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2026-04-20 01:38:38 +00:00
Daniel Han-Chen
8dfa076ee4
Add FA4 + persistent CB benchmarks and a naive TRL baseline
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New scripts under scripts/benchmarks/:
- flash_attn_fa4_shim.py: monkey-patches that let transformers CB dispatch to
Flash Attention 4 on Blackwell (sm_100). CB's ContinuousBatchProcessor
otherwise emits a 4D paged attention mask for flash_attention_2 (which then
breaks _flash_attention_forward's _upad_input branch), and passes
max_seqlen_q instead of max_length_q. The shim skips the mask for FA and
accepts both names.
- persistent_cb.py: replaces model.generate_batch with a version that reuses
one ContinuousBatchingManager across calls, avoiding the per-step
PagedAttentionCache realloc. Wired up behind --persistent_cb on the tpaged
and standalone scripts.
- qwen3_grpo_naive.py: vanilla HF model.generate + TRL GRPOTrainer, no vLLM
and no CB. Mirrors the TRL docs example. Useful as a third column in the
comparison and also as a "will this at least converge" sanity check.
Adds --attn_impl and --persistent_cb flags to the existing generation and
training scripts. No changes to Unsloth internals.
Updated README.md with the FA install recipe (flash-attn-4==4.0.0b9, plus
a small site-packages shim that re-exports FA4's cute.* symbols under the
FA2 flash_attn namespace so transformers' is_flash_attn_2_available() and
_lazy_imports("flash_attention_2") succeed on B200).
Benchmark numbers on a single B200, Qwen3-4B-Base LoRA rank 32, bf16:
Generation microbenchmark (32 prompts, 512 new tokens):
vLLM 7224 decode tok/s (100%)
CB paged|sdpa 527 decode tok/s ( 7.3%)
CB paged|flash_attention_2 (FA4) 709 decode tok/s ( 9.8%)
CB paged|flash_attention_2 persistent 529 decode tok/s ( 7.3%)
GRPO training (max_steps=20, num_generations=2, per_device_batch=2):
vLLM colocated 136.6 s peak 157 GB
naive TRL (HF generate) 910.0 s peak 15 GB
CB SDPA 1521.5 s peak 98 GB (prior run)
CB FA4 1470.7 s peak 82 GB
CB FA4 + persistent 1562.1 s peak 87 GB
CB FA4 + ng=4 persistent 1597.0 s peak 94 GB
FA4 is a real ~1.4x improvement over SDPA for CB decode throughput but the
50% of vLLM target is still not reached. The remaining gap is driven by
CUDA graph capture (which ContinuousBatchingManager still NotImplementedErrors
on) and vLLM's scheduler being more efficient for decode-heavy GRPO rollouts.
Naive TRL generate is the honest small-rig baseline: 6.7x slower than vLLM
at 10% of the VRAM footprint, and ~1.7x faster than CB here.
2026-04-20 01:38:12 +00:00
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2026-04-19 14:45:48 +00:00
Daniel Han-Chen
07939bb025
Add Qwen3-4B GRPO rollout engine benchmarks
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Adds reproducible scripts under scripts/benchmarks/ that compare vLLM
colocated rollouts against the transformers continuous batching API
(model.generate_batch, paged attention) for GRPO training on Qwen3-4B.
Contents:
- unsloth_grpo_common.py: shared dataset, reward functions, and GRPO
hyperparameters so the two backends differ only in the rollout engine.
- qwen3_grpo_vllm.py: baseline training entry using fast_inference=True
and TRL use_vllm=True, vllm_mode=colocate.
- qwen3_grpo_tpaged.py: candidate using a vanilla HF Qwen3 + PEFT LoRA
with TRL use_transformers_paged=True.
- cb_vs_vllm_generation.py: standalone generation microbenchmark.
- README.md: integration notes, reproduction steps, and observed numbers.
On a single B200 with Unsloth Qwen3-4B-Base at LoRA rank 32 bf16,
transformers continuous batching reaches 7-9 percent of vLLM throughput
on this workload. The README documents the integration sharp edges
(top_k=-1, PagedAttentionCache default upper bounds, Unsloth's
Qwen3Attention_fast_forward bypassing the functional attention
interface, TRL importing GuidedDecodingParams from a newer vLLM that no
longer exports it, and UnslothGRPOTrainer expecting for_training /
for_inference hooks on the model).
The scripts are intentionally self-contained so they are easy to rerun
after either upstream change that could close the throughput gap
(flash-attn availability, CUDA graphs in ContinuousBatchingManager,
persistent paged caches across generate_batch calls, or a
paged-compatible Unsloth attention forward).
2026-04-19 14:45:03 +00:00
Manan Shah
7d0d2f256c
Add qwen3.6 script ( #5084 )
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* unsloth gemma4 support files
* some fixes
* Fixing cache.empty() calls (#4813 )
* Fixing cache.empty() calls
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---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix/gemma4 mlx (#4816 )
* Fixing cache.empty() calls
* fixing for mlx versions
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---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* removed bidirectional check for 31b (#4839 )
Co-authored-by: Manan17 <shahmanan170602@gmail.coml>
* Add Gemma 4 26B MoE support (MLX) (#4844 )
* removed bidirectional check for 31b
* Change gemma4_text for moe
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(gemma4): cast RoPE offset to int before mx.arange() (#4901 )
* fix(gemma4): cast RoPE offset to int before mx.arange()
* fix(gemma4): use zero-based arange + offset to avoid CPU-GPU sync
* qwen3.6 patches for multi-turn chat
* qwen3.6 script
* removing unnecessary scripts
* displaying errors for not installed packages
---------
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Co-authored-by: Manan Shah <mananshah@Manans-MacBook-Pro.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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2026-04-17 01:21:30 -07:00
Manan Shah
80c12ff1a6
Move gemma4 script ( #4994 )
...
* updating gemma4 script
* moving gemma4 script to scripts folder
2026-04-12 23:41:15 -07:00
Daniel Han
d6bb89ad44
Formatting & bug fixes ( #3563 )
...
* Update rl.py
* Fix CE Loss
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* Mistral3 vllm (#3349 )
* [WIP] use vLLM for vision language models
* Update README.md
Editing icon sizes
* Update README.md
Updating icon sizes
* Update README.md (#2885 )
* MoE kernels AGPLv3
* versioning
* Many bug fixes (#2908 )
* add deepseek v3
* add deepseek r1 base
* add deepseek r1 zero
* add deepseek distill llama
* add deepseek distill models
* remove redundant code when constructing model names
* add mistral small to registry
* rename model registration methods
* rename deepseek registration methods
* refactor naming for mistral and phi
* add global register models
* refactor model registration tests for new registry apis
* add model search method
* remove deprecated registration api
* add quant type test
* add registry readme
* make llama registration more specific
* clear registry when executing individual model registration file
* more registry readme updates
* Update _auto_install.py
* Llama4
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---------
Co-authored-by: jeromeku <jerome.ku@gmail.com>
Co-authored-by: Michael Han <107991372+shimmyshimmer@users.noreply.github.com>
* silienty skip falcon h1 import is transformers_version < 4.53.0 (#2912 )
* Dynamically adjust get_per_token_logps function and patch as well (#2911 )
* add intel gpu with vllm support (#2903 )
* [bugs] fix for casual mask (#2868 )
* fix for casual mask
* use un_casual in sdpa
* add missing mask
* fix for type
* Explicitly check if xformers exists for attention (#2889 )
* Update __init__.py
* Update llama.py
* if mlp doesn't exist in layer module check for feed_forward name for falcon h1 (#2913 )
* Move inputs to right devices. (#2919 )
* Move tensors to right devices
* fix multi gpu for non mistral models
* multi GPU RoPE for gemma2
* Finish up multi GPU inference
* Make multiGPU rope a list
* Remove unnecessary transfer to CPU
* Remove unnecessary move to CPU
* Donot move inputs to device yet
will be handled separately in another PR
* Move inputs to appropriate decoder device
* Make device count global variable
* Cleanup RoPE device code
* Fixup num_gpu to device count
* Cleanup device counts
* Use device index for RoPE get_cache
* Donot typecast
* Use tuple instead of list for tensors. Use device index directly
* fixup move to device logic
* WIP VLM vLLM
* Make vLLM patch a function
* Add save and load lora functions
* Make fast_inference setup depend on the flag
* Improve fast inference patching mechanism
* Make vision setting depend on checks in fastbasemodel
* Check LoRA and vLLM intercompatibility for vision models
* Comment pointing to vLLM LoRA check
* Improve lora validation on vLLM
* Error out on no vLLM and increase max lora rank
* Bug fixes (#3017 )
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* Revert "Revert "Add Qwen2.5-VL-32B-Instruct mapping to fix quantized model me…" (#2990 )
This reverts commit 4021da634a#3011 )
* [intel] add for intel path for llama.py (#3012 )
* fix for intel path
* remove unuse code
* Update unsloth/models/llama.py
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
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* Revert "Revert "Add Qwen2.5-VL-32B-Instruct mapping to fix quantized model me…" (#2990 )
This reverts commit 4021da634a#3026 )
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* Revert "Revert "Add Qwen2.5-VL-32B-Instruct mapping to fix quantized model me…" (#2990 )
This reverts commit 4021da634a#3186 )
* Fix mamba
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---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Update loader.py
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---------
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
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---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
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---------
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
* adallow float32 dtype in FastLanguageModel (#3204 )
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* Add mistral 3 support
---------
Co-authored-by: Michael Han <107991372+shimmyshimmer@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: jeromeku <jerome.ku@gmail.com>
Co-authored-by: DoubleMathew <mmathew23@gmail.com>
Co-authored-by: Lei Zhenyuan <zhenyuan.lei@intel.com>
Co-authored-by: parth2510 <parthguptapg7326@gmail.com>
* Set padding to 0
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Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
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---------
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
Co-authored-by: Michael Han <107991372+shimmyshimmer@users.noreply.github.com>
Co-authored-by: jeromeku <jerome.ku@gmail.com>
Co-authored-by: DoubleMathew <mmathew23@gmail.com>
Co-authored-by: Lei Zhenyuan <zhenyuan.lei@intel.com>
Co-authored-by: parth2510 <parthguptapg7326@gmail.com>
Co-authored-by: Dan Saunders <danjsaund@gmail.com>
2025-11-07 06:00:22 -08:00
pre-commit-ci[bot]