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[OMNIML-6227] Convert GGML checkpoints to INT8 compressed-tensors for vLLM - #2903
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Every GGML weight is an integer level of at most 8 bits times the step of its 16- or 32-weight sub-block (IQ1: ±1/±7/±9 eighths, IQ2: ±8/±25/±43, Q8_0: its quants). Storing those levels as INT8 with the steps as group scales reproduces the GGML decoding up to rounding the scales to the model dtype, and stock vLLM serves it with its integer kernels (Marlin or Humming) instead of a BF16 fold. - GGMLFormat gains int8_group_size, unpack_int8 and decode_int8. Each format's torch decoder now multiplies out its own unpack_int8, so the integer view and the decoding cannot disagree. - convert_ggml_export_to_int8 rewrites a GGML HF export in place as a pack-quantized compressed-tensors checkpoint, one config group per group size, targeting layers by name; it validates before rewriting. - hf_ptq.py --export_ggml_as_int8 runs it after the export. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: converting GGML checkpoints to INT8 compressed-tensors checkpoints for vLLM. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns No listed security anti-pattern is introduced. The PR's changed modelopt and examples Python files contain no torch.load, numpy.load/np.load, allow_pickle, trust_remote_code, eval/exec, or # nosec usa… Full details: Security Anti-Patterns
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No listed security anti-pattern is introduced. The PR's changed modelopt and examples Python files contain no torch.load, numpy.load/np.load, allow_pickle, trust_remote_code, eval/exec, or # nosec usage. The converter uses safetensors safe_open and JSON parsing instead. The PR changes no pyproject.toml or requirements file, so it adds no dependency requiring license review.
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Review comments at @modelopt/torch/export/ggml_int8_export.py:
- Around line 53-54: Update the shard conversion flow around _convert_shard to
stage every converted shard into a temporary file before replacing any original
shard. Have _convert_shard return each temporary path and destination path, then
replace all staged files only after every conversion succeeds; preserve the
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CHANGELOG.rstexamples/hf_ptq/README.mdexamples/hf_ptq/hf_ptq.pymodelopt/torch/export/__init__.pymodelopt/torch/export/ggml_int8_export.pymodelopt/torch/quantization/ggml/common.pymodelopt/torch/quantization/ggml/iq1_m.pymodelopt/torch/quantization/ggml/iq1_s.pymodelopt/torch/quantization/ggml/iq2_s.pymodelopt/torch/quantization/ggml/iq2_xs.pymodelopt/torch/quantization/ggml/iq2_xxs.pymodelopt/torch/quantization/ggml/q8_0.pytests/gpu_vllm/torch/export/test_ggml_int8_export.pytests/unit/torch/export/test_ggml_int8_export.pytests/unit/torch/quantization/test_iq_formats.py
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… export Replace the post-export converter, which rewrote a written GGML checkpoint, with export_hf_ggml_int8_checkpoint(model, export_dir). Like export_hf_vllm_fq_checkpoint, it builds the state dict from the model: each GGML layer's pack() payload (the codes fake quant decoded, including GPTQ's pinned ones) becomes INT8 levels and scales, then save_pretrained writes it and the compressed-tensors config is added with the saved layer names. This writes once instead of writing GGML and rewriting it, leaves the GGML exporter untouched, and no longer depends on the on-disk payload layout or deletes hf_quant_config.json. Every enabled quantizer must be a GGML weight quantizer, checked before anything is written. hf_ptq.py now branches if --vllm_fakequant_export / elif --export_ggml_as_int8 / else export_hf_checkpoint, and rejects the flag with FSDP2, disk offload and layerwise.export_dir before calibrating. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Review comments at @modelopt/torch/export/plugins/ggml_int8_hf.py:
- Around line 98-101: Update _ggml_layers after collecting layers to validate
fused qkv_proj and gate_up_proj siblings before writing or returning them. For
each existing sibling module, require its sibling name to be present in layers
and its ggml_format.int8_group_size to match; otherwise raise ValueError, while
ignoring non-module siblings and preserving unsupported-layer handling.
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…xport flag
Drop --export_ggml_as_int8 and the in-memory exporter. Like the vLLM
fake-quant in-place export, it saved through save_pretrained, which fails
on offloaded quantized models ('SequentialHook' object has no attribute
'weights_map'), so it could not serve the large models INT8 is for.
The regular GGML export already handles offloading, FSDP2 and layerwise
export. examples/hf_ptq/convert_ggml_to_int8.py, a thin wrapper over the new
convert_ggml_export_to_int8(ggml_dir, output_dir), expands that export into
a new directory as INT8 compressed-tensors, streaming tensors into output
shards of bounded size. hf_ptq.py is back to main, and the README documents
the two steps.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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changed the title
The conversion has one caller, the example script, so it lives there instead of in modelopt.torch.export. Its tests move with it and load the script by path, like the other example-script tests. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Inline comments:
Review comments at @examples/hf_ptq/convert_ggml_to_int8.py:
- Around line 126-157: Update _write_int8_shards to clean up all generated
part-*.safetensors files in output_dir when tensor decoding or save_file fails
during the write phase, then re-raise the original exception. Keep successful
shard renaming and weight-map generation unchanged.
- Around line 147-149: Update the max_shard_size documentation and
--max_shard_size_gb CLI help to describe the value as a soft target, noting that
a tensor larger than the target is written to an oversized shard and it is not a
hard shard-size or memory bound.
- Around line 88-112: Update the GGML layer-format validation in
_ggml_layer_formats to detect fused q_proj/k_proj/v_proj and gate_proj/up_proj
sibling groups after building layer_formats and before returning it. For each
complete sibling set present in weight_map, compare the resolved int8_group_size
values and raise ValueError when they differ; leave incomplete or unresolved
groups unchanged.
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Changes requested: the converter silently drops checkpoint sidecars stored in subdirectories, despite promising to preserve non-weight files.
Needs action:
- Replace the top-level sidecar copy loop in
examples/hf_ptq/convert_ggml_to_int8.pywithcopy_non_model_files; retain off-index weight handling and add a nested-sidecar regression test.
No action needed:
- Integer unpacking and packing look consistent; the conformance and vLLM logprob tests provide meaningful numerical coverage.
- New file headers match
LICENSE_HEADER. - Tests were inspected, not executed.
| shards = sorted(set(weight_map.values())) | ||
| for path in ggml_dir.iterdir(): | ||
| # The shards are rewritten below, and the GGML metadata does not describe the result. | ||
| if path.is_file() and path.name not in {*shards, _INDEX, "hf_quant_config.json"}: |
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Bot comment.
This only visits immediate children and copies regular files, so an otherwise valid GGML export loses assets such as chat_templates/*.jinja or nested remote-code resources. The regular exporter preserves these through copy_non_model_files in modelopt/torch/utils/plugins/hf_checkpoint_utils.py; losing them here can change evaluation prompting or break loading. Please reuse that helper after writing the output-owned metadata, preserving the separate handling of off-index safetensors. Extend the conversion test with a nested sidecar and assert its bytes survive. The shared helper also supplies containment checks for checkpoint symlinks that this raw copy2 loop bypasses.
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Fixed in 2244ea4: the other files now go through copy_non_model_files, called after the converter writes its shards, index and config.json. copy_off_index_safetensors keeps handling off-index weight files separately. Subdirectories and the helpers' link checks are covered as a result. The conversion test now ships chat_templates/tool_use.jinja and an off-index mtp.safetensors, and checks that both arrive byte-for-byte.
cjluo-nv
changed the title
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| #### vLLM with GGML recipes (IQ, Q8_0) | ||
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| vLLM has no GGML kernels. To serve a checkpoint exported with a GGML recipe on vLLM's integer kernels, expand it into an INT8 weight-only compressed-tensors checkpoint with [convert_ggml_to_int8.py](./convert_ggml_to_int8.py): |
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I think vLLM has GGML kernels, but only supports SM120.
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Fixed in 7607678. Right, vLLM does have GGML kernels, for GGUF files. The README now names what it cannot load instead: the GGML checkpoints ModelOpt exports, which are Hugging Face checkpoints holding packed GGML blocks.
- Copy the GGML export's other files with copy_non_model_files and copy_off_index_safetensors, as the exporters do. The top-level loop dropped files in subdirectories such as chat_templates/, and bypassed the helpers' checks on links that point outside the checkpoint. - Empty the output directory if conversion fails, so a rerun is not refused over a partial checkpoint. - Document max_shard_size as a target: a larger tensor gets its own shard. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Reviewed the converter, the decoder refactor and the tests. The math looks right: the per-format _unpack_int8 plus dequantize_blocks is equivalent to the old torch decoders (the IQ1 _DELTA = 0.125 fold is a power of two, so exact), and the packing matches compressed-tensors. The nested-sidecar issue from the earlier bot review is fixed in the latest commit. Two points, one inline on the converter and the test layout below.
Test layout (see inline comments on both test files)
Neither tests/gpu_vllm/examples/ nor tests/unit/examples/ is a convention we follow for example-script tests; example tests live in tests/examples/<example>/ and use tests/_test_utils/examples/run_command.py::run_example_command to invoke the script, instead of loading it with importlib.util.spec_from_file_location. Suggested layout:
tests/examples/hf_ptq/test_convert_ggml_to_int8.pyfor the CPU conversion tests. It runs in the existing hf_ptq lane (already triggered byexamples/hf_ptq/**) and drives the real CLI:run_example_command(["python", "convert_ggml_to_int8.py", str(ggml_dir), str(int8_dir), "--max_shard_size_gb", "0.0002"], "hf_ptq"). That also coversmain()/argparse, which the in-process function call skips, and takes these tests out of the 20-minlinuxunit job. The refusal test becomespytest.raises(subprocess.CalledProcessError)plus the not-created assertion.tests/examples/vllm_serve/test_convert_ggml_to_int8.pyfor the vLLM logprob test.vllm_serveis the only example lane whose image has vLLM (the hf_ptq lane runs in the TRT-LLM image). Convert viarun_example_command(..., "hf_ptq")before constructingLLM(...). Noconftest.pyis needed since the test doesn't usecollective_rpc..github/workflows/example_tests.yml: addexamples/hf_ptq/convert_ggml_to_int8.pyto thevllm:files_yamlfilter (the torch lane does the same forexamples/hf_ptq/**), otherwise a PR touching only the converter would not trigger the vLLM lane.- The tiny IQ1_S/IQ2_XS export builder is duplicated across the two files; put it in
tests/_test_utils/examples/(e.g.ggml_export.py) and import it from both. - Delete
tests/gpu_vllm/examples/andtests/unit/examples/test_convert_ggml_to_int8.py.
Minor
- If the unit test stays in
tests/unit, check it against the <5s budget in CLAUDE.md (--durations): it builds two tiny-llama exports plusmtq.quantizeper test. - The converter only handles 2D weights (
weight_shape/pack layout). A 3D fused-expert payload would be written wrong; refuse non-2D payloads explicitly or note the limitation in the README. "ignore": []is fine because targets are explicit, but a README line saying so would help.
| ] | ||
| for name in payloads: | ||
| layer = name.removesuffix(".weight") | ||
| algo = layer_configs[layer]["quant_algo"] if mixed else quantization["quant_algo"] |
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In the MIXED_PRECISION case, any uint8 *.weight that is not listed in quantized_layers raises a bare KeyError here, after the output directory is created only later but with an unhelpful message. Since the point of this function is to refuse unsupported exports clearly, raise a ValueError naming the layer, e.g. if mixed and layer not in layer_configs: raise ValueError(f"{layer} is a uint8 weight not listed in quantized_layers.").
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Fixed in 7607678: _ggml_layer_formats now raises ValueError(f"{layer} holds a uint8 weight but is not in quantized_layers.") before anything is written. It also refuses a payload that is not a 2-D weight, the minor point from your summary.
| from modelopt.torch.export import export_hf_checkpoint | ||
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| _SCRIPT = Path(__file__).resolve().parents[3] / "examples" / "hf_ptq" / "convert_ggml_to_int8.py" | ||
| _SPEC = importlib.util.spec_from_file_location("convert_ggml_to_int8", _SCRIPT) |
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We don't keep example-script tests under tests/gpu_vllm/examples/. Please move this to tests/examples/vllm_serve/test_convert_ggml_to_int8.py (the vLLM example lane, which has vLLM in its image) and call the converter through run_example_command(..., "hf_ptq") from _test_utils.examples.run_command instead of loading it with importlib.util.spec_from_file_location. This also needs examples/hf_ptq/convert_ggml_to_int8.py added to the vllm: path filter in example_tests.yml. See the summary comment.
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Moved in 7607678 to tests/examples/vllm_serve/test_convert_ggml_to_int8.py, converting through run_example_command([...], "hf_ptq") before building LLM(...). examples/hf_ptq/convert_ggml_to_int8.py is now in the vllm: path filter, and the tiny-model builder is shared in tests/_test_utils/examples/ggml_export.py.
| from modelopt.torch.quantization.ggml import GGML_FORMAT_REGISTRY | ||
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| _SCRIPT = Path(__file__).resolve().parents[3] / "examples" / "hf_ptq" / "convert_ggml_to_int8.py" | ||
| _SPEC = importlib.util.spec_from_file_location("convert_ggml_to_int8", _SCRIPT) |
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Same here: move to tests/examples/hf_ptq/test_convert_ggml_to_int8.py and run the script as a CLI via run_example_command([...], "hf_ptq") rather than importing it by path. That exercises main()/argparse too, and keeps these tests out of the linux unit job. --max_shard_size_gb takes a float, so a tiny value gives the multi-shard case. The two tests that monkeypatch copy_non_model_files or call the function directly would need a different seam; the rollback test can instead make the output dir fail (e.g. a conflicting file) or stay as a function-level test in the same file.
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Moved in 7607678 to tests/examples/hf_ptq/test_convert_ggml_to_int8.py. The conversion and refusal tests run the CLI through run_example_command, with --max_shard_size_gb 0.0002 for the multi-shard case and subprocess.CalledProcessError for the refusal. The rollback test stays function-level in the same file: it patches copy_non_model_files to fail after the shards, index and config are written, and imports the script through hf_ptq_example_utils.
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Follow-up to my review: an alternative to item 2 (putting the vLLM test under Alternative: a new What it needs in CI:
Trade-offs:
Either works, and the CPU conversion tests belong in |
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Review comments at @examples/hf_ptq/convert_ggml_to_int8.py:
- Line 85: Update the converter’s call to copy_non_model_files in the conversion
flow to enable strict error propagation, while preserving the helper’s default
warning-and-continue behavior for other exporters. Ensure an OSError from
auxiliary-file copying causes this converter to fail by passing the helper’s
strict-mode option, and cover the behavior through the real helper with a
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…ible payloads The CPU tests now run convert_ggml_to_int8.py as a CLI from tests/examples/hf_ptq, and the vLLM test lives in tests/examples/vllm_serve, whose CI filter now includes the script. Both build their model with a shared helper in _test_utils. The converter refuses a payload that isn't a 2-D weight, or a uint8 weight missing from quantized_layers, with a clear error before writing anything. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Thanks @kevalmorabia97. 7607678 follows the layout from your review, items 1-5; details are in the inline replies.
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The vLLM image has no python binary, so the vllm_serve lane failed with FileNotFoundError before the test reached vLLM. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
### What does this PR do? Type of change: Bug fix `convert_ggml_to_int8.py` (#2903) read the scale dtype from the top-level `dtype` / `torch_dtype` of `config.json`. Multimodal configs such as GLM-5.3-Flash's keep it only in `text_config`, so converting a GGML export of GLM-5.3-Flash failed with `KeyError: 'torch_dtype'`. The converter now falls back to `text_config`. ### Usage Unchanged: ```bash python convert_ggml_to_int8.py <ggml_dir> <int8_dir> ``` ### Testing - New `test_dtype_is_read_from_text_config` in `tests/examples/hf_ptq/test_convert_ggml_to_int8.py` moves the tiny export's dtype into `text_config` and converts it. It fails on main with `KeyError: 'torch_dtype'` and passes with the fix. All 4 tests in the file pass locally (the new one runs in 0.2 s). - End to end with this patch: converted a GLM-5.3-Flash IQ2_S GGML export (122 GB → 350 GB INT8, 5.5 min) and served it on vLLM 0.31 at TP4 on GB300. Per-token NLL on GPQA and AA-LCR responses matches the BF16 fake-quant export within 0.0013 nats in every length bin. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A (the converter is unreleased) - Did you get Claude approval on this PR?: ❌ ### Additional Information Follow-up to #2903. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **Bug Fixes** * Int8 conversion now correctly handles exported models whose data type is specified within their text configuration, preserving the appropriate precision for converted weight scales. This prevents conversion from failing when the data type is not provided at the top level. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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What does this PR do?
Type of change: new feature, new example
Goal: evaluate GGML-quantized models (IQ formats, Q8_0) in vLLM on its integer kernels, at about half the BF16 weight memory, instead of
--vllm_fakequant_export's BF16 fold. No vLLM changes are needed.Why INT8 is exact: every GGML weight is an integer level of at most 8 bits times the step of its sub-block:
d·(2s+1))So an INT8 weight-only checkpoint with those steps as group scales (group 16 or 32) reproduces the GGML decoding, up to rounding the scales to the model dtype.
Changes:
GGMLFormatgainsint8_group_size,unpack_int8anddecode_int8. Each format's torch decoder now multiplies out its ownunpack_int8, so the integer view can't drift from the decoding. The format-specific bit unpacking moved intounpack_int8unchanged; the CUDA decoders are untouched.examples/hf_ptq/convert_ggml_to_int8.pyexpands a checkpoint exported with a GGML recipe into a new directory as apack-quantizedcompressed-tensors checkpoint:weight_packed, the steps inweight_scale(model dtype).re:targets.--max_shard_size_gbeach (a larger tensor gets its own), so host memory stays bounded. Other files, including subdirectories and off-index weight files, are copied with the exporters' helpers, and the GGML checkpoint is left unchanged. A failed conversion leaves the output directory empty.quantized_layers.hf_ptq.pyis unchanged. Conversion runs on the regular GGML export, so it works however that export was produced: with offloaded weights, FSDP2 orlayerwise.export_dir. The README's vLLM deployment section documents the two steps.Usage
Testing
New tests:
test_iq_formats.py::test_int8_view_matches_llama_cpp_on_captured_blocks: levels × steps reproduce llama.cpp's own decoding of blocks from a real checkpoint, bit-exactly, for all five IQ formats.tests/examples/hf_ptq/test_convert_ggml_to_int8.py(CPU; runs the script as a CLI throughrun_example_command):chat_templates/*.jinja) and an off-index weight file are copied byte-for-byte.tests/examples/vllm_serve/test_convert_ggml_to_int8.py: vLLM serves the converted tiny model (Marlin for group 32, Humming for group 16). Its prompt logprobs match the HF fake-quant model within 0.05; I observed at most 0.017. Giving each group its neighbour's scale makes the test fail at 1.16. The vLLM example job's path filter now includesconvert_ggml_to_int8.py, so this test runs when the script changes.tests/_test_utils/examples/ggml_export.py.Test runs (vLLM 0.32.0 container, torch 2.13, transformers 5.19):
vllm/vllm-openai:v0.30.0, CI's vLLM lane setup: the vLLM test passes.mainin that container; unrelated.test_q8_0_cuda.pyencoder tests fail the same way on the base commit in that container; unrelated.End to end with offloading: Qwen3.8-27B,
general/ptq/iq1_s(GPTQ, MLP only), on one H200.hf_ptq.pyexported it twice: once with most decoder layers offloaded to CPU (--use_seq_device_map --gpu_max_mem_percentage 0.15), once fully resident.convert_ggml_to_int8.py, both INT8 checkpoints are identical, all 1583 tensors and the config.Accuracy vs the BF16 fold: on B200, with the same recipe and calibration, through an earlier revision of this PR that packs each weight the same way. Each checkpoint was served by vLLM on one B200.
--vllm_fakequant_export(BF16 fold)general/ptq/iq2_xsserved on Humming gives PPL 7.7001.Throughput: one B300, TP 1, same vLLM config for every row. Decode uses 128-token prompts and 256 output tokens; prefill is 128 × 2048-token prompts with prompt logprobs.
VLLM_DISABLED_KERNELS=MarlinLinearKernel)Not run: Q8_0 through vLLM, and converting exports written by FSDP2 or
layerwise.export_dir. Those write the same checkpoint format, which is all the conversion reads.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).GGMLFormatgains two required fields. Every registered format sets them, and nothing outside the package constructs one.CONTRIBUTING.md: N/AAdditional Information
Why a separate script: earlier revisions of this PR added an
hf_ptq.pyflag, then an exporter working from the in-memory model. That exporter, likeexport_hf_vllm_fq_checkpoint(inplace_mem_efficient=True), saves withsave_pretrained, which fails on offloaded quantized models:'SequentialHook' object has no attribute 'weights_map'. Converting the regular GGML export avoids this and keepshf_ptq.pyunchanged. The cost is writing the GGML weights once more than strictly needed; IQ payloads are about a fifth of the INT8 size.🤖 Generated with Claude Code