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✅ Updates

  • May. 26th, 2024: Update: we release the updated weights of VMambav2, together with the new arxiv paper.

  • May. 7th, 2024: Update: Important! using torch.backends.cudnn.enabled=True in downstream tasks may be quite slow. If you found vmamba quite slow in your machine, disable it in vmamba.py, else, ignore this.

  • April. 10th, 2024: Update: we have released arXiv 2401.10166v2, which contains lots of updates we made related to VMambav2!

  • March. 20th, 2024: Update: we have released the configs/logs/checkpoints for classification/detection/segmentation of VMambav2. We'are still working on VMambav3!

  • March. 16th, 2024: Improvement: we implemented models with channel_first data layout, which GREATLY raises the throughput of the model on A100 (On V100, due to the slow implementation of F.conv2d compared to F.linear, it would not speed up.), Try using norm_layer="ln2d" (when inferencing or training) rather than norm_layer="ln" to unlock this feature with almost no performance cost!

  • March. 8th, 2024: Update + Improvement: we update the performance of VMamba-T, Vmamba-S, VMamba-B with nightly build, checkpoints and logs are coming soon. (Note that these models are trained without CrossScanTriton or forwardtype=v4, you can modify those configs yourself to raise the speed with almost no cost!)

  • March. 8th, 2024: Improvement: we implemented CrossScan and CrossMerge in triton, which speed the training up again. CrossScan and CrossMerge implemented in triton is ~2x faster than implemented in pytorch. Meanwhile, use v4 rather than v3 or v2 in forwardtype also raise the speed GREATLY!.

  • Feb. 26th, 2024: Improvement: we now support flexible output of selective scan. That means whatever type the input is, the output can always be float32. The feature is useful as when training with float16, the loss often get nan due to the overflow over float16. In the meantime, training with float32 costs more time. Input with float16 and output with float32 can be fast, but in the meantime, the loss is less likely to be NaN. Try SelectiveScanOflex with float16 input and float32 output to enjoy that feature!

  • Feb. 22th, 2024: Pre-Release: we set a pre-release to share nightly-build checkpoints in classificaion. Feel free to enjoy those new features with faster code and higher performance!

  • Feb. 18th, 2024: Release: all the checkpoints and logs of VMamba (VSSM version 0) in classification have been released. These checkpoints correspond to the experiments done before date #20240119, if there is any mismatch to the latest code in main, please let me know, and I'll fix that. This is related to issue#1 and issue#37.

  • Feb. 16th, 2024: Fix bug + Improvement: SS2D.forward_corev1 is deprecated. Fixed some bugs related to issue#30 (in test_selective scan.py, we now compare ours with mamba_ssm rather than selective_scan_ref), issue#32, issue#31. backward nrow has been added and tested in selective_scan.

  • Feb. 4th, 2024: Fix bug + Improvement: Do not use SS2D.forward_corev1 with float32=False for training (testing is ok), as it's unstable training in float16 for selective scan. We released SS2D.forward_corev2, which is in float32, and is faster than SS2D.forward_corev1.

  • Feb. 1st, 2024: Fix bug: we now calculate FLOPs with the algrithm @albertgu provides, which will be bigger than previous calculation (which is based on the selective_scan_ref function, and ignores the hardware-aware algrithm).

  • Jan. 31st, 2024: Add feature: selective_scan now supports an extra argument nrow in [1, 2, 4]. If you find your device is strong and the time consumption keeps as d_state rises, try this feature to speed up nrows x without any cost ! Note this feature is actually a bug fix for mamba.

  • Jan. 28th, 2024: Add feature: we cloned main into a new branch called 20240128-achieve, the main branch has experienced a great update now. The code now are much easier to use in your own project, and the training speed is faster! This new version is totally compatible with original one, and you can use previous checkpoints without any modification. But if you want to use exactly the same models as original ones, just change forward_core = self.forward_corev1 into forward_core = self.forward_corev0 in classification/models/vmamba/vmamba.py#SS2D or you can change into the branch 20240128-archive instead.

  • Jan. 23th, 2024: Add feature: we add an alternative for mamba_ssm and causal_conv1d. Typing pip install . in selective_scan and you can get rid of those two packages. Just turn self.forward_core = self.forward_corev0 to self.forward_core = self.forward_corev1 in classification/models/vmamba/vmamba.py#SS2D.__init__ to enjoy that feature. The training speed is expected to raise from 20min/epoch for tiny in 8x4090GPU to 17min/epoch, GPU memory cost reduces too.

  • Jan. 22th, 2024: We have released VMamba-T/S pre-trained weights. The ema weights should be converted before transferring to downstream tasks to match the module names using get_ckpt.py.

  • Jan. 19th, 2024: The source code for classification, object detection, and semantic segmentation are provided.