsonl, and prepare_megatron_data_blend rejects anything but megatron. main() only dispatches, so there is one check per entry point instead of three. Both checks have tests.
Drop text rows with an empty non-System turn or a role marker not followed by a newline, and any row without an assistant turn; keep a row's tools for the chat template; require target_samples for Hugging Face split sources; make each entry point reject the other's output_format. Tests consolidated per review. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
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@kevalmorabia97 thanks for the test patch. I applied it as is in 4ecf574, then added cases for this round's fixes: What I ran in the NeMo container:
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Changes requested: earlier cache and conversion fixes are verified, but source-row projection still silently removes media indicators and chat context.
Needs action:
- Check original rows for media before
_to_chatprojection, preserve supported top-levelsystemcontext, and add regressions intest_prepare_megatron_data_blend.py(see inline comments). - 💬 Author requires budgets for HF splits — uncapped
filesstill retain all rows in memory; require a budget there too, or implement bounded-memory mixing and test it. - Move
load_datasetto module scope inprepare_megatron_data_blend.py, or document a concrete reason for deferring it.
No action needed:
- ✔️ Earlier cache identity, allocation, normalization, malformed-role, empty-source, gzip and format-check concerns are resolved; the local Bridge import is justified.
- Existing base-test assertions remain intact; consolidation of newly added tests is justified by shared-helper coverage and parametrized integration cases.
- Tests were inspected, not executed.
| if want is not None and rejected > 10 * want: | ||
| break | ||
| continue | ||
| rows.append(chat) |
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Bot comment.
The earlier source-media finding remains open: _to_chat has already discarded every top-level field except messages/tools when this filter runs. A source row with ordinary text messages plus images therefore looks indistinguishable from a genuinely text-only row here. Run Bridge's media check on the original row before projection (and retain the converted-row check as appropriate). Add a regression with text messages and top-level media, not only image blocks inside message content.
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Fixed in 157897c. _take_chat_rows now runs Bridge's is_text_only_chat_example on {**row, **chat}. That way the source row's top-level media keys are judged too, while the conversation checked is still the converted one. Added the regression: a row with text messages and a top-level images key is rejected.
| # As in the tokenizing mode: the chat template renders the tool definitions into the prompt. | ||
| if row.get("tools"): | ||
| chat["tools"] = row["tools"] | ||
| else: |
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Bot comment.
The earlier chat-context finding also remains open. This projection retains tools but still discards a source's top-level system prompt. For the Bridge-supported row shape {system: ..., messages: [...]}, the emitted conversation loses its instructions. Preserve that supported field or normalize it into a system message consistently with Bridge, and test that the prompt survives conversion.
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Fixed in 157897c. A non-empty top-level system field is now kept alongside tools, so Bridge prepends it as the system message just as it would for the source row (normalize_chat_conversation). Added a test that the prompt survives conversion, and the docs now mention it.
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|
||
| def prepare_chat_data_blend(config_path: Path) -> dict[str, int]: | ||
| """Write a weighted chat blend as ``train.jsonl`` / ``validation.jsonl``; return their sizes. | ||
|
|
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Bot comment.
The reply addresses uncapped HF splits, but not the original uncapped-files memory path: returning None budgets causes _take_chat_rows to materialize each whole file source and preparation to retain the entire combined blend. Raw repository files can be just as large as splits (and this reader exists specifically to bypass split schema problems). Require target_samples for these sources too, or provide bounded-memory spooling/mixing. Add coverage for the chosen restriction or bounded path.
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Fixed in 157897c. target_samples is now always required, since the selected rows are mixed in memory whatever the source type. This also removes the uncapped path: the capped cache flag and the budget-less branches. The validation test covers a missing target_samples, and restoring the old uncapped path makes it fail.
| print(f"[WARN] Skipping malformed JSONL record in {path}: {e}", flush=True) | ||
| return | ||
| from datasets import load_dataset | ||
|
|
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Bot comment.
Please move this import to module scope, or add a concrete justification for deferring it. The existing Bridge import has the required optional-dependency explanation, but this one does not; merely noting in the reply that it was moved locally does not explain why it must remain local.
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Moved back to module scope in 157897c. This module already imports megatron_preprocess_data at module scope, which imports load_dataset at its own top level, so deferring it here bought nothing.
Run Megatron-Bridge's text-only check on the source row's top-level keys too, keep a top-level system prompt for Bridge to prepend, always require target_samples since the selected rows are mixed in memory, and import load_dataset at module scope like megatron_preprocess_data. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
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Review comments at @examples/dataset/MEGATRON_DATA_PREP.md:
- Line 188: Update the target_samples explanation in the example to clarify that
it is required for Hugging Face splits, while files-only blends can omit it to
keep every usable row.
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| matches = _CHAT_TURN.findall(text) | ||
| # Markers that parse to no turns make the row malformed, not plain text: falling through | ||
| # would train the raw <extra_id_*> delimiters as assistant content. A marker without a known | ||
| # role (a Tool turn, or a SteerLM label line) or a known turn left empty would silently cut | ||
| # it out of the conversation, so drop the row. Only a bare delimiter at the very end needs no | ||
| # role, and only the System block, which real rows leave empty, may be empty. | ||
| roleless = len(_CHAT_MARKER.findall(text)) - len(matches) | ||
| if roleless > (1 if _CHAT_TRAILING_MARKER.search(text) else 0) or any( | ||
| not content.strip() and role != "System" for role, content in matches | ||
| ): | ||
| return None |
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[IMPORTANT Correctness] Text before the first <extra_id_*> marker is silently dropped from the conversation, which is exactly the failure the guard right below it is meant to prevent.
_CHAT_TURN only matches starting at a marker, so any prefix text is never part of a turn. The roleless counter can't see it either: it compares marker counts, and a prefix contains no marker. For
preamble text
<extra_id_1>User
q
<extra_id_1>Assistant
a
_CHAT_MARKER.findall → 2, matches → 2, so roleless == 0 and the row is kept as [user "q", assistant "a"] with preamble text discarded.
Why it matters: the comment two lines above states the invariant — "A marker without a known role … would silently cut it out of the conversation, so drop the row" — and this is the one way content still gets cut silently. A text row whose system/instruction block precedes the first marker would train on a conversation missing its own setup, with no warning and no row rejection, and the blend would look fine. The 10 * want give-up rule can't catch it because the rows are accepted.
Suggested fix — require the row to begin with a marker, so a prefix drops the row like any other unparseable shape:
matches = _CHAT_TURN.findall(text)
# Markers that parse to no turns make the row malformed, not plain text: falling through
# would train the raw <extra_id_*> delimiters as assistant content. A marker without a known
# role (a Tool turn, or a SteerLM label line), text before the first marker, or a known turn
# left empty would silently cut it out of the conversation, so drop the row. Only a bare
# delimiter at the very end needs no role, and only the System block, which real rows leave
# empty, may be empty.
roleless = len(_CHAT_MARKER.findall(text)) - len(matches)
if (
not _CHAT_MARKER.match(text.lstrip())
or roleless > (1 if _CHAT_TRAILING_MARKER.search(text) else 0)
or any(not content.strip() and role != "System" for role, content in matches)
):
return NoneWorth a case in test_to_chat_handles_all_row_shapes alongside the existing drop cases (the "Userguide" / Tool / SteerLM ones), since nothing in the suite currently exercises a leading-text row.
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Fixed in 312129b. A marked-up text row must now start with a marker (after leading whitespace), so a preamble drops the row instead of being cut out. Added your case to test_to_chat_handles_all_row_shapes. The tutorial blend's rebuild is still byte-identical.
| raise RuntimeError(f"{name}: failed to read or convert") from e | ||
| if len(rows) < want: | ||
| gave_up = rejected > 10 * want | ||
| reason = f"gave up after {rejected} rejected rows" if gave_up else "source exhausted" | ||
| raise RuntimeError(f"{name}: only {len(rows)}/{want} usable rows ({reason})") |
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[SUGGESTION] The give-up threshold scales with the budget, so it is tightest exactly where it is least informative. A small-weight source gets want small — e.g. weight: 2 out of 82 with target_samples: 100 gives want == 2, so 21 rejected rows abort the entire run.
Since rejection rate is a property of the source, not of its weight, a source that is (say) 10% text-only is fine at want == 900 (9000 rejects allowed, ~9000 needed) but fails at want == 2. The run stops with only 0/2 usable rows (gave up after 21 rejected), which reads like a broken source rather than "the sampling window was too small to tell".
A floor would make the rule budget-independent without weakening it for the large sources it is there to protect:
budget = 10 * want + 100and the matching gave_up = rejected > budget / rejected > budget check in the loop. Non-blocking — the current behavior fails loudly, which is the right default; this just avoids a false alarm on low-weight sources.
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Done in 312129b. The limit is now 10 * want + 100 rejected rows, so a low-weight source isn't judged by its first few rows. The test now feeds 200 rejected rows and expects gave up after 121 rejected.
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Claude review — scope: full PR (4 files; the two-dot git diff <base> HEAD also surfaced unrelated hunks from main moving ahead, which I excluded). Reviewed modelopt/torch/utils/plugins/prepare_megatron_data_blend.py in full, plus the test and doc changes; CHANGELOG.rst skimmed.
Findings: CRITICAL 0 · IMPORTANT 1 · SUGGESTION 1
IMPORTANT — leading text in a text row is silently cut from the conversation
_to_chat, the marker branch: _CHAT_TURN only matches starting at an <extra_id_*> marker, so content before the first marker never joins a turn — and the roleless guard cannot detect it, because it compares marker counts and a prefix has no marker. A row of the form preamble then <extra_id_1>User / q / <extra_id_1>Assistant / a yields markers == matches == 2, so roleless == 0 and the row is accepted as [user "q", assistant "a"] with preamble dropped.
That is the one remaining way content gets cut silently, which is exactly what the comment above the guard says the row should be dropped for ("would silently cut it out of the conversation, so drop the row"). The 10 * want give-up rule cannot catch it either, since these rows are accepted rather than rejected. The fix is a one-line addition to the existing guard (not _CHAT_MARKER.match(text.lstrip())), plus a case in test_to_chat_handles_all_row_shapes next to the Tool / SteerLM / Userguide drops — nothing in the suite currently covers a leading-text row.
SUGGESTION — give-up threshold scales with the weight, not the source
_take_chat_rows: 10 * want makes the window tightest for the lowest-weight sources, where it says the least. At want == 2 a source that is 10% text-only aborts the whole run after 21 rejects, reporting only 0/2 usable rows, which reads like a broken source rather than too small a sampling window. A floor (10 * want + 100) keeps the rule budget-independent. Non-blocking.
What I checked and found sound
- Validate-before-mutate ordering: every config check (
output_format, the other mode's keys,max_samples,content_field,reasoning_content,_chat_budgets) runs beforeparts_dir.mkdir, so a rejected config leaves no directories behind. - Cache key:
_part_pathkeys onhf_dataset/config/split/files/content_fieldplus exactly the settings that select rows, and correctly omitsseedwhenshuffleis false (seed then only affects the cross-source mix and hold-out, which are not part-level).target_samplesis rightly excluded — a part serves any budget it covers, andlen(got) < wantforces a refresh. The parametrized reselection test covers all five key dimensions. - Media filter:
is_text_only_chat_example({**row, **chat})judges the source row's top-level media keys as well as the converted turns, so a row carryingimagesis dropped — the earlier gap is closed, and tested both ways. - Marker edge cases: in-content markers, roles not followed by a newline, Tool/SteerLM label markers and mid-conversation empty turns all push
rolelessover the trailing-marker allowance and drop the row; the empty-Systemexemption is the only pass-through, and it is filtered out ofturns. - Failure loudness: a non-list
messagesraises inside the_take_chat_rowstry and surfaces asfailed to read or convertrather than a half-converted row; a short source and a too-small blend both raise;_write_jsonlwrites through a.tmpplusos.replace, and the test asserts no partial file survives a serialization error. - Plugin laziness:
is_text_only_chat_exampleis imported inside_take_chat_rowswith the required justifying comment, so the tokenizing mode still works without Megatron-Bridge;datasetsat module top is a corepyprojectdependency, and the module already loads underimport_plugin. - Backward compatibility: no shipped blend YAML sets any of the newly rejected chat-only keys (checked
examples/megatron_bridge/data/nemotron-cascade-2-blend.yamland the Qwen3.6 tutorial), and those keys were previously ignored, so rejecting them is a fail-loud improvement rather than a break.prepare_megatron_data_blend's signature and return type are unchanged, and__all__grows additively. - No mode/state, export or checkpoint surface is touched, so the
modelopt_stateand export-compatibility categories do not apply. 36_800in the doc and PR body parses as an int under PyYAML's YAML 1.1 resolver, so the stricttarget_samplestype check accepts it.
Risk: low. The change is additive and confined to one data-prep utility, the validation is thorough, and the byte-identical end-to-end reproduction of the #2720 blend is strong evidence. The one IMPORTANT finding is a narrow silent-data-loss path in the text marker parser; the rest of that parser is already conservative, which is why it stands out.
🤖 Generated with Claude Code
A text row with content before its first chat marker would lose it, so the row is dropped. Give up after 10 * want + 100 rejected rows, so a small budget does not misjudge a source by its first few rows. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Yue <yueshen@nvidia.com>
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@kevalmorabia97 could you take another look when you have a moment? Every thread you opened is addressed in a pushed commit, and your test patch is applied. Since your last review:
28 tests pass in the NeMo container, and the #2720 tutorial blend still rebuilds byte-identical. |
| try: | ||
| for row in _iter_rows(source, output_dir, shuffle, seed): | ||
| chat = _to_chat(row, source["content_field"], reasoning_content) | ||
| # Judge the source row's top-level media keys too, which the conversion does not keep. | ||
| if chat is None or not is_text_only_chat_example({**row, **chat}): | ||
| rejected += 1 | ||
| if rejected > max_rejected: | ||
| break | ||
| continue | ||
| rows.append(chat) | ||
| if len(rows) >= want: | ||
| break | ||
| except Exception as e: # e.g. a gated split, or a schema the streaming reader cannot cast | ||
| raise RuntimeError(f"{name}: failed to read or convert") from e |
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[IMPORTANT Correctness] The blanket except Exception spans the whole row loop, not just the reader, so one malformed row kills the entire source — and that diverges from the precedent this file sets for itself two modules over.
Concretely reachable. With a files: source there is no schema to cast against (_iter_rows does a raw json.loads per line), so a row like {"messages": "hello"} or {"messages": [{...}, "oops"]} reaches _to_chat:
messagesis truthy, so thenot messagesguard passes;prepare_messages_for_chat_templateiterates it and appends each non-dict element unchanged (dataset_utils.py:87-89), returning["h", "e", …];- line 228 then evaluates
turn.get("role")on astr→AttributeError.
That AttributeError is caught here and re-raised as f"{name}: failed to read or convert", which also discards the traceback — the user is told the source is unreadable when in fact one record out of millions was.
Why it matters. The tokenizing mode deliberately does the opposite, and says so, for exactly this data: megatron_preprocess_data.py:206-213 skips a malformed record ("E.g. Nemotron-SFT-Agentic-v2 / tool_calling has one record padded with 239MB of NUL bytes") and :236-237 skips a record whose chat template failed. _iter_rows already mirrors the first of those for JSON decode errors (lines 244-248) — but a row that decodes fine and converts badly is not covered. So switching a working tokenizing YAML to chat_jsonl hard-fails on data the other mode absorbs.
It compounds with the cache: the part is only written after _take_chat_rows returns (line 400), so a streaming run that dies at row N has nothing to resume from. A re-launch redoes the whole stream and dies at the same row.
Suggested fix — narrow the try to the reader and skip-with-warning on a per-row conversion failure, counting it as rejected so the existing give-up rule still bounds a source that is wholly unusable:
try:
for row in _iter_rows(source, output_dir, shuffle, seed):
try:
chat = _to_chat(row, source["content_field"], reasoning_content)
# Judge the source row's top-level media keys too, which the conversion does not keep.
usable = chat is not None and is_text_only_chat_example({**row, **chat})
except Exception as e:
# Skip as the tokenizing reader does: one unparseable record must not lose the source.
print(f"[WARN] {name}: skipping unconvertible row: {e}", flush=True)
usable = False
if not usable:
rejected += 1
if rejected > max_rejected:
break
continue
rows.append(chat)
if len(rows) >= want:
break
except Exception as e: # e.g. a gated split, or a schema the streaming reader cannot cast
raise RuntimeError(f"{name}: failed to read") from eA test feeding files: a row whose messages is a string (alongside enough good rows to meet the budget) fails on the current code with RuntimeError: failed to read or convert and passes after the fix — it slots naturally into test_chat_blend_reads_files_sources, which already pins the malformed-JSON skip.
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+1. I reproduced this in nemo:26.08: a files: row {"messages": "hello"} raises AttributeError in _to_chat, and that fails the whole source as "failed to read or convert". The tokenizing mode skips such records. A per-row try that counts the failure as a rejected row, as suggested above, keeps the give-up limit and matches that behaviour. Please land this before merging.
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kevalmorabia97
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kevalmorabia97
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Approving to unblock. All four of my earlier comments are addressed, and the test consolidation looks good.
I re-checked at 312129b in nemo:26.08: the 26 chat tests pass, CI is green, and I read Bridge's is_text_only_chat_example / normalize_chat_conversation. Running the Bridge filter on {**row, **chat} checks the right things, and keeping system matches how Bridge prepends it.
Please address the open claude[bot] thread on the per-row try before merging (my +1 is in that thread). There's also a one-line nit inline.
| if row.get(key): | ||
| chat[key] = row[key] | ||
| else: | ||
| text = row.get("text") or "" |
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Nit: a non-string text value (e.g. a list) skips the marker branch and becomes {"role": "assistant", "content": ["x"]}. Bridge's text-only filter accepts a list of strings, so the row is kept. if not isinstance(text, str) or not text: return None would drop it.
rohansjoshi
added
the
cherry-pick-done
#2769 #2770 #2719 #2796 #2801 #2794 #2787 #2682 #2793 #2791 #2792 #2766 #2823 #2777 #2773 #2813 #2832 #2795 #2684 #2822 #2838 #2807 #2772 #2837 #2820 (#2843) ## Cherry-picked PRs - #2647 - #2700 - #2718 - #2578 - #2580 - #2711 - #2731 - #2726 - #2697 - #2769 - #2770 - #2719 - #2796 - #2801 - #2794 - #2787 - #2682 - #2793 - #2791 - #2792 - #2766 - #2823 - #2777 - #2773 - #2813 - #2832 - #2795 - #2684 - #2822 - #2838 - #2807 - #2772 - #2837 - #2820 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Expanded quantized model export support for MXFP4, MXFP8, FP8, and additional block-quantization formats. * Added quantization support for GPT-OSS and Step-family models, plus export support for native FP8 experts. * Added chat JSONL data preparation for Megatron workflows and an option to freeze the student MTP head during distillation. * Added model recipes for Kimi, Gemma, DiffusionGemma, and GLM variants. * **Bug Fixes** * Improved ONNX quantization and export compatibility, distributed calibration consistency, and handling of packed attention caches. * Improved safe model loading and low-precision quantization accuracy. * **Documentation** * Updated the 0.48.0 changelog and guidance for data preparation and quantization recipes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Also includes a partial backport of #2656 (only the `_validate_fold_weight` hook and the `mtq.fold_weight` pre-check), which #2820 depends on. --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> Signed-off-by: Rohan Joshi <rohjoshi@nvidia.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com> Signed-off-by: Yue <yueshen@nvidia.com> Signed-off-by: joe-nv <222375587+joe-nv@users.noreply.github.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: ColinM-sys <cmcdonough@50words.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Signed-off-by: Meng Xin <164952785+mxinO@users.noreply.github.com> Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com> Co-authored-by: Shengliang Xu <106840466+shengliangxu@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Juhi Mittal <39641197+juhi10071998@users.noreply.github.com> Co-authored-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com> Co-authored-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com> Co-authored-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Co-authored-by: Wei-Ming Chen <17592131+meenchen@users.noreply.github.com> Co-authored-by: yueshen2016 <39203804+yueshen2016@users.noreply.github.com> Co-authored-by: joe <222375587+joe-nv@users.noreply.github.com> Co-authored-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Co-authored-by: Colin McDonough <cmcdonough@50words.com> Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: mxinO <164952785+mxinO@users.noreply.github.com> Co-authored-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com> Co-authored-by: Chad Voegele <chadvoegele@users.noreply.github.com>
What does this PR do?
Type of change: New feature
prepare_megatron_data_blendwrites tokenized.bin/.idxdata, which has no loss mask, so--data_pathstraining computes the loss on every token. Recipes that distill only on assistant responses use Megatron-Bridge's chat path instead (distill.py --sft --sft_hf_dataset ... --sft_loss_mode assistant), which reads chat JSONL and applies the model's chat template and assistant mask itself. Until now each such recipe needed its own blend script.output_format: chat_jsonlwrites the same YAML-defined weighted sources astrain.jsonl/validation.jsonl(one{"messages": [...]}per line) for that path:target_samplessets the row budget; each source contributesround(target_samples * weight / sum(weights))rows. It is required, since the selected rows are mixed in memory.content_fieldmust bemessagesortext. Rows are kept only if Megatron-Bridge'sis_text_only_chat_exampleaccepts them, judged on the converted conversation plus the source row's top-level media keys. Atextrow without markers becomes one assistant turn, so all of it is trained. Atextrow with the<extra_id_1>User/Assistant/Systemmarkers is split into turns. It is dropped if it has text before the first marker, a marker has any other role (a Tool turn, a SteerLM label line), a role name is not followed by a newline, or a turn other thanSystemis empty. Any row with no assistant turn is dropped, since assistant-only loss trains nothing in it.messagesgo throughprepare_messages_for_chat_template, like the tokenizing mode, sotool_callsarguments are normalized, and a row'stoolslist and top-levelsystemprompt are kept for the chat path.reasoning_content(native/inline/strip) defaults tonative, unlike the tokenizing mode'sinline: Bridge applies the model's chat template at training time, and templates such as Nemotron 3's renderreasoning_contentthemselves. Useinlinefor a template that ignores the field.shuffle: falsetakes the first rows of each split in order (default: a buffer shuffle);seedseeds that shuffle, the cross-source mix and themin(1000, n // 20)validation hold-out.filesmay be.jsonlor.jsonl.gz; malformed records are skipped with a warning, as in the tokenizing reader.<output_dir>/parts, keyed by the settings that select its rows, so an interrupted run resumes and a changed configuration selects afresh.partscan be deleted oncetrain.jsonlis written.The tokenizing mode is unchanged, except that it now rejects the chat-only keys instead of ignoring them. Each entry point rejects a config whose
output_formatbelongs to the other.This is [1/2]; #2720 [2/2] uses it for the NemotronH omni VL QAD tutorial (replacing that tutorial's
build_qad_blend.py) and merges after this one.Usage
python -m modelopt.torch.utils.plugins.prepare_megatron_data_blend --config blend.yaml python examples/megatron_bridge/distill.py ... --sft --sft_hf_dataset /path/to/chat_blend/train.jsonl \ --sft_hf_validation /path/to/chat_blend/validation.jsonl --sft_loss_mode assistantTesting
tests/gpu_megatron/torch/utils/plugins/test_prepare_megatron_data_blend.py: 28 passed in the NeMo 26.08 container with Megatron-Bridgemain(2 existing, 26 new, consolidated per review). They cover the row shapes (leading text, unknown-role and label markers, roles without a newline, empty turns, rows without an assistant turn),toolsandreasoning_contenthandling, the text-only filter and give-up rule, sources without usable rows,.jsonl.gzand malformed records infiles, the weighted split with a zero-weight source, config validation (budgets, weights, a missingtarget_samples,content_field,output_format, the other mode's keys), cache reuse and rebuild, no reuse after a changed selection, atomic writes, and failing or short sources. Reverting each review fix of the last three rounds (14 in all) one at a time makes at least one test fail.train.jsonlandvalidation.jsonl) to the data that tutorial's published QAD run trained on; re-checked on the latest commit.Before your PR is "Ready for review"
output_format: chat_jsonlare now rejected.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
train.jsonlandvalidation.jsonlfiles for chat training workflows with assistant-only loss configuration.Documentation