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fix(profiler): size Hub checkpoints from file metadata - #17563
get_model_weight_size_from_hub called model_info() without files_metadata=True, so the Hub returned no file sizes and every model fell back to the parameter-count estimate, which overstates quantized checkpoints by 2–3×. Hub models are now sized from their checkpoint files.
Key Changes
About 50 lines in profiler/utils/model_info.py: request files_metadata=True and pick the weight files the way vLLM's default loader does. That means repo-root files of the first format present, minus PEFT adapters and training state, and only the files in model.safetensors.index.json when the repo has one (a small JSON download). If any selected file lacks a size, or the index can't be read (a gated repo without access, or a network error), the existing estimate is kept.
Validation
Repo
Before (MiB)
After (MiB)
TinyLlama-1.1B-Chat GPTQ (issue repro)
2099
733
Qwen2.5-7B-Instruct-AWQ
14526
5313
gpt-oss-20b (extra copies in original/, metal/)
39892
13124
Devstral-Small-2-24B (two shard sets)
24598
24598
Medical-llama-2 (PEFT adapter + .bin shards)
0
12853
The new tests in test_model_info.py cover the GPTQ case, repos that ship the weights twice (gpt2, Mistral, Devstral, a single file next to its own shards), an adapter or optimizer state next to full weights, and the fallbacks (missing size, missing shard, gated index, httpx/httpx2 network errors, no root weights). All 15 cases fail on main.
get_model_weight_size_from_hub called model_info() without
files_metadata=True, so sibling sizes were always None and the size
always came from the safetensors parameter-count estimate. That estimate
overstates packed quantized checkpoints (TinyLlama GPTQ: 2099 MiB vs a
733 MiB file; Qwen2.5-7B AWQ: 14526 vs 5313 MiB; gpt-oss-20b: 39892 vs
13124 MiB) and feeds the profiler's minimum-GPU search bound.
Request file metadata and count a single copy of the weights: only
repo-root files, the first format present in safetensors, bin, pt, pth
order, and only that format's shards when it has any. Without this,
repos that ship the weights twice (gpt2 bin + safetensors, Mistral
consolidated + shards, Llama and gpt-oss original/ folders) would be
counted two or three times. If any selected file has no size, keep the
existing parameter-count estimate rather than a partial sum.
Closesai-dynamo#16140
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
👋 Hi Alex-Wengg! Thank you for contributing to ai-dynamo/dynamo.
Just a reminder: The NVIDIA Test Github Validation CI runs an essential subset of the testing framework to quickly catch errors.Your PR reviewers may elect to test the changes comprehensively before approving your changes.
Hub weight sizing now requests file metadata and selects one root-level checkpoint copy by format priority. It uses the selected files’ sizes when all are available and falls back to the existing parameter-count estimate otherwise.
Changes
Hub checkpoint weight sizing
Layer / File(s)
Summary
Checkpoint selection and weight sizing components/src/dynamo/profiler/utils/model_info.py, components/src/dynamo/profiler/tests/unit/test_model_info.py
The sizing helper requests file metadata, selects root-level checkpoint files in format-priority order, and sums their sizes only when all selected sizes are available. Tests cover duplicate formats and copies, shards, packed GPTQ weights, incomplete metadata, and the estimate fallback.
Hub weight sizing can understate the model size for repositories that ship a LoRA adapter beside full weights, and overstate it for repositories that ship two sharded checkpoint copies. Both cases skew the profiler's minimum-GPU search bound. Fix the checkpoint selection for these layouts before merging.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title "fix(profiler): size Hub checkpoints from file metadata" states the main change: Hub model weight sizing now uses checkpoint file sizes from file metadata instead of the parameter-count esti…
Linked Issues check
Issue [#16140] requires Hub file metadata and a fallback estimate when checkpoint sizes are unavailable. get_model_weight_size_from_hub() calls model_info(..., files_metadata=True). It selects one…
Out of Scope Changes check
The changes stay within issue [#16140]. The implementation changes only Hub model sizing, and the added unit tests validate that behavior. The unchanged local-model sizing behavior is stated as follow…
Description check
The pull request description includes all required sections: Overview, Key Changes, Validation, Issues and Ongoing Work, the issue link, and the checklist. It provides clear implementation details and…
Full details: Title check
Explanation
The title "fix(profiler): size Hub checkpoints from file metadata" states the main change: Hub model weight sizing now uses checkpoint file sizes from file metadata instead of the parameter-count estimate. It is specific, concise, and follows a conventional commit style. It matches the changeset in model_info.py and the linked issue #16140.
Full details: Linked Issues check
Explanation
Issue [#16140] requires Hub file metadata and a fallback estimate when checkpoint sizes are unavailable. get_model_weight_size_from_hub() calls model_info(..., files_metadata=True). It selects one root-level checkpoint copy in the required format order, sums complete file sizes, and retains the safetensors parameter estimate for missing or unavailable sizes. The added tests cover the quantized GPTQ case, duplicate formats, shards, nested copies, and fallback behavior.
Full details: Out of Scope Changes check
Explanation
The changes stay within issue [#16140]. The implementation changes only Hub model sizing, and the added unit tests validate that behavior. The unchanged local-model sizing behavior is stated as follow-up work and is not an unrelated change in this pull request.
Full details: Description check
Explanation
The pull request description includes all required sections: Overview, Key Changes, Validation, Issues and Ongoing Work, the issue link, and the checklist. It provides clear implementation details and validation results.
Full details: Docstring Coverage
Explanation
Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 3 functions across 2 files.
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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Actionable comments posted: 2
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @components/src/dynamo/profiler/utils/model_info.py:
- Line 112: Update the shard selection that feeds `return shards or files` so it
returns only one complete checkpoint set when multiple sets share an extension.
Group shards by checkpoint stem or select them using one index’s weight_map,
while preserving the `files` fallback.
- Line 105: Update the checkpoint selection around the root_files filtering so
it identifies the full model checkpoint from its index or recognized model
filenames before applying extension priority. Exclude adapter weights such as
adapter_model.safetensors when full-model weights are present, ensuring the
reported size reflects the model checkpoint rather than the adapter.
After applying the fix, consider running `coderabbit review --agent` for local
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Address CodeRabbit review on ai-dynamo#17563. A repo can ship two shard sets of
one format (RedHatAI/Devstral-Small-2-24B-Instruct-2512 has consolidated-*
and model-* safetensors), which were summed to twice the model size. A
PEFT adapter_model.safetensors next to full PyTorch shards
(patikop/Medical-llama-2) won the format priority, so the 128 MiB adapter
was reported instead of the 12.6 GiB model.
Skip adapter_model.* files, group files of the chosen format by name with
the shard suffix removed, and use the largest group.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
…tate
Address review on ai-dynamo#17563. The largest-group fallback could pick training
state over the model: an Accelerate checkpoint stores pytorch_model.bin
next to an optimizer.bin about twice its size. Within the chosen format,
use the transformers default names (model, pytorch_model) when present
and keep the largest group only as the fallback.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
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Previously reported defects still present:
Original discussion: The underlying defect remains for non-default checkpoint names. The new model/pytorch_model preference fixes the Accelerate example, but the fallback still selects the largest arbitrary stem and applies extension priority first. A repository with consolidated.* weights plus a larger or preferred-format optimizer.* can therefore size training state instead of model weights, overstating the profiler's GPU bound. Exclude optimizer/scheduler/training artifacts and identify checkpoint files from recognized names or an index weight map before selecting a format/group.
Address review on ai-dynamo#17563. Grouping files by name kept producing new edge
cases: a single model.safetensors next to its own shards was summed, and
non-default names could still pick training state. Mirror vLLM's default
loader instead: repo-root files of the first format present, minus PEFT
adapters and training state (filter_files_not_needed_for_inference), and
only the files in model.safetensors.index.json when the repo has one.
The index is a small JSON download. If it cannot be read, as for a gated
repo without access, or lists a file the repo lacks, keep the
parameter-count estimate. Drop the gpt-oss and Llama test rows, which no
longer exercise anything the other cases do not.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
…rk error
Address tedzhouhk's review on ai-dynamo#17563. huggingface_hub 1.x raises httpx
transport errors such as ConnectError and ReadTimeout, which are not
OSError, so a failed index GET escaped to the outer handler and the size
query raised instead of using the safetensors estimate. Also catch
httpx.HTTPError. The import is guarded because ai-dynamo does not declare
httpx, and older huggingface_hub raises requests errors, which are
OSError.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
…errors
Address tedzhouhk's review on ai-dynamo#17563. huggingface_hub 2.x uses httpx2,
whose ConnectError and ReadTimeout are neither OSError nor httpx errors,
so a failed index GET still raised there. Collect HTTPError from each of
httpx and httpx2 that is installed, and run the transport-error tests for
both clients; each stack skips the client it does not have.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
…errors
Address tedzhouhk's review on ai-dynamo#17563. huggingface_hub 2.x uses httpx2,
whose ConnectError and ReadTimeout are neither OSError nor httpx errors,
so a failed index GET still raised there. Collect HTTPError from each of
httpx and httpx2 that is installed, and run the transport-error tests for
both clients; each stack skips the client it does not have.
Signed-off-by: Alex-Wengg <hanweng9@gmail.com>
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Overview
get_model_weight_size_from_hubcalledmodel_info()withoutfiles_metadata=True, so the Hub returned no file sizes and every model fell back to the parameter-count estimate, which overstates quantized checkpoints by 2–3×. Hub models are now sized from their checkpoint files.Key Changes
About 50 lines in
profiler/utils/model_info.py: requestfiles_metadata=Trueand pick the weight files the way vLLM's default loader does. That means repo-root files of the first format present, minus PEFT adapters and training state, and only the files inmodel.safetensors.index.jsonwhen the repo has one (a small JSON download). If any selected file lacks a size, or the index can't be read (a gated repo without access, or a network error), the existing estimate is kept.Validation
original/,metal/).binshards)The new tests in
test_model_info.pycover the GPTQ case, repos that ship the weights twice (gpt2, Mistral, Devstral, a single file next to its own shards), an adapter or optimizer state next to full weights, and the fallbacks (missing size, missing shard, gated index,httpx/httpx2network errors, no root weights). All 15 cases fail onmain.Issues and Ongoing Work
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