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Encode each GPU's imatrix weights in its own thread - #3043
After its forward, imatrix encodes every target weight once, one weight after another. In the default single-pass run of a model that device_map spreads over several GPUs, each encode runs on its weight's GPU. One more thing serializes them: decoding the payload reads its shape to the host, which waits for that GPU. So all but one GPU sat idle.
Weights that are readable as they stand are now grouped by device and encoded by one thread per GPU, each on the GPU the weight already lives on. Nothing moves between devices, so the codes are the same as before.
Weights that need a materialization window are still encoded in turn inside it: offloaded weights, FSDP2 shards and TP-sharded linears (requires_weight_materialization, is_quantized_parallel_linear).
The first encode runs before the workers start, so a format's CUDA extension, which is not thread-safe to load, loads once.
Worker threads turn off autograd themselves, since grad mode is per thread.
With weights on one GPU, as in a layerwise run that holds one layer at a time, nothing changes.
This is independent of #3002, which moves GPTQ expert updates to other GPUs. Here every weight stays where it is.
Usage
No API change.
Testing
Synthetic model, 192 bf16 4,096 x 4,096 linears over 3 RTX PRO 6000s, IQ2_XXS imatrix, warm extension: the encode phase takes 3.2 s on main and 1.2 s (2.7x) with this PR.
Real models on 4x GB300 (oci-jhb): single-pass IQ2_XXS imatrix, 1,024 x 512 calibration tokens, the model spread over the 4 GPUs by device_map. Encode phase, minus the one-time JIT build of the GGML extension that falls in the first encode (128-144 s in each run):
Qwen's smaller expert weights leave more of each encode to Python launch work, which the threads share under the GIL. Qwen's KL is identical token by token (0.287096). GLM's moves within its run-to-run noise (0.7221 -> 0.7209, paired -0.0011 ± 0.0023, p 0.32), because its forward, and with it the statistics, varies between runs.
New 2-GPU test test_imatrix_encodes_each_gpus_weights_at_once (tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py). It quantizes four layers split over two GPUs and the same layers on one GPU. Their weights must be bit-identical, and encodes on the two GPUs must overlap in time. It fails when the encodes run in turn.
tests/unit/torch/quantization/{test_ggml_imatrix,test_ggml_gptq}.py and plugins/test_fused_experts.py: 152 passed.
CI's GPU runner has one GPU, so it skips the new test; I ran it on 2 local GPUs. Not run: offloaded or FSDP2 imatrix with this change, whose weights take the unchanged sequential path.
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modelopt/torch/quantization/model_calib.py
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Walkthrough
The calibration flow groups eligible weights by device and encodes them concurrently when multiple CUDA devices have work. A GPU test compares quantized weights with a single-GPU reference and checks for overlapping encoding intervals.
Changes: GGML imatrix encoding
Layer / File(s)
Summary
Weight eligibility and access contexts modelopt/torch/quantization/model_calib.py
Weights that need materialization, including quantized parallel linears, are encoded inside their weight-access context. Other eligible weights are grouped by device.
Per-GPU encoding and validation modelopt/torch/quantization/model_calib.py, tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py
Encoding runs sequentially for non-CUDA items and single-GPU workloads. When multiple GPUs have work, the first encode runs before worker threads process the remaining per-GPU work. The test checks weight equality and overlap between encoding intervals.
sequenceDiagram
participant Calibration as imatrix_calibrate
participant Encoder as GGMLImatrixEncoder
participant Workers as ThreadPoolExecutor
participant GPU0 as First CUDA GPU
participant GPU1 as Other CUDA GPU
Calibration->>Encoder: Encode first item
Encoder->>GPU0: Run encoding
Calibration->>Workers: Submit remaining GPU work
Workers->>Encoder: Encode assigned items
Encoder->>GPU1: Run encoding
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Possibly related PRs
NVIDIA/Model-Optimizer#3044: Changes the imatrix statistics reduction that populates targets, which imatrix_calibrate reads to compute second moments before encoding.
Merge Risk: Low · up to 24805
Multi-GPU encoding can fail when workers modify inference tensors. Preserve the caller’s inference mode in workers before merging for that use case.
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The title "Encode each GPU's imatrix weights in its own thread" matches the main change. The PR adds per-GPU parallel encoding for imatrix weights that can be read in place, with one worker thread per GPU. The title is specific and short. It does not mention the sequential fallback for materialized weights, but that detail is secondary and the title does not conflict with it.
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Inline comments:
Review comments at @modelopt/torch/quantization/model_calib.py:
- Line 2750: Update the worker execution in imatrix_calibrate to capture the
caller’s inference-mode state before starting workers and apply inference_mode
in each worker when enabled; otherwise retain no_grad, since
inference_mode(False) would re-enable grad.
Review comments at @tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py:
- Around line 52-83: Update the timed_encode wrapper in
test_imatrix_encodes_each_gpus_weights_at_once to record each span’s end time
immediately after encode completes and CUDA synchronization finishes, before the
artificial sleep, so the overlap assertion measures encoding rather than the
delay.
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Minor · Functional Correctness · Quick win
Supported by static analysis
Script executed:
sed -n '1,110p' tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py
Repository: NVIDIA/Model-Optimizer
Length of output: 3122
Script executed:
set -eu
printf'%s\n''--- encoder definition and references ---'
rg -n -F --glob '*.py' -e 'class GGMLImatrixEncoder' -e 'GGMLImatrixEncoder' -e 'ThreadPoolExecutor' -e 'threading' modelopt tests/gpu/torch/quantization
printf'%s\n''--- encoder implementation ---'
python3 - <<'PY'from pathlib import Pathp = Path('modelopt/torch/quantization/ggml/imatrix.py')text = p.read_text().splitlines()for i, line in enumerate(text, 1): if 'class GGMLImatrixEncoder' in line: start = max(1, i - 10) end = min(len(text), i + 180) for n in range(start, end + 1): print(f'{n:5} {text[n-1]}') breakPYprintf'%s\n''--- changed-file callers and relevant quantization path ---'
rg -n -F --glob '*.py' -e 'imatrix' -e 'encode(' modelopt/torch/quantization/ggml modelopt/torch/quantization | head -240
Repository: NVIDIA/Model-Optimizer
Length of output: 13737
Script executed:
set -eu
printf'%s\n''--- encoder definition and references ---'
rg -n -F --glob '*.py' -e 'class GGMLImatrixEncoder' -e 'GGMLImatrixEncoder' -e 'ThreadPoolExecutor' -e 'threading' modelopt tests/gpu/torch/quantization
printf'%s\n''--- encoder implementation ---'
python3 - <<'PY'from pathlib import Pathp = Path('modelopt/torch/quantization/ggml/imatrix.py')text = p.read_text().splitlines()for i, line in enumerate(text, 1): if 'class GGMLImatrixEncoder' in line: start = max(1, i - 10) end = min(len(text), i + 180) for n in range(start, end + 1): print(f'{n:5} {text[n-1]}') breakPYprintf'%s\n''--- quantization path references ---'
rg -n -F --glob '*.py' -e 'GGMLImatrixEncoder' -e 'ThreadPoolExecutor' -e 'threading.Thread' -e 'encode(' modelopt/torch/quantization/ggml modelopt/torch/quantization
Repository: NVIDIA/Model-Optimizer
Length of output: 8064
Script executed:
set -eu
python3 - <<'PY'from pathlib import Pathp = Path('modelopt/torch/quantization/model_calib.py')lines = p.read_text().splitlines()for start, end in ((2622, 2762), (446, 482)): print(f'--- modelopt/torch/quantization/model_calib.py:{start}-{end} ---') for n in range(start, min(end, len(lines)) + 1): print(f'{n:5} {lines[n-1]}')PYprintf'%s\n''--- relevant calibration callers ---'
rg -n -F --glob '*.py' -e 'imatrix_calibrate(' -e 'ImatrixCalibration' -e '"imatrix"' modelopt/torch/quantization/mode.py modelopt/torch/quantization/model_calib.py tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py
Repository: NVIDIA/Model-Optimizer
Length of output: 10494
Record the encode interval before the artificial delay.
spans ends after time.sleep(0.2). One worker can finish encode and enter the delay before another worker starts encode, while the recorded spans still overlap. The assertion can therefore pass when encoding is serialized.
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only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at @tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py around
lines 52 - 83:
Update the timed_encode wrapper in
test_imatrix_encodes_each_gpus_weights_at_once to record each span’s end time
immediately after encode completes and CUDA synchronization finishes, before the
artificial sleep, so the overlap assertion measures encoding rather than the
delay.
After applying the fix, consider running `coderabbit review --agent` for local
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❌ Patch coverage is 67.56757% with 12 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.46%. Comparing base (dfd89ce) to head (2480538). ⚠️ Report is 1 commits behind head on main.
imatrix encodes every target weight once after its forward, one weight
after another. In a single-pass run of a model that device_map spreads
over several GPUs, each encode runs on its weight's GPU, so all but one
GPU sat idle. Weights readable as they stand are now grouped by device
and encoded by one thread per GPU, on the GPU they already live on, so
the codes are the same. Weights that need a materialization window
(offloaded, FSDP- or TP-sharded) are still encoded in turn inside it.
The first encode runs before the workers start, so a format's CUDA
extension loads once.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Approving: weights stay on their own GPU and are encoded with the same inputs, so the codes are unchanged. Any weight whose access context would do real work still takes the old sequential path inside that window. The new 2-GPU test fails if the encodes run one after another.
No action needed.
I checked requires_weight_materialization together with is_quantized_parallel_linear. They cover every case where enable_weight_access_and_writeback does more than nothing: offloaded/meta weights, FSDP2 DTensors and HF TP linears. Skipping the window for other modules changes nothing, including the layerwise persistent_materialization case, where the hook's offload is already False.
The iter_weights_for_calibration implementations (dense, fused experts, transposed BMM experts, TE grouped) all yield views of live parameters. So encoding them after the loop still writes into the parameter.
55 added logic lines. The new test file's header matches LICENSE_HEADER.
…y token (#3008)
### What does this PR do?
Type of change: new feature (performance)
Transformers' `Qwen4ExpTextQSAIndexer` (Qwen3.8, model type `qwen4_exp`)
picks each query's attended tokens in a Python loop over every (sample,
query) pair. On Qwen3.8-Flash-Next that is 524K iterations, about 340 s,
per forward of 1,024 x 512 calibration tokens, on each of its 12
full-attention layers. Layerwise calibration runs each layer about
twice, so the loop alone adds about two hours to a calibration that
otherwise takes well under one.
A query keeps its `indexer_budget // indexer_compress_ratio` best
complete key blocks plus the incomplete tail. So when the keys hold no
more blocks than that, the query keeps every visible token, and the
indexer's output equals the visibility mask it was given. With
Qwen3.8-Flash-Next's 2,048-token budget, that covers every `--calib_seq`
up to 2,048.
- `modelopt/torch/models/qwen4_exp/qsa_indexer.py`:
`prepare_for_calibration` gives each indexer instance of the model a
forward that returns that mask directly when every visible key fits. It
binds it with `bind_forward_method`, so an Accelerate hook from a
`device_map` load still wraps it, bit-identical to the loop, and calls
the original forward otherwise. It still runs `index_qk_proj` once, as
the reference does, so a recipe that quantizes that projection's input
still calibrates it. With a cache, it appends the step's indexer keys
first. Nothing is patched at class level.
- `modelopt/torch/models/hf.py` dispatches `qwen4_exp` and
`qwen4_exp_text` to it, and now lets a model plugin define only the
hooks it needs, `prefers_bundled_code` included; until now each plugin
had to define all of them.
- `modelopt/torch/models/qwen4_exp/specs.py` registers the model type,
as every model package does. It records only `model_type` and
`min_transformers_version` (5.16, the first release with `qwen4_exp`);
the fused MoE layout is left undeclared, so export and quantization
behave as before for this model.
`hf_ptq.py` already calls `prepare_model_for_calibration`, so it picks
this up with no flag.
**Also carries a fix for #3043**, which merged before it could land
there. imatrix's per-GPU encode workers ran without the caller's
inference mode, which like grad mode is per thread. So encoding weights
that are inference tensors under `torch.inference_mode()` failed with
"Inplace update to inference tensor outside InferenceMode is not
allowed". Each worker now enters inference mode when the caller is in
it, and `no_grad` otherwise.
### Usage
No API change. Library users calling `mtq.quantize` directly can apply
it the same way:
```python
from modelopt.torch.models.hf import prepare_model_for_calibration
prepare_model_for_calibration(model) # Qwen3.8: skip the QSA indexer loop where it keeps every token
```
### Testing
- **Qwen3.8-Flash-Next on 4x GB300 (oci-jhb)**: single-pass IQ2_XXS
imatrix on the routed gate_up experts, 1,024 x 512 calibration tokens,
flash-linear-attention installed, no other patch. `main` here includes
#3043, run twice:
| | Statistics forward | Whole job | KL to BF16 |
|---|---|---|---|
| `main`, two runs | 4,821 s / 4,493 s | 2:03:05 / 1:55:53 | 0.288507 /
0.287096 |
| This PR | **226 s** | **28:56 (4.0-4.3x)** | 0.287096 |
The loop costs 71-77 min per forward over the calibration data. The rest
of the 87-94-minute gap is the batch-size probe and the KL-scoring
forward, which also run it on `main`. A layerwise run forwards each
layer about twice, so it saves about twice the forward time; earlier
layerwise runs measured 374 s instead of 33 s per pass on each
full-attention layer.
This PR's KL is identical token by token to `main`'s second run, and to
two runs on the code before #3043. `main`'s first run differed (+0.0014
± 0.0017, p 0.11), but its rerun did not, so that one 80-minute
reference-loop forward varied. These runs predate the projection fix,
which adds one small linear per full-attention layer.
- **Layerwise GPTQ equivalence:** this PR alone vs `main` with the
class-level patch our campaign used, 45:15 vs 45:43 layerwise, KL
0.249200 identical per token.
- New CPU test `test_qsa_indexer_fast_path_matches_the_reference`
compares the patched indexer with Transformers' reference on bool and
float masks, left and right padding, an incomplete key tail (62 tokens),
and with a real indexer cache, whose accumulated keys must also match.
Within the budget it fails if the loop runs, through the class `forward`
or through the forward the plugin saved. Over the budget it checks that
the loop's result comes back. It also counts `index_qk_proj` calls,
which must be one as in the reference.
- New CPU test
`test_qsa_indexer_decoding_past_the_budget_matches_the_reference`:
prefill 30 tokens, then decode one token at a time to 42 with a 32-token
budget, so the indexer moves from the fast path into the loop at 36
keys. Every step and the final cached keys match an unpatched copy.
- New CPU test
`test_qsa_indexer_fast_path_runs_inside_an_accelerate_hook`: a real
Accelerate hook attached before preparation still runs around the fast
path.
- New CPU tests for the review's robustness points: a call with an
argument the fast path does not know goes to the reference
(`test_qsa_indexer_hands_unknown_arguments_to_the_reference`), a Qwen3.8
model with no recognized indexer warns
(`test_a_qwen3_8_model_without_a_known_indexer_warns`), and a plugin
exporting a misspelled hook is refused
(`test_a_plugin_exporting_an_unknown_hook_is_refused`).
`models/README.md` documents the four optional plugin hooks.
- Planting each bug these tests target makes them fail: assigning
`forward` directly, always delegating to the loop, using the fast path
past the budget, or skipping the cache update.
- New CPU test
`test_loading_hooks_the_plugin_lacks_keep_their_defaults`: for
`qwen4_exp` and `qwen4_exp_text`, `prefers_bundled_code` and
`checkpoint_has_mtp` return their defaults. Without the optional-hook
fix, `prefers_bundled_code` raises `AttributeError`.
- `tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py` now runs with
and without inference mode; on 2 GPUs the inference-mode case fails
without the #3043 fix and passes with it.
- `tests/unit/torch/models/`,
`tests/examples/hf_ptq/test_hf_ptq_args.py` and `test_example_utils.py`:
303 passed. `tests/unit/torch/quantization/test_ggml_imatrix.py` and
`plugins/test_fused_experts.py` pass too.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (bit-identical outputs; the
dispatcher accepts the existing plugin unchanged)
- 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)?:
✅
- Did you get Claude approval on this PR?: ❌
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
### What does this PR do?
Type of change: new feature (performance)
In a layerwise GPTQ run of a model that `device_map` spreads over
several GPUs, only the GPU holding the current layer did that layer's
work. The fused-MoE expert updates, a large share of each MoE layer's
time, ran one after another on that GPU while the others sat idle.
The updates of one layer's experts are independent. For helpers whose
update reads only the weight, its Hessian and the format's codec, the
expert updates now run in one worker thread per GPU this process already
uses that has room for an update. Each worker copies an expert's weight
and Hessian to its GPU, runs the update there and writes the result back
into the expert's weight. That covers the GGML formats when their CUDA
encoders are loaded (`GGMLGPTQHelper.updates_on_any_device()`); helpers
that read quantizer state from the weight's device, such as NVFP4's, and
GGML updates on the PyTorch fallback encoder, whose scratch grows with
`block_chunk_size`, keep running in turn.
- **Single process only.** In a multi-rank run, the GPUs this process
can see may belong to other ranks, so updates stay on the weight's
device. Hessian offloading from #2945 now takes its candidate GPUs from
the same helper.
- **Room check.** A GPU qualifies only if it has room for the largest
expert update: 8 Hessians (the Hessian, its inverse and their
temporaries), 2 float32 copies of the weight (the working copy and a
dtype-conversion temporary), up to 5 GPTQ blocks of columns (the block's
errors and up to four group-sized temporaries around each triangular
solve, each as large as the weight when the input is no wider than a
block), and 64 MiB for the CUDA codec's tables and scratch. Room is
measured with #2945's `get_available_gpu_memory`, which also counts
PyTorch's reusable cached blocks. With fewer than two qualifying GPUs,
nothing changes.
- **Lazy setup.** The first update runs in the caller's thread, so setup
that is not thread-safe, such as loading a format's CUDA extension,
happens once before the workers start.
- **Autograd.** Grad and inference mode are per thread, so worker
threads take the caller's, as the imatrix encode workers do.
- **Offload.** `gptq()` drops its expert weight slices before leaving
each module's weight window, so under offload they don't keep that
module's onloaded weights on the GPU while the next module's are
onloaded.
The results are bit-identical to the sequential updates.
`hessian_input_dtype` (#3019) applies while the Hessians accumulate,
before any update runs, so it is unaffected. #3043 runs imatrix encodes
one thread per GPU, each weight on its own GPU; this PR moves GPTQ
expert updates to other GPUs. Both now share one runner,
`run_on_each_gpu`, which runs the first item in the caller's thread and
gives each worker the caller's grad and inference mode.
### Usage
No API change.
### Testing
**Real models on 4x GB300 (oci-jhb).** Layerwise GPTQ on the routed
experts at IQ2_XXS, 1,024 x 512 calibration tokens, KL to BF16 over
wikitext-2 (100 x 512 tokens, second half scored). Measured on top of
#2945, which sets the batch size and Hessian placement these runs used:
| | Without this PR | With this PR | KL |
|---|---|---|---|
| GLM-5.3-Flash (batch 16), layerwise calibration | 41:12 | **35:41
(-13%)** | 0.6197 -> 0.6189 (-0.0009 +- 0.0037, p 0.82) |
| Qwen3.8-Flash-Next (batch 64), layerwise calibration | 21:08 | **18:23
(-13%)** | 0.249348, identical per token |
Qwen's KL is identical token by token. GLM's moves within run-to-run
noise: its Hessian placement varies between runs with free memory (for
example 222 vs 217 experts' Hessians on one GPU), which changes how they
are summed.
**Update step alone, 2x RTX PRO 6000.** One fused-MoE layer of 32
experts, IQ2_XXS, 64 expert updates:
| Expert shape (hidden x intermediate) | One GPU | Two GPUs | Peak per
worker GPU | Room check (block size 256) |
|---|---|---|---|---|
| 2,048 x 768 | 0.49 s | 0.32 s | 108 MiB | 224 MiB |
| 4,096 x 1,536 | 1.39 s | 0.89 s | 432 MiB | 687 MiB |
- New GPU test `test_expert_updates_on_another_gpu_match_one_gpu` (2
GPUs, with and without inference mode): expert updates spread over two
GPUs give bit-identical weights and codes to running them in turn on
one, with the codes pinned on the weight's GPU. Each expert input spans
two GGML groups, so the Hessian shapes what is compared. It fails when
the GGML helper's updates stay on their own GPU, when the codes are left
on the worker's GPU, and when a worker uses only the Hessian's diagonal.
- New unit test
`test_gptq_drops_each_fused_experts_weights_before_the_next_window`: no
updated expert weight slice outlives its module's weight window. It
fails without the fix.
- Room check, measured on 2x RTX PRO 6000 (peak allocated on the worker
GPU for one update, CUDA encoder): across 17 combinations of format
(IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, Q8_0), expert shape and block
size (256 to 2048), the peak is 29-63% of the room. For example, IQ2_XXS
65536x256 at block 256 peaks at 325 MiB (room 514), 16384x2048 at block
2048 at 361 MiB (1088), and 28672x4096 at block 1024 at 879 MiB (2032).
A Hessian-only bound was exceeded by every expert weight wider than its
input, and a bound without the block term by any input no wider than a
block.
- New GPU test `test_an_update_that_may_move_fits_its_room`: an update a
helper lets move peaks on the worker GPU within its room check (IQ2_XXS
16384x2048 and 65536x256, and IQ1_S 1024x512, on the CUDA encoder; IQ1_S
on the PyTorch fallback). It fails when fallback updates may move (IQ1_S
1024x512 peaks at 96 MiB against 76 MiB), with a Hessian-only bound, and
without the block term (IQ2_XXS 65536x256 peaks at 325 MiB against 194
MiB).
- A local script (not a committed test) ran this with
`hessian_input_dtype: bfloat16`. Every expert Hessian accumulation took
the bfloat16 GEMM, and the two-GPU updates matched one GPU bit for bit.
- After rebasing onto #3122 and addressing review:
`tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate,test_local_hessian_gpu,test_ggml_imatrix_gpu,test_iq_formats_cuda}.py`
117 passed on 2x RTX PRO 6000 (CI's GPU runner has one GPU and skips the
2-GPU tests).
`tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_ggml_imatrix,test_local_hessian,test_layerwise_calibrate}.py`
and `plugins/test_fused_experts.py` 250 passed on CPU (transformers
5.18).
- Full suites: locally at df3f78f, on H200 at 958a02d (958a02d
only adds the extension guards to two `test_gptq.py` tests, re-run
locally):
- `tests/gpu/torch/quantization` on 2x RTX PRO 6000 (torch 2.12,
transformers 5.18): 1050 passed, 35 skipped, 8 failed. 4
`test_quant_conv_gpu` wan cases fail on the local `diffusers`, which
lacks `autoencoder_kl_wan`. 4 `test_q8_0_cuda` cases fail the same way
at the base (55898ee) on these GPUs.
- `tests/gpu/torch/quantization` on 4x H200 NVL (computelab, TRT-LLM
1.4.0rc3 image: torch 2.14, transformers 5.5.4), split over two GPU
pairs: 989 passed, 41 skipped, 1 failed.
`test_fsdp2_gradient_qwen_moe_autoquant` fails the same way at the base
in this image (FSDP2 `set_model_state_dict` gets an empty state dict).
Not collected: `test_deepspeed` (no deepspeed) and two ONNX-export
modules. All of `test_gptq.py`, including the 2-GPU tests, passed.
- `tests/unit/torch/quantization` on CPU: 1595 passed, 1 skipped. Not
collected: three diffusers and ONNX-export modules, from stale local
packages.
- Not run: multi-rank runs, where the change does nothing; real models
after rebasing onto #3019, #3043 and #3122, which do not touch the
update step.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (no API change; bit-identical
results)
- 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 (speeds up GPTQ on fused MoE experts, which is unreleased)
- Did you get Claude approval on this PR?: ❌
🤖 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
* **New Features**
* GPTQ calibration can distribute eligible expert-weight updates across
multiple GPUs and run updates on a selected device.
* Calibration can choose Hessian storage and available GPUs based on
memory availability.
* **Bug Fixes**
* Expert-weight updates now keep packed quantized data aligned with the
expert’s device.
* Calibration tracks updated and unreached experts when processing
expert weights.
<!-- 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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shengliangxu
left a comment
What does this PR do?
Type of change: new feature (performance)
After its forward, imatrix encodes every target weight once, one weight after another. In the default single-pass run of a model that
device_mapspreads over several GPUs, each encode runs on its weight's GPU. One more thing serializes them: decoding the payload reads its shape to the host, which waits for that GPU. So all but one GPU sat idle.Weights that are readable as they stand are now grouped by device and encoded by one thread per GPU, each on the GPU the weight already lives on. Nothing moves between devices, so the codes are the same as before.
requires_weight_materialization,is_quantized_parallel_linear).This is independent of #3002, which moves GPTQ expert updates to other GPUs. Here every weight stays where it is.
Usage
No API change.
Testing
Synthetic model, 192 bf16 4,096 x 4,096 linears over 3 RTX PRO 6000s, IQ2_XXS imatrix, warm extension: the encode phase takes 3.2 s on
mainand 1.2 s (2.7x) with this PR.Real models on 4x GB300 (oci-jhb): single-pass IQ2_XXS imatrix, 1,024 x 512 calibration tokens, the model spread over the 4 GPUs by
device_map. Encode phase, minus the one-time JIT build of the GGML extension that falls in the first encode (128-144 s in each run):mainQwen's smaller expert weights leave more of each encode to Python launch work, which the threads share under the GIL. Qwen's KL is identical token by token (0.287096). GLM's moves within its run-to-run noise (0.7221 -> 0.7209, paired -0.0011 ± 0.0023, p 0.32), because its forward, and with it the statistics, varies between runs.
New 2-GPU test
test_imatrix_encodes_each_gpus_weights_at_once(tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py). It quantizes four layers split over two GPUs and the same layers on one GPU. Their weights must be bit-identical, and encodes on the two GPUs must overlap in time. It fails when the encodes run in turn.tests/unit/torch/quantization/{test_ggml_imatrix,test_ggml_gptq}.pyandplugins/test_fused_experts.py: 152 passed.CI's GPU runner has one GPU, so it skips the new test; I ran it on 2 local GPUs. Not run: offloaded or FSDP2 imatrix with this change, whose weights take the unchanged sequential path.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/A🤖 Generated with Claude Code
Summary by CodeRabbit