Allow basename of dataset paths to match registered names#997
Allow basename of dataset paths to match registered names#997
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📝 WalkthroughWalkthroughAdds local-filesystem dataset resolution: if a provided dataset identifier is a local path, derive a registered key from the path name, load the corresponding registered config and override its Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant DatasetUtils
participant Filesystem
participant Registry
participant DatasetLoader
Client->>DatasetUtils: get_dataset_samples(name_or_path, ...)
DatasetUtils->>Filesystem: os.path.exists(name_or_path)?
alt path exists
DatasetUtils->>DatasetUtils: dataset_name = last_segment(name_or_path)
DatasetUtils->>Registry: find registered key by substring(dataset_name)
alt registered key found
Registry-->>DatasetUtils: config
DatasetUtils->>DatasetUtils: config.path = name_or_path (override)
DatasetUtils->>DatasetLoader: load_dataset(config, split)
DatasetLoader-->>Client: samples
else no registered key
DatasetUtils->>DatasetLoader: load_from_local_path(name_or_path)
DatasetLoader-->>Client: samples
end
else not a path
DatasetUtils->>Registry: resolve registered key or auto-detect
alt resolved
Registry-->>DatasetUtils: config
DatasetUtils->>DatasetLoader: load_dataset(config, split)
DatasetLoader-->>Client: samples
else auto-detect
DatasetUtils->>DatasetLoader: auto_detect_and_load(name_or_path)
DatasetLoader-->>Client: samples
end
end
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Around line 248-254: The code overwrites dataset_name with a manually parsed
basename which breaks unregistered local dataset paths and is not OS-portable;
instead keep dataset_path as the full local path, do not replace dataset_name,
and use os.path.basename() for any display/name extraction. Update the block
around dataset_name/dataset_path so dataset_path stays set to the full path when
os.path.exists(dataset_name) is true, and later when building config (used by
load_dataset) use dataset_path (not the truncated basename) for the "path" entry
unless the dataset is found in SUPPORTED_DATASET_CONFIG; replace any
rstrip("/").split("/")[-1] logic with os.path.basename(dataset_path) if you need
just the name for registration/display.
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modelopt/torch/utils/dataset_utils.py
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get_dataset_samples now checks if any key in SUPPORTED_DATASET_CONFIG is a substring of dataset_name, so local paths like /hf-local/cais/mmlu correctly match the registered 'mmlu' config without requiring an exact name match. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Chenhan Yu <chenhany@nvidia.com>
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #997 +/- ##
==========================================
+ Coverage 72.13% 72.25% +0.11%
==========================================
Files 209 209
Lines 23631 23674 +43
==========================================
+ Hits 17046 17105 +59
+ Misses 6585 6569 -16 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Actionable comments posted: 2
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⚠️ Outside diff range comments (1)
modelopt/torch/utils/dataset_utils.py (1)
293-302:⚠️ Potential issue | 🟠 MajorUnregistered local datasets will fail: path contains only basename.
When a local path is provided but no registered key matches,
config["path"]is set todataset_name(line 298), which has been truncated to just the basename at line 258. This causesload_dataset()to fail because the full path is lost.For example, passing
/data/my_custom_datasetresults inconfig = {"path": "my_custom_dataset"}instead of the full path.Proposed fix
else: warn( f"Dataset '{dataset_name}' is not in SUPPORTED_DATASET_CONFIG. " "Auto-detecting format from column names." ) - config = {"path": dataset_name} + config = {"path": dataset_path if dataset_path is not None else dataset_name} splits = _normalize_splits(split) if split is not None else ["train"] def _preprocess(sample: dict) -> str: return _auto_preprocess_sample(sample, dataset_name, tokenizer)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@modelopt/torch/utils/dataset_utils.py` around lines 293 - 302, The code sets config["path"] to a truncated basename in the auto-detect branch, so local dataset paths (e.g., /data/my_custom_dataset) lose their full path and load_dataset() fails; fix by using the original full path variable (preserve the input path before any basename truncation) when creating config (i.e., set config["path"] = original_full_dataset_path or dataset_path instead of the truncated dataset_name), leaving _preprocess (which calls _auto_preprocess_sample) unchanged so auto-detection still works for unregistered datasets.
♻️ Duplicate comments (1)
modelopt/torch/utils/dataset_utils.py (1)
255-261:⚠️ Potential issue | 🟡 MinorUse
os.path.basename()for cross-platform compatibility.Line 258 uses
dataset_path.rstrip("/").split("/")[-1]which will fail on Windows where paths use backslash separators. Theosmodule is already imported.Proposed fix
if os.path.exists(dataset_name): # Local path dataset_path = dataset_name - dataset_name = dataset_path.rstrip("/").split("/")[-1] + dataset_name = os.path.basename(os.path.normpath(dataset_path)) else: dataset_path = None🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@modelopt/torch/utils/dataset_utils.py` around lines 255 - 261, The code that derives the dataset folder name uses manual string splitting (dataset_path.rstrip("/").split("/")[-1]) which is not cross-platform; replace that logic with os.path.basename(dataset_path.rstrip(os.sep)) (or simply os.path.basename(dataset_path) if you prefer) so dataset_name is computed portably; update the block that sets dataset_path/dataset_name in dataset_utils.py to call os.path.basename and remove the hardcoded "/" usage.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Line 262: The current selection logic uses a substring test when computing
registered_key (next((k for k in SUPPORTED_DATASET_CONFIG if k in dataset_name),
None)), which can produce incorrect matches (e.g., 'pile' matching
'minipile_100_samples'); change the match to a safe strategy such as exact
equality (k == dataset_name) or, if prefix/suffix matching is required, sort
keys by length descending and check for full-token or prefix matches to prefer
the most specific key; update the code that sets registered_key and any
downstream logic that assumes that key (referencing SUPPORTED_DATASET_CONFIG,
registered_key, and dataset_name) so the correct dataset configuration is always
chosen.
In `@tests/unit/torch/utils/test_dataset_utils.py`:
- Around line 86-108: The unit test test_get_dataset_samples_minipile_local_path
currently performs a network call via snapshot_download and belongs in
integration tests; change it to either move the test to tests/examples
(integration) or mock huggingface_hub.snapshot_download so the unit test is fast
and CPU-only, keeping the call to get_dataset_samples intact. Also add a new
unit test that creates a local directory whose basename does NOT match any key
in SUPPORTED_DATASET_CONFIG and calls get_dataset_samples with that local path
to exercise the auto-detection branch in dataset_utils.get_dataset_samples (the
code that handles unregistered local dataset paths), asserting it returns
expected text samples.
---
Outside diff comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Around line 293-302: The code sets config["path"] to a truncated basename in
the auto-detect branch, so local dataset paths (e.g., /data/my_custom_dataset)
lose their full path and load_dataset() fails; fix by using the original full
path variable (preserve the input path before any basename truncation) when
creating config (i.e., set config["path"] = original_full_dataset_path or
dataset_path instead of the truncated dataset_name), leaving _preprocess (which
calls _auto_preprocess_sample) unchanged so auto-detection still works for
unregistered datasets.
---
Duplicate comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Around line 255-261: The code that derives the dataset folder name uses manual
string splitting (dataset_path.rstrip("/").split("/")[-1]) which is not
cross-platform; replace that logic with
os.path.basename(dataset_path.rstrip(os.sep)) (or simply
os.path.basename(dataset_path) if you prefer) so dataset_name is computed
portably; update the block that sets dataset_path/dataset_name in
dataset_utils.py to call os.path.basename and remove the hardcoded "/" usage.
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modelopt/torch/utils/dataset_utils.pytests/unit/torch/utils/test_dataset_utils.py
| def test_get_dataset_samples_minipile_local_path(tmp_path): | ||
| """Test that get_dataset_samples resolves a local path for minipile_100_samples. | ||
|
|
||
| Downloads nanotron/minipile_100_samples via snapshot_download and passes the | ||
| local directory path to get_dataset_samples. This exercises: | ||
| - local path detection (os.path.exists branch) | ||
| - substring matching of 'minipile_100_samples' in SUPPORTED_DATASET_CONFIG | ||
| - the 'text' column preprocessor for the registered config | ||
| """ | ||
| from huggingface_hub import snapshot_download | ||
|
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||
| local_dir = tmp_path / "nanotron" / "minipile_100_samples" | ||
| snapshot_download( | ||
| repo_id="nanotron/minipile_100_samples", | ||
| repo_type="dataset", | ||
| local_dir=str(local_dir), | ||
| ) | ||
|
|
||
| samples = get_dataset_samples(str(local_dir), num_samples=5) | ||
|
|
||
| assert isinstance(samples, list) | ||
| assert len(samples) == 5 | ||
| assert all(isinstance(s, str) and len(s) > 0 for s in samples) |
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Move network-dependent test to integration tests or mock external dependencies.
This test downloads from HuggingFace Hub via snapshot_download, requiring network connectivity and violating the coding guideline that tests/unit/**/*.py must be fast CPU-based tests. The test belongs in tests/examples (integration tests) or should mock snapshot_download to avoid network calls.
Additionally, add a test for unregistered local dataset paths (where the basename does not match any registered key in SUPPORTED_DATASET_CONFIG). Currently, only the registered minipile_100_samples path is tested, leaving the auto-detection code path (lines 294–302 of dataset_utils.py) untested.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@tests/unit/torch/utils/test_dataset_utils.py` around lines 86 - 108, The unit
test test_get_dataset_samples_minipile_local_path currently performs a network
call via snapshot_download and belongs in integration tests; change it to either
move the test to tests/examples (integration) or mock
huggingface_hub.snapshot_download so the unit test is fast and CPU-only, keeping
the call to get_dataset_samples intact. Also add a new unit test that creates a
local directory whose basename does NOT match any key in
SUPPORTED_DATASET_CONFIG and calls get_dataset_samples with that local path to
exercise the auto-detection branch in dataset_utils.get_dataset_samples (the
code that handles unregistered local dataset paths), asserting it returns
expected text samples.
There was a problem hiding this comment.
@AAnoosheh , so this test likely needs to be moved to gpu_test. I think the basic torch test does not have datasets installed
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Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!
- Add minipile_100_samples to SUPPORTED_DATASET_CONFIG with text column preprocessor - Add unit test covering local path + substring matching via snapshot_download Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Chenhan Yu <chenhany@nvidia.com>
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Actionable comments posted: 1
♻️ Duplicate comments (2)
tests/unit/torch/utils/test_dataset_utils.py (1)
86-108:⚠️ Potential issue | 🟠 MajorThis unit test is still network-bound and won't catch the resolver bug.
snapshot_download()makestests/unit/...depend on the Hub, and the assertions only check for five non-empty strings. Because both thepileandminipile_100_samplesconfigs currently preprocesssample["text"], this can still pass when the resolver selects the wrong key. Please stubdatasets.load_dataset/Hub calls and assert the resolved kwargs instead (for example, that no inheritedname="v1.0"is passed), then add a second case whose directory basename is not inSUPPORTED_DATASET_CONFIGso the unregistered local-path branch is exercised too.Suggested direction
-def test_get_dataset_samples_minipile_local_path(tmp_path): +def test_get_dataset_samples_minipile_local_path(monkeypatch, tmp_path): - from huggingface_hub import snapshot_download - local_dir = tmp_path / "nanotron" / "minipile_100_samples" - snapshot_download( - repo_id="nanotron/minipile_100_samples", - repo_type="dataset", - local_dir=str(local_dir), - ) + local_dir.mkdir(parents=True) + + captured: dict[str, object] = {} + + def fake_load_dataset(*, split, streaming, **kwargs): + captured.update({"split": split, "streaming": streaming, **kwargs}) + return iter([{"text": f"sample-{i}"} for i in range(5)]) + + import datasets + monkeypatch.setattr(datasets, "load_dataset", fake_load_dataset) samples = get_dataset_samples(str(local_dir), num_samples=5) - assert isinstance(samples, list) - assert len(samples) == 5 - assert all(isinstance(s, str) and len(s) > 0 for s in samples) + assert samples == [f"sample-{i}" for i in range(5)] + assert captured["path"] == str(local_dir) + assert "name" not in capturedAs per coding guidelines,
tests/unit/**/*.py: Unit tests in tests/unit should be fast CPU-based tests taking no more than a few seconds to run.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tests/unit/torch/utils/test_dataset_utils.py` around lines 86 - 108, The test currently performs network I/O via snapshot_download and only asserts result strings; instead, mock/stub external Hub calls and datasets.load_dataset so the test runs offline and can assert how get_dataset_samples resolves kwargs: patch huggingface_hub.snapshot_download and datasets.load_dataset (or the internal call site used by get_dataset_samples) to return a fake local_dir and a fake dataset object, then call get_dataset_samples with that local path and assert the resolved kwargs passed into datasets.load_dataset (e.g., that no inherited name="v1.0" or incorrect config is forwarded and that the selected config equals the one matched from SUPPORTED_DATASET_CONFIG like "minipile_100_samples"); also add a second case where the directory basename is not present in SUPPORTED_DATASET_CONFIG to exercise the unregistered-local-path branch and assert datasets.load_dataset is called with the generic/local-path kwargs instead.modelopt/torch/utils/dataset_utils.py (1)
255-262:⚠️ Potential issue | 🟠 MajorKeep the full local path separate from the lookup key.
This block truncates a local path to
rstrip("/").split("/")[-1]and then resolves configs with substring matching. That breaks unregistered local datasets later on Lines 294-298 becauseload_dataset()receives only the basename, and it also makesminipile_100_samplesresolve to the earlierpileentry in the current config order. Preservedataset_pathas-is, derive a normalized basename for lookup, and match that key exactly.Suggested fix
- if os.path.exists(dataset_name): - # Local path - dataset_path = dataset_name - dataset_name = dataset_path.rstrip("/").split("/")[-1] - else: - dataset_path = None - - registered_key = next((k for k in SUPPORTED_DATASET_CONFIG if k in dataset_name), None) + dataset_path: str | None = None + dataset_key = dataset_name + if os.path.exists(dataset_name): + dataset_path = dataset_name + dataset_key = os.path.basename(os.path.normpath(dataset_path)) + + registered_key = next((k for k in SUPPORTED_DATASET_CONFIG if k == dataset_key), None) @@ - f"Dataset '{dataset_name}' is not in SUPPORTED_DATASET_CONFIG. " + f"Dataset '{dataset_key}' is not in SUPPORTED_DATASET_CONFIG. " "Auto-detecting format from column names." ) - config = {"path": dataset_name} + config = {"path": dataset_path or dataset_name} splits = _normalize_splits(split) if split is not None else ["train"] def _preprocess(sample: dict) -> str: - return _auto_preprocess_sample(sample, dataset_name, tokenizer) + return _auto_preprocess_sample(sample, dataset_key, tokenizer)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@modelopt/torch/utils/dataset_utils.py` around lines 255 - 262, The current code mutates dataset_name to the basename which loses the full local path and uses substring matching against SUPPORTED_DATASET_CONFIG; revert this by keeping dataset_path set to the original full path (do not overwrite dataset_name), derive a separate normalized basename (e.g., use os.path.basename(dataset_name.rstrip("/"))) for lookup, and compute registered_key by exact equality against entries in SUPPORTED_DATASET_CONFIG using that basename (not substring containment); ensure later calls such as load_dataset() are passed dataset_path when it exists (the full local path) and only use registered_key/basename for config lookup.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Around line 231-234: The docstring for the dataset_name parameter overstates
supported inputs (mentions .jsonl.gz and uses "mmlu" as an example) while the
implementation only special-cases .jsonl and SUPPORTED_DATASET_CONFIG does not
contain "mmlu"; update the docstring to accurately reflect current behavior by
removing .jsonl.gz from the supported list and replacing the "mmlu" example with
an actual registered key from SUPPORTED_DATASET_CONFIG (or a generic phrase like
"one of the keys in SUPPORTED_DATASET_CONFIG"); ensure you update the docstring
near the dataset_name parameter and reference SUPPORTED_DATASET_CONFIG so docs
and code match.
---
Duplicate comments:
In `@modelopt/torch/utils/dataset_utils.py`:
- Around line 255-262: The current code mutates dataset_name to the basename
which loses the full local path and uses substring matching against
SUPPORTED_DATASET_CONFIG; revert this by keeping dataset_path set to the
original full path (do not overwrite dataset_name), derive a separate normalized
basename (e.g., use os.path.basename(dataset_name.rstrip("/"))) for lookup, and
compute registered_key by exact equality against entries in
SUPPORTED_DATASET_CONFIG using that basename (not substring containment); ensure
later calls such as load_dataset() are passed dataset_path when it exists (the
full local path) and only use registered_key/basename for config lookup.
In `@tests/unit/torch/utils/test_dataset_utils.py`:
- Around line 86-108: The test currently performs network I/O via
snapshot_download and only asserts result strings; instead, mock/stub external
Hub calls and datasets.load_dataset so the test runs offline and can assert how
get_dataset_samples resolves kwargs: patch huggingface_hub.snapshot_download and
datasets.load_dataset (or the internal call site used by get_dataset_samples) to
return a fake local_dir and a fake dataset object, then call get_dataset_samples
with that local path and assert the resolved kwargs passed into
datasets.load_dataset (e.g., that no inherited name="v1.0" or incorrect config
is forwarded and that the selected config equals the one matched from
SUPPORTED_DATASET_CONFIG like "minipile_100_samples"); also add a second case
where the directory basename is not present in SUPPORTED_DATASET_CONFIG to
exercise the unregistered-local-path branch and assert datasets.load_dataset is
called with the generic/local-path kwargs instead.
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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: Chenhan Yu <chenhany@nvidia.com>
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Signed-off-by: Asha Anoosheh <aanoosheh@nvidia.com>
Signed-off-by: Asha Anoosheh <aanoosheh@nvidia.com>
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