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Document installing flash-linear-attention for linear-attention models - #3003
Transformers runs the linear-attention layers of Qwen3-Next, Qwen3.5, Qwen3.8, GLM-5.x Flash, Kimi Linear and OLMo Hybrid through readable PyTorch reference kernels unless the flash-linear-attention kernels are importable. The reference path is slow and needs far more memory per sample, which caps the layerwise calibration batch. This adds a note to the hf_ptq README and one line to the ptq skill on installing the kernels: pip install fla-core.
fla-core alone is enough. The kernels Transformers looks for (fla.ops.kda.chunk_kda, fla.ops.gated_delta_rule.chunk_gated_delta_rule) live in fla-core, which depends only on einops. The flash-linear-attention package holds layers and models Transformers does not use, and pins transformers>=5.6.2,<6.
GPU only. Once the kernels are importable, Transformers uses them for every device, and a CPU forward of these models then fails (RuntimeError: 0 active drivers). That is also why the package is documented rather than added to examples/hf_ptq/requirements.txt, which the example CI installs.
Usage
pip install fla-core
Testing
Layerwise GPTQ on the routed experts at IQ2_XXS on 4x GB300 (oci-jhb), 1,024 x 512 calibration tokens, with #2945 and #3002, flash-linear-attention and fla-core 0.6.0:
Without the kernels
With them
GLM-5.3-Flash: batch the probe picks
16
64 (linear-attention layers fit 181-295 samples instead of 16)
With flash-linear-attention installed but not fla-core, nothing changed: Transformers could not import fla.ops.kda.chunk_kda and kept the reference path. With fla-core alone, it binds fla.ops.kda.chunk.chunk_kda for GLM-5.x and fla.ops.gated_delta_rule.chunk.chunk_gated_delta_rule for Qwen, and a tiny Qwen3-Next runs on GPU through them; on CPU it fails as described above.
The kernels compute slightly differently from the reference: the unquantized GLM-5.3-Flash under them is 0.018 KL from the reference logits, which were computed without them. KL to that reference after GPTQ: Qwen 0.2493 -> 0.2480 (not significant); GLM 0.6189 -> 0.6230 at batch 16 (+0.0041 +- 0.0045, p 0.36) and 0.6306 at batch 64 (+0.0118 +- 0.0050, p 0.02), paired per chunk over 100 x 512 wikitext-2 tokens.
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 (documents an optional third-party package; nothing added to requirements)
Added guidance to install fla-core>=0.5.2 before GPU calibration of supported models with linear-attention layers; otherwise, Transformers may use slower, memory-intensive reference kernels.
Warned that older fla-core versions can compute packed Gated DeltaNet sequences incorrectly, and that the optimized kernels can cause these models’ CPU runs to fail.
Included a GLM-5.3-Flash calibration timing and memory observation.
Transformers runs the linear-attention layers of Qwen3-Next, Qwen3.5,
Qwen3.8, GLM-5.x Flash, Kimi Linear and OLMo Hybrid through its PyTorch
reference kernels unless the flash-linear-attention kernels are importable.
Those kernels are in fla-core, which installing flash-linear-attention with
--no-deps leaves out. Note the install in the hf_ptq README and the ptq
skill.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Reviewing files that changed from the base of the PR and between 4bd40a0 and 58d6763.
Files selected for processing: 2
examples/hf_ptq/README.md
plugins/modelopt/skills/ptq/SKILL.md
Files skipped from review as they are similar to previous changes: 2
plugins/modelopt/skills/ptq/SKILL.md
examples/hf_ptq/README.md
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Walkthrough
The PTQ documentation adds guidance for linear-attention models. It states the fla-core version requirement, describes fallback kernel behavior and CPU limitations, and reports a GLM-5.3-Flash calibration observation.
Changes: Linear-attention PTQ guidance
Layer / File(s)
Summary
Kernel prerequisites and calibration guidance examples/hf_ptq/README.md, plugins/modelopt/skills/ptq/SKILL.md
The documentation names affected models and advises installing fla-core>=0.5.2 before GPU calibration. It notes that older versions compute packed sequences incorrectly, and that installing the package causes CPU runs of these models to fail. The README also reports a GLM-5.3-Flash calibration timing and memory observation.
NVIDIA/Model-Optimizer#2455: Vendors GatedDeltaNet kernels and specifies a flash-linear-attention dependency for the same linear-attention kernel context.
Suggested reviewers:shengliangxu, kevalmorabia97
Merge Risk: Minimal · up to 58d67
The guidance now gives a versioned installation command and warns that these kernels do not support CPU runs. No actionable issue remains before merge.
Pre-merge checks: 6
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The title clearly describes the main change: adding installation guidance for linear-attention kernels. It uses the broader term "flash-linear-attention" instead of the documented package name "fla-core", but it remains related and understandable.
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Review comments at @examples/hf_ptq/README.md:
- Line 178: Update the flash-linear-attention installation note to state that
Triton must be importable when using the `--no-deps` install command, or include
an explicit Triton installation step, so `fla.ops` can load its kernels.
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Transformers binds the kernels from fla-core alone, which depends only on
einops, while flash-linear-attention pins Transformers. With the kernels
importable, Transformers also sends CPU tensors to them, which fails.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 78.29%. Comparing base (3c423a9) to head (58d6763). ⚠️ Report is 56 commits behind head on main.
- **Model-specific dependencies**: Models with `trust_remote_code` may import packages not in the container (e.g., `mamba-ssm` for hybrid Mamba models). See Step 2.5. Use `EXTRA_PIP_DEPS` env var with the launcher, or install manually before running `hf_ptq.py`
- **Linear-attention models** (Qwen3-Next/3.5/3.8, GLM-5.x Flash, Kimi Linear, OLMo Hybrid) fall back to slow, memory-hungry PyTorch kernels without `pip install fla-core>=0.5.2`; install it before calibrating on GPU (it breaks CPU runs of these models)
- **Transformers version**: New models may need a newer version of transformers than what's installed. Check `config.json` for `transformers_version`. In containers, beware of `PIP_CONSTRAINT` blocking upgrades — see `references/slurm-setup-ptq.md` for workarounds
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Bot comment.
[P2] Quote the versioned requirement: pip install "fla-core>=0.5.2". In Bash/POSIX shells, the current > redirects stdout to a file named =0.5.2, and pip receives only fla-core. An already-installed older version therefore satisfies the command and is not upgraded, defeating the stated minimum. Markdown backticks do not provide shell quoting.
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Fixed in 58d6763: the skill now says pip install "fla-core>=0.5.2", quoted, and the hf_ptq README note gets the same quoted pin plus the reason for it (earlier versions compute packed Gated DeltaNet sequences wrongly).
fla-core before 0.5.2 computes packed Gated DeltaNet sequences wrongly
(review). Quote the requirement so a shell does not read >= as a
redirect.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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2026-10-27 21:58 UTC
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What does this PR do?
Type of change: documentation
Transformers runs the linear-attention layers of Qwen3-Next, Qwen3.5, Qwen3.8, GLM-5.x Flash, Kimi Linear and OLMo Hybrid through readable PyTorch reference kernels unless the flash-linear-attention kernels are importable. The reference path is slow and needs far more memory per sample, which caps the layerwise calibration batch. This adds a note to the
hf_ptqREADME and one line to the ptq skill on installing the kernels:pip install fla-core.fla-corealone is enough. The kernels Transformers looks for (fla.ops.kda.chunk_kda,fla.ops.gated_delta_rule.chunk_gated_delta_rule) live infla-core, which depends only oneinops. Theflash-linear-attentionpackage holds layers and models Transformers does not use, and pinstransformers>=5.6.2,<6.RuntimeError: 0 active drivers). That is also why the package is documented rather than added toexamples/hf_ptq/requirements.txt, which the example CI installs.Usage
Testing
Layerwise GPTQ on the routed experts at IQ2_XXS on 4x GB300 (oci-jhb), 1,024 x 512 calibration tokens, with #2945 and #3002,
flash-linear-attentionandfla-core0.6.0:flash-linear-attentioninstalled but notfla-core, nothing changed: Transformers could not importfla.ops.kda.chunk_kdaand kept the reference path. Withfla-corealone, it bindsfla.ops.kda.chunk.chunk_kdafor GLM-5.x andfla.ops.gated_delta_rule.chunk.chunk_gated_delta_rulefor Qwen, and a tiny Qwen3-Next runs on GPU through them; on CPU it fails as described above.Before your PR is "Ready for review"
CONTRIBUTING.md: N/A (documents an optional third-party package; nothing added to requirements)🤖 Generated with Claude Code
Summary by CodeRabbit
fla-core>=0.5.2before GPU calibration of supported models with linear-attention layers; otherwise, Transformers may use slower, memory-intensive reference kernels.fla-coreversions can compute packed Gated DeltaNet sequences incorrectly, and that the optimized kernels can cause these models’ CPU runs to fail.