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7
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Chenjie LuoandClaude Opus 5.5
|
2758c046fb |
Run the diffusers example scripts in the pytest process (#2728)
### What does this PR do?
Type of change: Tests (CI speed-up)
Follow-up to #2724. Each step of the diffusers example tests started a
fresh `python <script>.py` subprocess. On the tiny test models most of
each step was startup: about 20s importing torch/diffusers/modelopt and
initializing CUDA. For example, `quantize.py`'s own timer reports 3.1s
for the SDXL quantize run inside a ~30s step.
- **In-process runner**
(`tests/_test_utils/examples/diffusers_example_runner.py`). It plugs
into the existing `set_in_process_runner` hook, like the megatron_bridge
suite does, and runs the script with `runpy.run_path(...,
run_name="__main__")`. Argument parsing and `if __name__ == "__main__":`
blocks run exactly as on the command line; `diffusion_trt.py` swaps
`torch.nn.RMSNorm` only in that block. After each step it puts back the
process-wide state the script changed:
- sibling modules imported by bare name (`config`, `utils`, `quantize`,
...), with any same-named module imported elsewhere stashed and
restored;
- the `torch.nn.RMSNorm` swap;
- logger levels, plus the handlers `quantize.py` attaches to the
captured stdout;
- warning filters, the environment, the working directory and
`sys.path`.
- **Opt-in per module** with `pytestmark =
pytest.mark.usefixtures("in_process_examples")`: `test_diffusers.py`,
`test_export_diffusers_hf_ckpt.py` and `sparsity/test_sparsity.py`. The
deprecated ONNX/TRT test, which CI doesn't run, keeps using
subprocesses.
- **`torch.compile` steps stay in a subprocess.** They already pass
`env=`, which forces one, and that is where the Inductor settings and
cudagraphs belong. The expected fallback warning is filtered.
- **`examples/diffusers/quantization/{onnx_utils,calib}/__init__.py`.**
As namespace packages these dirs lose to any same-named regular module
anywhere on `sys.path`. Importing `onnxruntime.transformers` appends its
own directory, which holds an `onnx_utils.py`. Once that has happened in
a long-lived process, `from onnx_utils.export import ...` in
`diffusion_trt.py` and in the ONNX/TRT deprecation test resolves to
onnxruntime's module. A fresh subprocess never hit this. Making them
regular packages lets the example's own directory win. Both files
contain only the license header.
### Usage
N/A (tests only).
### Testing
I ran these on an RTX PRO 6000 in `nvcr.io/nvidia/pytorch:26.07-py3`,
set up like the CI lane (editable `.[onnx,hf,dev-test]`,
`diffusers==0.39.0`). Both runs used `pytest tests/examples/diffusers
--ignore=test_diffusers_onnx_trt.py --ignore=fastgen --cov`, with
`COVERAGE_PROCESS_START` set as in CI:
| | `main` (
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Chenjie LuoandClaude Opus 5.5
|
af07754681 |
Speed up the diffusers example tests (#2724)
### What does this PR do?
Type of change: Tests (CI speed-up)
The diffusers example lane spends ~29 min in pytest, almost all of it in
~45 example-script subprocesses on tiny models. This PR removes
redundant and avoidable work without dropping coverage:
- **Export HF checkpoints from runs that already happen.** The SDXL
int8/fp8 and Wan 2.2 14B transformer int8/fp8 configs in
`test_export_diffusers_hf_ckpt.py` used exactly the same arguments as
runs in `test_diffusers.py`. Those quantize runs now also pass
`--hf-ckpt-dir` (`hf_export=True`) and check the export, and the four
duplicate export tests are removed (30 → 26 tests in these files).
- **Calibrate from a local prompts file.** `quantize.py` loaded the Hub
prompt datasets on every calibration run. For Wan 2.2 that is all of
`nkp37/OpenVid-1M`, which cost ~30s per run. Tests now pass
`--prompts-file tests/examples/diffusers/calib_prompts.txt`, which also
removes a network dependency.
- **Tiny PixArt for `test_pixart_cachify`.** It downloaded the full-size
`PixArt-XL-2-1024-MS`. It now uses a tiny local PixArt-Alpha pipeline,
with the same tiny config as diffusers' own PixArt tests and built like
the existing tiny Wan 2.2 fixture. `cachify` still wraps both
transformer blocks. `PIXART_PATH` is removed because nothing else uses
it.
- **Cheaper `torch.compile` tests.** `diffusion_trt.py` compiles with
`mode="max-autotune"`. The compile tests now set
`TORCHINDUCTOR_MAX_AUTOTUNE_{GEMM,CONV}_BACKENDS=ATEN`, so Inductor
skips benchmarking Triton GEMM/conv templates. The `--torch-compile`
path still runs.
### Usage
N/A (tests only).
### Testing
I ran these on an RTX PRO 6000 (same GPU as the CI runner) in
`nvcr.io/nvidia/pytorch:26.07-py3`, set up like the CI lane: editable
`.[onnx,hf,dev-test]`, `diffusers==0.39.0`, apex uninstalled. Coverage
was on, the same way CI collects it (`COVERAGE_PROCESS_START`, `--cov`).
- Branch: `pytest tests/examples/diffusers
--ignore=test_diffusers_onnx_trt.py --ignore=fastgen --cov` → **40
passed in 22m37s**. fastgen needs `nemo_automodel`, which I didn't
install; this PR doesn't touch it.
- `main`, same machine: `test_diffusers.py`,
`test_export_diffusers_hf_ckpt.py`, `test_cache_diffusion.py -k "not
pixart"` → **29 passed in 28m21s**. This set is smaller than the branch
run: no sparsity, PixArt or deprecation tests (~1m50s on the branch).
Per-test call times (s), with CI from run 37945140337:
| Test | CI (main) | local main | local branch |
|---|---:|---:|---:|
| `test_wan22_quantization[*]` (7 tests, each) | 66–86 | 79–98 | 49–52 |
| `test_wan22_hf_ckpt_export` (2 tests) | 44 + 46 | 54 + 54 | removed |
| `test_diffusers_hf_ckpt_export[sdxl int8 / fp8]` | 43 + 37 | 32 + 31 |
removed |
| `test_pixart_cachify` | 86 | not run | 1.8 |
| `test_diffusion_trt_torch[sdxl_1.0_torch_compile]` | 132 | 156 | 119 |
| `test_diffusion_trt_torch[flux_schnell_torch_compile]` | 60 | 71 | 57
|
| `test_diffusion_trt_torch[sd3_medium_torch_compile]` | 51 | 58 | 49 |
On the same test set this saves about 7.5 min locally, plus about 84s
for PixArt. Scaled to CI timings, I expect the lane's pytest step to
drop from ~29 min to ~21 min.
The ATen restriction cuts the SDXL compile run from 108s to 63s without
coverage (run on its own). Coverage tracing of Inductor's Python
accounts for most of what remains.
`pre-commit` passes on the changed files.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: N/A (tests only)
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A (the tiny
PixArt config values mirror diffusers' own tests, as the existing tiny
Wan 2.2 helper does)
- Did you write any new necessary tests?: N/A
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A (tests only)
- Did you get Claude approval on this PR?: ❌
### Additional Information
Possible follow-ups not in this PR:
- Run fewer restore subprocesses: one per format instead of all 13,
about 3 min, but it gives up some coverage.
- Run the example scripts in-process like `megatron_example_runner.py`:
about 20s of startup saved per subprocess, but a larger change.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Tests**
* Expanded diffusion-model test coverage for PixArt-Alpha, including
cache behavior and checkpoint exports.
* Added validation that exported checkpoints include configuration and
model-weight files.
* Updated quantization tests to use local calibration prompts and cover
checkpoint export for selected SDXL and Wan 2.2 runs.
* Adjusted compiled test runs to use compatible autotuning settings.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Keval Morabia
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19e0121d0a |
Fix 2-GPU test_model_load_utils hang; test import and fixture cleanup (#2079)
### What does this PR do? Type of change: Bug fix + new tests (test-suite only; changes are confined to `tests/`) - **Fix `test_parallel_load_and_export` hang on 2 GPUs.** The temp paths were built from `os.getpid()` *inside* the workers, so each rank got a different `ckpt_dir`; rank 1 then took `_resolve_checkpoint_dir`'s Hub branch and blocked on an extra barrier while rank 0 ran the loader's broadcasts. The checkpoint is now built once in the parent under `tmp_path` and passed in, so all ranks agree on the path. - **Imports moved to module top** across `tests/gpu*` and `tests/examples`; function-local imports kept only where guarded (`importorskip`/`try`), where the import *is* the test (JIT compile), or where it must follow `sys.path` setup. - **Reuse `_test_utils` instead of local copies:** added `get_tiny_mixtral`; deduped `assert_nodes_are_quantized` (5 copies), the accelerate-offload/layerwise config helpers, `make_quant_attention`, `get_dflash_config`, the NVFP4 amax assertions, and 3 copies of the `tiny_wan22_path` fixture. - **Shared model-dir fixtures assert they were not modified** (`assert_unmodified_tree`): a file manifest is compared on teardown, so a test that writes into a session-scoped fixture directory fails instead of silently changing what later tests see. - **Dropped dependency guards the CI env already guarantees** (diffusers, tensorrt_llm in `gpu_trtllm`, transformer_engine in `gpu_megatron`, transformers in examples) so a missing dep fails loudly instead of skipping. - **`test_heterogenous_sharded_state_dict` is skipped on Blackwell** (sm_120), matching the existing marker and its TE/CUDA-13 rationale — same tracking issue as #1901. ### Testing Local, 2x RTX 6000 Ada: `tests/gpu/torch/utils/test_model_load_utils.py` passes on 2 GPUs and on 1 GPU (previously hung on 2). Also ran the touched files in `tests/unit` (608 passed) and `tests/gpu` (~340 passed). ### 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?: N/A — test-only PR - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A - Did you get Claude approval on this PR?: ❌ — not yet run <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Tests** * Added reusable validation utilities for generated files, quantization behavior, attention modules, model fixtures, offloading, and speculative decoding. * Expanded coverage for tiny Wan, Mixtral, Llama, and related model scenarios. * Consolidated duplicated setup and assertions across ONNX, GPU, quantization, export, and sparsity tests. * Improved fixture integrity checks, NVFP4 validation, and handling of identity inputs. * Reduced unnecessary dependency-based skips and isolated known platform-specific flakiness. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> |
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jingyu-mlandClaude Opus 4.8
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6b4ad85849 |
Qwen-Image diffusers PTQ: FP8 / NVFP4 / NVFP4-SVDQuant HF checkpoints (#1706)
### What does this PR do? Type of change: New feature Adds **Qwen-Image** (`Qwen/Qwen-Image`, `QwenImageTransformer2DModel`) to the diffusers quantization example and exports HuggingFace checkpoints in three precisions — **FP8**, **NVFP4**, and **NVFP4 + SVDQuant** — through the unified HF export. - Registers `--model qwen-image` (lazy diffusers import; no `trust_remote_code`). - Transformer-block-range recipe: quantizes only the linears under `transformer_blocks`, keeping the **first 2 / last 2** blocks (and everything outside `transformer_blocks`) in original precision. Applied **before** calibration so SVDQuant never mutates the excluded blocks. Expressed with the top-level `enable` `QuantizerCfgEntry` field (disable-all → re-enable `transformer_blocks` → disable first/last-N). - SVDQuant export (AWQ-style): promotes quantizer-owned tensors to clean module-level safetensors keys at export time — `weight_quantizer.svdquant_lora_a/b → <module>.svdquant_lora_a/b` and `input_quantizer._pre_quant_scale → <module>.pre_quant_scale` — with a documented `NVFP4_SVD` `quantization_config` (`group_size`, `has_zero_point: false`, `pre_quant_scale: true`, `lora_rank`). **Core SVDQuant quantization code (`modelopt/torch/quantization`) is unchanged.** - Shared export-path change — **intentionally global** (applies to all diffusers exports — SDXL / Flux / Wan, not just Qwen; the full export suite was verified green on GB200): `hide_quantizers_from_state_dict` now strips quantizer state from *all* modules (not just quant-linears) so calibrated norm-layer input quantizers no longer leak `input_quantizer._amax`. (An earlier `max_shard_size` workaround was dropped after merging `main`: #1794 makes the ComfyUI layerwise-metadata post-processing a no-op unless explicitly opted in, so a default sharded export no longer hits the unsupported-sharded path.) ### Usage ```bash python examples/diffusers/quantization/quantize.py \ --model qwen-image --override-model-path <Qwen-Image> --model-dtype BFloat16 \ --format fp4 --quant-algo svdquant --lowrank 32 \ --calib-size 64 --n-steps 20 \ --hf-ckpt-dir <out> # FP8: --format fp8 --quant-algo max # NVFP4: --format fp4 --quant-algo max ``` ### Testing - Focused unit + example tests pass on GB200 (sm_100): block-range recipe, `NVFP4_SVD` config schema, SVDQuant forward/fold (LoRA stays on `weight_quantizer`), Qwen dummy-input / strict-QKV-fusion / promotion, pipeline loading, and the diffusers HF-export test for Qwen FP8 / NVFP4 / SVDQuant. - Full `tests/examples/diffusers/test_export_diffusers_hf_ckpt.py` is green (SDXL, Flux, Qwen, Wan2.2) — confirms the shared export changes do not regress other models. - End-to-end on the real `Qwen/Qwen-Image` (~20B): all three formats export valid HF checkpoints — only `transformer_blocks` 2..57 quantized, nothing outside, no quantizer/`_amax` leak, correct `weight_scale`(`_2`)/`input_scale`, promoted SVDQuant keys (rank-consistent shapes), and the expected `quantization_config`. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ <!-- live-model LoRA storage unchanged; existing exports unaffected --> - 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?: ❌ <!-- new-example feature; add a CHANGELOG.rst entry if required --> - Did you get Claude approval on this PR?: ❌ <!-- run /claude review --> ### Additional Information All changes are confined to the diffusers example (`examples/diffusers/quantization`) plus the shared export path (`modelopt/torch/export`); the core quantization library is untouched. ### Follow-up (next step): fused-QKV SVDQuant for sglang / Nunchaku This export keeps attention `q/k/v` (and `add_q/k/v_proj`) as **separate** projections — the diffusers-native layout. That matches sglang's bf16 / FP8 / plain-NVFP4 paths (which also keep QKV separate) and ModelOpt/TRT-LLM consumers, so those load 1:1. sglang's **NVFP4-SVDQuant (Nunchaku)** path, however, builds a **fused** `to_qkv` with a *single* fused rank-r LoRA in Nunchaku-native format (`proj_down`/`proj_up`, `smooth_factor`, `wscales`/`wtscale`). Our per-projection tensors (`svdquant_lora_a/b` + `pre_quant_scale`; three independent rank-r decompositions) are not directly loadable there — and cannot be fused at load time, because the fp16 weight residual needed to derive a single fused rank-r is not preserved after export. **Planned next step:** an opt-in fused-QKV SVDQuant export mode that fuses q/k/v **before** SVDQuant calibration (yielding one rank-r over the fused weight) and emits a Nunchaku-compatible layout, enabling lower-latency fused-QKV inference in sglang. Tracked as a separate follow-up. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Qwen-Image (`QWEN_IMAGE`) model quantization and Diffusers export support * Added NVFP4_SVD (SVDQuant) export configuration support * Added transformer block-range quantization recipes (exclude first/last blocks) * **Bug Fixes** * Improved missing-pipeline error messaging for Qwen-Image * Prevented quantizer-related tensor/buffer leakage by promoting and cleaning quantizer outputs during export * **Tests** * Added Qwen-Image HF checkpoint export tests and offline fixtures * Added unit coverage for SVDQuant promotion/clean state-dict keys <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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Keval MorabiaandClaude Opus 4.8
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0081473861 |
Speed up slow unit/gpu/example tests (#1616)
### What does this PR do?
Type of change: test infrastructure / test speedups + CI stabilization
Make the test suite faster, `tests/unit` hermetic, and the CI lanes
stable, without losing coverage. Most changes are mechanical test/infra
edits; the buckets below cover the diff broadly.
**Unit tests — hermetic (no HF Hub):** toy local datasets/configs + the
local tiny tokenizer (with a checked-in chat template) replace Hub
assets; `tests/unit/conftest.py` enforces offline mode. Genuinely-HF
tests moved to `tests/gpu*` (e.g. the new
`tests/gpu/torch/utils/test_dataset_utils.py`). `CONTRIBUTING.md`
documents the hermetic-unit-test expectation.
**Unit-test speedups (no coverage loss):** speculative (disable CPU
torch.compile), calibrator (fewer histogram bins), ONNX conv/dynamo
(smaller shapes + representative subset), Ruler/sparse-attention (local
tokenizer), data-parallel autoquant (world size 4→2). Shared
`tiny_tokenizer` fixture. The distributed test helper now uses a private
`spawn` context instead of mutating the global start method (avoids
cross-test contamination).
**Rarely-used autonas/fastnas tests:** heavy parametrize cases marked
`@pytest.mark.manual`, one representative kept per test (fastnas
preferred); lighter sibling tests still cover core behavior. The legacy
FSDP1 NAS distributed test is also dropped: FastNAS/AutoNAS aren't used
with either FSDP1 or FSDP2, and FSDP1 is superseded by the newer FSDP2
API — so we keep a single FSDP2 case as a sanity check and drop FSDP1,
leaving the suite leaner.
**gpu_megatron:** deduplicate distributed worker pools by world_size
within a module (saves a redundant pool spin-up in multi-pool files;
module-scoped, no cross-module reuse).
**Example tests:** reduce per-test work via args that default to current
behavior (tests pass the fast values) — torch_onnx TRT optimization
level, diffusers calibration/inference steps, eagle `sample_size`,
megatron_bridge iters/calib, llm_sparsity data slice, export
safetensors-structure `calib_size`. Also enable the recently added
`gpt-oss` example tests in CI.
**Per-test timeouts:** `pytest-timeout` with a default per-directory
timeout (60s unit / 300s gpu+example) enforced in `tests/conftest.py`
(`timeout_func_only` in `pyproject.toml`), so a new test cannot silently
exceed the budget — an unmapped test dir crashes collection. A few
inherently slow tests carry explicit higher per-test overrides
(CUDA-compile, autotune, dflash).
**CUDA kernel pre-compilation:** a dedicated `tests/gpu/_extensions`
test JIT-builds the conv3d implicit-GEMM kernel up front (collected
before the functional tests in the same process) so the one-time build
cost no longer lands on — and time out — the first functional test that
uses it. Mirrored into the `llm_ptq`/`vlm_ptq` example lanes.
**Test relocation & optional-dependency guards:** vLLM sparsity plugin
test moved to `tests/gpu_vllm` (drops the in-test `importorskip`);
diffusers-dependent unit test guarded with `importorskip("diffusers")`
for partial-install lanes; `gpt_oss` example test dir renamed to
`gpt-oss` to match the CI matrix.
**Diffusers test models:** shared model-path constants in
`tests/_test_utils/examples/models.py` consolidated/renamed and point at
tiny `hf-internal-testing` test pipes (SDXL/SD3/FLUX) so
cachify/quantize/export tests run on toy weights; `local_id`s
normalized.
**Shared dataset utils:** `examples/llm_sparsity/.../hf_pts.py` now uses
`get_dataset_dataloader` (drops the bespoke cnn_dailymail-only
`get_calib_dataloader`; supports any registered/HF/JSONL dataset,
includes attention_mask); `data_prep.py` gains `--max_samples`.
**CI workflows:** container image bumps (pytorch 26.04→26.05, TRT-LLM
rc16→rc17) and tightened lane timeouts (unit 30→15 min, gpu lanes
trimmed, onnx example lane 45 min).
**Imports at top of file:** in-function imports across the test suite
are moved to module top per the coding guideline, conservatively —
optional deps stay guarded (in-function or behind a module-level
`importorskip`) in `tests/unit` since the partial-install lane runs
without them, and build/hardware-availability imports (apex, triton,
megatron/transformer_engine, tensorrt_llm) plus `_test_utils` lazy
guards are left in place.
**Kernel warning filters:** the repeated `filterwarnings` blanket-ignore
in six `tests/gpu/torch/kernels/**` modules is consolidated into a
scoped hook in `tests/gpu/torch/kernels/conftest.py` (kernel tests only
— the rest of the suite keeps surfacing warnings).
**Eagle example speedups:** `torch.compile` (eagle recipe default) added
~2 min to every eagle training test; it's now disabled in the eagle
example tests except one smoke (`test_llama_eagle3[1-False]`), and the
downstream resume / AR-validate / export tests point at the compile-free
checkpoint. Measured: `test_ar_validate` 139s→17s, offline training
142s→22s, streaming 140s→23s — the compile path is still smoke-tested
once.
**Example lanes install editable (`-e`):** so example scripts launched
as subprocesses resolve `modelopt` to the same source path as the test
process and reuse the pre-compiled CUDA-extension cache instead of
recompiling (~2 min/test); verified in the TRT-LLM container.
**Tiny test tokenizer:** `get_tiny_tokenizer` defaults to left padding
(what decoder-LM calibration expects) and ships a terse
generation-tagged chat template — replacing a verbose ChatML one that
inflated tokenized length on the 128-vocab tokenizer and broke the
offline-PTQ example tests' `max-seq-len` filter.
**Restored Hub-download coverage:** the live (ungated) HF dataset
round-trips exercising `get_dataset_samples`' download branch now live
in `tests/gpu/torch/utils/test_dataset_utils.py` (they had been dropped
from the hermetic unit file without a counterpart).
Individual file changes not explicitly called out above fall under this
general test/CI cleanup.
### Testing
Unit + the touched gpu_megatron files validated locally; example/GPU
lanes validated in CI.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (tests + example CLI args
default to prior behavior)
- If you copied code from any other sources or added a new PIP
dependency: N/A
- Did you write any new necessary tests?: N/A (optimizes/relocates
existing tests)
- Did you update Changelog?: N/A
- Did you get Claude approval on this PR?: ❌ (pending)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **Chores**
* Updated container image versions for PyTorch (26.04→26.05),
TensorRT-LLM (1.3.0rc16→1.3.0rc17), and ONNX/TensorRT (26.04→26.05).
* **Tests**
* Enhanced test isolation: unit tests now run hermetically without
HuggingFace Hub access.
* Optimized test runtime via smaller model/dataset parameters and
parallel test caching.
* Added CUDA extension availability tests and extended dataset utility
coverage.
* **Documentation**
* Updated testing guidelines in `CONTRIBUTING.md` to emphasize offline
test design.
* **Chores**
* Added pytest timeout configuration and improved CI/CD workflow
efficiency with editable installs.
<!-- 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 4.8 (1M context) <noreply@anthropic.com>
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jingyu-ml
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26ae8da517 |
[2/3] Implicit Gemm NVFP4 (#1227)
### What does this PR do? Type of change: new feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> - Add Conv3D implicit GEMM kernel with BF16 WMMA tensor cores and fused NVFP4 activation quantization for video diffusion VAE layers - Integrate into _QuantConv3d via QuantModuleRegistry — automatically dispatched when NVFP4 quantization is applied to nn.Conv3d - Move kernel from `experimental/conv/ to modelopt/torch/kernels/conv/`; move tests to `tests/gpu/torch/quantization/kernels/` ### Testing <!-- Mention how have you tested your change if applicable. --> - Added test cases to measure the difference between cuDNN and our CUDA implicit GEMM kernel - Added an NVFP4 fake quantization test using CUDA code ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ <!--- Mandatory --> - Did you write any new necessary tests?: ✅ <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Per-backbone quantization/export in a single run with per-backbone checkpoints and backbone-aware quant filters * Configurable NVFP4 block-size via CLI/config; improved NVFP4 Conv3D inference path and Wan 2.2 quantization support * **Bug Fixes** * Video-model calibration now respects extra params and forces video decoding during calibration * **Documentation** * Added comprehensive Conv3D implicit‑GEMM kernel documentation; removed experimental Conv3D prototype docs/benchmark * **Tests** * New Wan 2.2 quantization/export tests and expanded Conv3D/FP4 kernel test coverage <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com> |
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jingyu-ml
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2905cb0f2e |
Updated the diffusion config issue and more test cases (#937)
## What does this PR do?
**Type of change:** new tests, Bug fix <!-- Use one of the following:
Bug fix, new feature, new example, new tests, documentation. -->
**Overview:**
- **Fixed the INT8 config issue**
- **Add HF checkpoint export test coverage**
1. The `--hf-ckpt-dir` export path had zero test coverage. This MR adds
tests at two levels:
2. Unit tests (tests/unit/torch/export/test_export_diffusers.py):
- Extended test_export_diffusers_real_quantized to parametrize over
INT8, INT8 SmoothQuant, FP8, and FP4 configs
- (previously only FP8). This gives 3 models x 4 configs = 12 test
cases.
3. GPU integration tests
(tests/gpu/torch/export/test_export_diffusers_hf_ckpt.py)
- New file testing the full quantize.py --hf-ckpt-dir pipeline via
subprocess with 4 combos:
- SDXL INT8 smoothquant min-mean (the exact scenario that triggered the
bug)
- Flux INT8 smoothquant min-mean
- SDXL FP8
- Flux FP4
## Usage
<!-- You can potentially add a usage example below. -->
```python
# Add a code snippet demonstrating how to use this
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**:No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: Yes
- **Did you add or update any necessary documentation?**:No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No
<!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **Tests**
* Added test coverage for exporting Diffusers models with Hugging Face
checkpoints across multiple quantization formats (INT8, FP8, FP4)
* Extended quantization export testing to validate multiple
configuration scenarios
* **Chores**
* Refined INT8 quantization configuration with improved calibrator
support for convolution layers
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
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