Repository: kozistr/pytorch_optimizer

Issue:
PSGD Kron
PSGD is a second-order optimizer originally created by Xi-Lin Li that uses either a hessian-based or whitening-based (gg^T) preconditioner and lie groups to improve training convergence, generalization, and efficiency

Code: https://github.com/evanatyourservice/kron_torch

Hints from issue comments:

Solution patch:
diff --git a/README.md b/README.md
index 4ee52395..0e7932e9 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **93 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **94 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
 * Including many variants such as `ADOPT`, `Cautious`, `AdamD`, `StableAdamW`, and `Gradient Centrailiaztion`
 * Easy to use, clean, and tested codes
 * Active maintenance
@@ -201,6 +201,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | SPAM          | *Spike-Aware Adam with Momentum Reset for Stable LLM Training*                                    | [github](https://github.com/TianjinYellow/SPAM-Optimizer)                                                      | <https://arxiv.org/abs/2501.06842>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2025arXiv250106842H/exportcitation)                                                        |
 | TAM           | *Torque-Aware Momentum*                                                                           |                                                                                                                | <https://arxiv.org/abs/2412.18790>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv241218790M/exportcitation)                                                        |
 | FOCUS         | *First Order Concentrated Updating Scheme*                                                        | [github](https://github.com/liuyz0/FOCUS)                                                                      | <https://arxiv.org/abs/2501.12243>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2025arXiv250112243M/exportcitation)                                                        |
+| PSGD          | *Preconditioned Stochastic Gradient Descent*                                                      | [github](https://github.com/lixilinx/psgd_torch)                                                               | <https://arxiv.org/abs/1512.04202>                                                          | [cite](https://github.com/lixilinx/psgd_torch?tab=readme-ov-file#resources)                                                         |
 
 ## Supported LR Scheduler
 
diff --git a/docs/changelogs/v3.3.5.md b/docs/changelogs/v3.3.5.md
deleted file mode 100644
index e77f8f14..00000000
--- a/docs/changelogs/v3.3.5.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### Change Log
-
-### Feature
-
-* Implement `FOCUS` optimizer. (#330, #331)
-    * [First Order Concentrated Updating Scheme](https://arxiv.org/abs/2501.12243) 
-
-### Update
-
-* Support `OrthoGrad` variant to `Ranger25`. (#332)
-
-### Fix
-
-* Add the missing `state` property in `OrthoGrad` optimizer. (#326, #327)
-* Add the missing `state_dict`, and `load_state_dict` methods to `TRAC` and `OrthoGrad` optimizers. (#332)
-* Skip when the gradient is sparse in `OrthoGrad` optimizer. (#332)
-
-### Contributions
-
-thanks to @Vectorrent
diff --git a/docs/changelogs/v3.4.0.md b/docs/changelogs/v3.4.0.md
new file mode 100644
index 00000000..c8238008
--- /dev/null
+++ b/docs/changelogs/v3.4.0.md
@@ -0,0 +1,25 @@
+### Change Log
+
+### Feature
+
+* Implement `FOCUS` optimizer. (#330, #331)
+    * [First Order Concentrated Updating Scheme](https://arxiv.org/abs/2501.12243) 
+* Implement `PSGD Kron`. (#337)
+    * [preconditioned stochastic gradient descent w/ Kron pre-conditioner](https://arxiv.org/abs/1512.04202) 
+
+### Update
+
+* Support `OrthoGrad` variant to `Ranger25`. (#332)
+  * `Ranger25` optimizer is my experimental-crafted optimizer, which mixes lots of optimizer variants such as `ADOPT` + `AdEMAMix` + `Cautious` + `StableAdamW` + `Adam-Atan2` + `OrthoGrad`.
+
+### Fix
+
+* Add the missing `state` property in `OrthoGrad` optimizer. (#326, #327)
+* Add the missing `state_dict`, and `load_state_dict` methods to `TRAC` and `OrthoGrad` optimizers. (#332)
+* Skip when the gradient is sparse in `OrthoGrad` optimizer. (#332)
+* Support alternative precision training in `SOAP` optimizer. (#333)
+* Store SOAP condition matrices as the dtype of their parameters. (#335)
+
+### Contributions
+
+thanks to @Vectorrent, @kylevedder
diff --git a/docs/index.md b/docs/index.md
index 4ee52395..0e7932e9 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **93 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **94 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
 * Including many variants such as `ADOPT`, `Cautious`, `AdamD`, `StableAdamW`, and `Gradient Centrailiaztion`
 * Easy to use, clean, and tested codes
 * Active maintenance
@@ -201,6 +201,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | SPAM          | *Spike-Aware Adam with Momentum Reset for Stable LLM Training*                                    | [github](https://github.com/TianjinYellow/SPAM-Optimizer)                                                      | <https://arxiv.org/abs/2501.06842>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2025arXiv250106842H/exportcitation)                                                        |
 | TAM           | *Torque-Aware Momentum*                                                                           |                                                                                                                | <https://arxiv.org/abs/2412.18790>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv241218790M/exportcitation)                                                        |
 | FOCUS         | *First Order Concentrated Updating Scheme*                                                        | [github](https://github.com/liuyz0/FOCUS)                                                                      | <https://arxiv.org/abs/2501.12243>                                                          | [cite](https://ui.adsabs.harvard.edu/abs/2025arXiv250112243M/exportcitation)                                                        |
+| PSGD          | *Preconditioned Stochastic Gradient Descent*                                                      | [github](https://github.com/lixilinx/psgd_torch)                                                               | <https://arxiv.org/abs/1512.04202>                                                          | [cite](https://github.com/lixilinx/psgd_torch?tab=readme-ov-file#resources)                                                         |
 
 ## Supported LR Scheduler
 
diff --git a/docs/optimizer.md b/docs/optimizer.md
index 1cbc28d0..57768ef8 100644
--- a/docs/optimizer.md
+++ b/docs/optimizer.md
@@ -284,6 +284,10 @@
     :docstring:
     :members:
 
+::: pytorch_optimizer.Kron
+    :docstring:
+    :members:
+
 ::: pytorch_optimizer.QHAdam
     :docstring:
     :members:
diff --git a/poetry.lock b/poetry.lock
index ba2772b3..6eb6b816 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,23 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
-
-[[package]]
-name = "bitsandbytes"
-version = "0.44.1"
-description = "k-bit optimizers and matrix multiplication routines."
-optional = true
-python-versions = "*"
-files = [
-    {file = "bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:b2f24c6cbf11fc8c5d69b3dcecee9f7011451ec59d6ac833e873c9f105259668"},
-    {file = "bitsandbytes-0.44.1-py3-none-win_amd64.whl", hash = "sha256:8e68e12aa25d2cf9a1730ad72890a5d1a19daa23f459a6a4679331f353d58cb4"},
-]
-
-[package.dependencies]
-numpy = "*"
-torch = "*"
-
-[package.extras]
-benchmark = ["matplotlib", "pandas"]
-test = ["lion-pytorch", "scipy"]
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "black"
@@ -25,6 +6,8 @@ version = "24.8.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version == \"3.8\""
 files = [
     {file = "black-24.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:09cdeb74d494ec023ded657f7092ba518e8cf78fa8386155e4a03fdcc44679e6"},
     {file = "black-24.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:81c6742da39f33b08e791da38410f32e27d632260e599df7245cccee2064afeb"},
@@ -65,12 +48,62 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
+name = "black"
+version = "25.1.0"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.9\""
+files = [
+    {file = "black-25.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:759e7ec1e050a15f89b770cefbf91ebee8917aac5c20483bc2d80a6c3a04df32"},
+    {file = "black-25.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e519ecf93120f34243e6b0054db49c00a35f84f195d5bce7e9f5cfc578fc2da"},
+    {file = "black-25.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:055e59b198df7ac0b7efca5ad7ff2516bca343276c466be72eb04a3bcc1f82d7"},
+    {file = "black-25.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:db8ea9917d6f8fc62abd90d944920d95e73c83a5ee3383493e35d271aca872e9"},
+    {file = "black-25.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a39337598244de4bae26475f77dda852ea00a93bd4c728e09eacd827ec929df0"},
+    {file = "black-25.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:96c1c7cd856bba8e20094e36e0f948718dc688dba4a9d78c3adde52b9e6c2299"},
+    {file = "black-25.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bce2e264d59c91e52d8000d507eb20a9aca4a778731a08cfff7e5ac4a4bb7096"},
+    {file = "black-25.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:172b1dbff09f86ce6f4eb8edf9dede08b1fce58ba194c87d7a4f1a5aa2f5b3c2"},
+    {file = "black-25.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4b60580e829091e6f9238c848ea6750efed72140b91b048770b64e74fe04908b"},
+    {file = "black-25.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1e2978f6df243b155ef5fa7e558a43037c3079093ed5d10fd84c43900f2d8ecc"},
+    {file = "black-25.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3b48735872ec535027d979e8dcb20bf4f70b5ac75a8ea99f127c106a7d7aba9f"},
+    {file = "black-25.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:ea0213189960bda9cf99be5b8c8ce66bb054af5e9e861249cd23471bd7b0b3ba"},
+    {file = "black-25.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8f0b18a02996a836cc9c9c78e5babec10930862827b1b724ddfe98ccf2f2fe4f"},
+    {file = "black-25.1.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:afebb7098bfbc70037a053b91ae8437c3857482d3a690fefc03e9ff7aa9a5fd3"},
+    {file = "black-25.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:030b9759066a4ee5e5aca28c3c77f9c64789cdd4de8ac1df642c40b708be6171"},
+    {file = "black-25.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:a22f402b410566e2d1c950708c77ebf5ebd5d0d88a6a2e87c86d9fb48afa0d18"},
+    {file = "black-25.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a1ee0a0c330f7b5130ce0caed9936a904793576ef4d2b98c40835d6a65afa6a0"},
+    {file = "black-25.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f3df5f1bf91d36002b0a75389ca8663510cf0531cca8aa5c1ef695b46d98655f"},
+    {file = "black-25.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d9e6827d563a2c820772b32ce8a42828dc6790f095f441beef18f96aa6f8294e"},
+    {file = "black-25.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:bacabb307dca5ebaf9c118d2d2f6903da0d62c9faa82bd21a33eecc319559355"},
+    {file = "black-25.1.0-py3-none-any.whl", hash = "sha256:95e8176dae143ba9097f351d174fdaf0ccd29efb414b362ae3fd72bf0f710717"},
+    {file = "black-25.1.0.tar.gz", hash = "sha256:33496d5cd1222ad73391352b4ae8da15253c5de89b93a80b3e2c8d9a19ec2666"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.10)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "click"
 version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
     {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
@@ -85,6 +118,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
+markers = "(python_version >= \"3.9\" or python_version == \"3.8\") and (sys_platform == \"win32\" or platform_system == \"Windows\")"
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -96,6 +131,8 @@ version = "7.6.1"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "coverage-7.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b06079abebbc0e89e6163b8e8f0e16270124c154dc6e4a47b413dd538859af16"},
     {file = "coverage-7.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cf4b19715bccd7ee27b6b120e7e9dd56037b9c0681dcc1adc9ba9db3d417fa36"},
@@ -183,6 +220,8 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" and python_version < \"3.11\" or python_version == \"3.8\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -197,6 +236,8 @@ version = "3.16.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
     {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
@@ -213,6 +254,8 @@ version = "2024.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
     {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
@@ -252,6 +295,8 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -263,6 +308,8 @@ version = "5.13.2"
 description = "A Python utility / library to sort Python imports."
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version == \"3.8\""
 files = [
     {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
     {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
@@ -271,12 +318,31 @@ files = [
 [package.extras]
 colors = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "isort"
+version = "6.0.0"
+description = "A Python utility / library to sort Python imports."
+optional = false
+python-versions = ">=3.9.0"
+groups = ["dev"]
+markers = "python_version >= \"3.9\""
+files = [
+    {file = "isort-6.0.0-py3-none-any.whl", hash = "sha256:567954102bb47bb12e0fae62606570faacddd441e45683968c8d1734fb1af892"},
+    {file = "isort-6.0.0.tar.gz", hash = "sha256:75d9d8a1438a9432a7d7b54f2d3b45cad9a4a0fdba43617d9873379704a8bdf1"},
+]
+
+[package.extras]
+colors = ["colorama"]
+plugins = ["setuptools"]
+
 [[package]]
 name = "jinja2"
 version = "3.1.5"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "jinja2-3.1.5-py3-none-any.whl", hash = "sha256:aba0f4dc9ed8013c424088f68a5c226f7d6097ed89b246d7749c2ec4175c6adb"},
     {file = "jinja2-3.1.5.tar.gz", hash = "sha256:8fefff8dc3034e27bb80d67c671eb8a9bc424c0ef4c0826edbff304cceff43bb"},
@@ -294,6 +360,8 @@ version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
@@ -363,6 +431,8 @@ version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
     {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
@@ -380,6 +450,8 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -391,6 +463,8 @@ version = "3.1"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
     {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
@@ -409,6 +483,8 @@ version = "1.24.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.8\""
 files = [
     {file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
     {file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
@@ -446,6 +522,8 @@ version = "2.0.2"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.9\""
 files = [
     {file = "numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece"},
     {file = "numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04"},
@@ -500,6 +578,8 @@ version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
     {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
@@ -511,6 +591,8 @@ version = "0.12.1"
 description = "Utility library for gitignore style pattern matching of file paths."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
     {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
@@ -522,6 +604,8 @@ version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"},
     {file = "platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907"},
@@ -538,6 +622,8 @@ version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
     {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
@@ -553,6 +639,8 @@ version = "8.3.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6"},
     {file = "pytest-8.3.4.tar.gz", hash = "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761"},
@@ -575,6 +663,8 @@ version = "5.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "pytest-cov-5.0.0.tar.gz", hash = "sha256:5837b58e9f6ebd335b0f8060eecce69b662415b16dc503883a02f45dfeb14857"},
     {file = "pytest_cov-5.0.0-py3-none-any.whl", hash = "sha256:4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652"},
@@ -589,29 +679,31 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "ruff"
-version = "0.9.3"
+version = "0.9.4"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
-    {file = "ruff-0.9.3-py3-none-linux_armv6l.whl", hash = "sha256:7f39b879064c7d9670197d91124a75d118d00b0990586549949aae80cdc16624"},
-    {file = "ruff-0.9.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:a187171e7c09efa4b4cc30ee5d0d55a8d6c5311b3e1b74ac5cb96cc89bafc43c"},
-    {file = "ruff-0.9.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:c59ab92f8e92d6725b7ded9d4a31be3ef42688a115c6d3da9457a5bda140e2b4"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2dc153c25e715be41bb228bc651c1e9b1a88d5c6e5ed0194fa0dfea02b026439"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:646909a1e25e0dc28fbc529eab8eb7bb583079628e8cbe738192853dbbe43af5"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5a5a46e09355695fbdbb30ed9889d6cf1c61b77b700a9fafc21b41f097bfbba4"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:c4bb09d2bbb394e3730d0918c00276e79b2de70ec2a5231cd4ebb51a57df9ba1"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:96a87ec31dc1044d8c2da2ebbed1c456d9b561e7d087734336518181b26b3aa5"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9bb7554aca6f842645022fe2d301c264e6925baa708b392867b7a62645304df4"},
-    {file = "ruff-0.9.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cabc332b7075a914ecea912cd1f3d4370489c8018f2c945a30bcc934e3bc06a6"},
-    {file = "ruff-0.9.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:33866c3cc2a575cbd546f2cd02bdd466fed65118e4365ee538a3deffd6fcb730"},
-    {file = "ruff-0.9.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:006e5de2621304c8810bcd2ee101587712fa93b4f955ed0985907a36c427e0c2"},
-    {file = "ruff-0.9.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:ba6eea4459dbd6b1be4e6bfc766079fb9b8dd2e5a35aff6baee4d9b1514ea519"},
-    {file = "ruff-0.9.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:90230a6b8055ad47d3325e9ee8f8a9ae7e273078a66401ac66df68943ced029b"},
-    {file = "ruff-0.9.3-py3-none-win32.whl", hash = "sha256:eabe5eb2c19a42f4808c03b82bd313fc84d4e395133fb3fc1b1516170a31213c"},
-    {file = "ruff-0.9.3-py3-none-win_amd64.whl", hash = "sha256:040ceb7f20791dfa0e78b4230ee9dce23da3b64dd5848e40e3bf3ab76468dcf4"},
-    {file = "ruff-0.9.3-py3-none-win_arm64.whl", hash = "sha256:800d773f6d4d33b0a3c60e2c6ae8f4c202ea2de056365acfa519aa48acf28e0b"},
-    {file = "ruff-0.9.3.tar.gz", hash = "sha256:8293f89985a090ebc3ed1064df31f3b4b56320cdfcec8b60d3295bddb955c22a"},
+    {file = "ruff-0.9.4-py3-none-linux_armv6l.whl", hash = "sha256:64e73d25b954f71ff100bb70f39f1ee09e880728efb4250c632ceed4e4cdf706"},
+    {file = "ruff-0.9.4-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6ce6743ed64d9afab4fafeaea70d3631b4d4b28b592db21a5c2d1f0ef52934bf"},
+    {file = "ruff-0.9.4-py3-none-macosx_11_0_arm64.whl", hash = "sha256:54499fb08408e32b57360f6f9de7157a5fec24ad79cb3f42ef2c3f3f728dfe2b"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:37c892540108314a6f01f105040b5106aeb829fa5fb0561d2dcaf71485021137"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:de9edf2ce4b9ddf43fd93e20ef635a900e25f622f87ed6e3047a664d0e8f810e"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87c90c32357c74f11deb7fbb065126d91771b207bf9bfaaee01277ca59b574ec"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:56acd6c694da3695a7461cc55775f3a409c3815ac467279dfa126061d84b314b"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e0c93e7d47ed951b9394cf352d6695b31498e68fd5782d6cbc282425655f687a"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1d4c8772670aecf037d1bf7a07c39106574d143b26cfe5ed1787d2f31e800214"},
+    {file = "ruff-0.9.4-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfc5f1d7afeda8d5d37660eeca6d389b142d7f2b5a1ab659d9214ebd0e025231"},
+    {file = "ruff-0.9.4-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:faa935fc00ae854d8b638c16a5f1ce881bc3f67446957dd6f2af440a5fc8526b"},
+    {file = "ruff-0.9.4-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:a6c634fc6f5a0ceae1ab3e13c58183978185d131a29c425e4eaa9f40afe1e6d6"},
+    {file = "ruff-0.9.4-py3-none-musllinux_1_2_i686.whl", hash = "sha256:433dedf6ddfdec7f1ac7575ec1eb9844fa60c4c8c2f8887a070672b8d353d34c"},
+    {file = "ruff-0.9.4-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:d612dbd0f3a919a8cc1d12037168bfa536862066808960e0cc901404b77968f0"},
+    {file = "ruff-0.9.4-py3-none-win32.whl", hash = "sha256:db1192ddda2200671f9ef61d9597fcef89d934f5d1705e571a93a67fb13a4402"},
+    {file = "ruff-0.9.4-py3-none-win_amd64.whl", hash = "sha256:05bebf4cdbe3ef75430d26c375773978950bbf4ee3c95ccb5448940dc092408e"},
+    {file = "ruff-0.9.4-py3-none-win_arm64.whl", hash = "sha256:585792f1e81509e38ac5123492f8875fbc36f3ede8185af0a26df348e5154f41"},
+    {file = "ruff-0.9.4.tar.gz", hash = "sha256:6907ee3529244bb0ed066683e075f09285b38dd5b4039370df6ff06041ca19e7"},
 ]
 
 [[package]]
@@ -620,6 +712,8 @@ version = "75.8.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.12\""
 files = [
     {file = "setuptools-75.8.0-py3-none-any.whl", hash = "sha256:e3982f444617239225d675215d51f6ba05f845d4eec313da4418fdbb56fb27e3"},
     {file = "setuptools-75.8.0.tar.gz", hash = "sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6"},
@@ -640,6 +734,8 @@ version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.8\""
 files = [
     {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
     {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
@@ -654,6 +750,8 @@ version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.9\""
 files = [
     {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
     {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
@@ -671,6 +769,8 @@ version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.9\" and python_full_version <= \"3.11.0a6\" or python_version == \"3.8\""
 files = [
     {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
     {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -712,6 +812,8 @@ version = "2.5.1+cpu"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["main"]
+markers = "python_version >= \"3.9\" or python_version == \"3.8\""
 files = [
     {file = "torch-2.5.1+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:7f91a2200e352745d70e22396bd501448e28350fbdbd8d8b1c83037e25451150"},
     {file = "torch-2.5.1+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:df93157482b672892d29134d3fae9d38ba3219702faedd79f407eb36774c56ce"},
@@ -751,15 +853,14 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
-
-[extras]
-bitsandbytes = ["bitsandbytes"]
+markers = {main = "python_version >= \"3.9\" or python_version == \"3.8\"", dev = "python_version >= \"3.9\" and python_version < \"3.11\" or python_version == \"3.8\""}
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = ">=3.8"
-content-hash = "0fb4f6358622b11b8e5f09beaaa21a9622d536114716c7977535a20e549ab34d"
+content-hash = "2f6674bb2c0ae9a42111da81cc06e9ed17aab1a4f2243712309ceaf71005920b"
diff --git a/pyproject.toml b/pyproject.toml
index 2c525e4f..62211684 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,7 +16,7 @@ keywords = [
     "Apollo", "APOLLO", "AvaGrad", "bSAM", "CAME", "DAdaptAdaGrad", "DAdaptAdam", "DAdaptAdan", "DAdaptSGD",
     "DAdaptLion", "DeMo", "DiffGrad", "FAdam", "FOCUS", "Fromage", "FTRL", "GaLore", "Grams", "Gravity", "GrokFast",
     "GSAM", "Kate", "Lamb", "LaProp", "LARS", "Lion", "LOMO", "Lookahead", "MADGRAD", "MARS", "MSVAG", "Muno", "Nero",
-    "NovoGrad", "OrthoGrad", "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger",
+    "NovoGrad", "OrthoGrad", "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "PSGD", "QHAdam", "QHM", "RAdam", "Ranger",
     "Ranger21", "RotoGrad", "SAM", "ScheduleFreeSGD", "ScheduleFreeAdamW", "ScheduleFreeRAdam", "SGDP", "Shampoo",
     "ScalableShampoo", "SGDW", "SignSGD", "SM3", "SOAP", "SopihaH", "SPAM", "SRMM", "StableAdamW", "SWATS", "TAM",
     "Tiger", "TRAC", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine", "SoftF1", "Dice", "LDAM", "Jaccard",
@@ -52,29 +52,27 @@ numpy = [
     { version = "<=1.24.4", python = "<3.9" },
 ]
 torch = { version = ">=1.10", python = ">=3.8", source = "torch" }
-bitsandbytes = { version = "^0.44", optional = true }
 
-[tool.poetry.dev-dependencies]
-isort = { version = "^5", python = ">=3.8" }
-black = { version = "^24", python = ">=3.8" }
+[tool.poetry.group.dev.dependencies]
+isort = [
+    { version = "<6", python = "<3.9" },
+    { version = "^6", python = ">=3.9" },
+]
+black = [
+    { version = "<25", python = "<3.9" },
+    { version = "^25", python = ">=3.9" },
+]
 ruff = "*"
 pytest = "*"
 pytest-cov = "*"
 
-[tool.poetry.extras]
-bitsandbytes = ["bitsandbytes"]
-
 [[tool.poetry.source]]
 name = "torch"
 url = "https://download.pytorch.org/whl/cpu"
 priority = "explicit"
 
 [tool.ruff]
-src = [
-    "pytorch_optimizer",
-    "tests",
-    "examples",
-]
+src = ["pytorch_optimizer", "tests", "examples"]
 target-version = "py312"
 line-length = 119
 exclude = [
@@ -109,10 +107,10 @@ flake8-quotes.docstring-quotes = "double"
 flake8-quotes.inline-quotes = "single"
 
 [tool.ruff.lint.extend-per-file-ignores]
-"hubconf.py" = ["D", "INP001"]
 "examples/visualize_optimizers.py" = ["D103", "D400", "D415"]
-"**/__init__.py" = ["F401"]
 "{tests,examples}/*.py" = ["D", "S101"]
+"**/__init__.py" = ["F401"]
+"hubconf.py" = ["D", "INP001"]
 
 [tool.ruff.lint.isort]
 combine-as-imports = false
@@ -131,9 +129,9 @@ testpaths = "tests"
 
 [tool.coverage.run]
 omit = [
-    "./pytorch_optimizer/optimizer/rotograd.py",
     "./pytorch_optimizer/optimizer/adam_mini.py",
     "./pytorch_optimizer/optimizer/demo.py",
+    "./pytorch_optimizer/optimizer/rotograd.py",
 ]
 
 [build-system]
diff --git a/pytorch_optimizer/__init__.py b/pytorch_optimizer/__init__.py
index c0c92f01..5088dbb0 100644
--- a/pytorch_optimizer/__init__.py
+++ b/pytorch_optimizer/__init__.py
@@ -113,6 +113,7 @@
     Gravity,
     GrokFastAdamW,
     Kate,
+    Kron,
     Lamb,
     LaProp,
     Lion,
diff --git a/pytorch_optimizer/base/optimizer.py b/pytorch_optimizer/base/optimizer.py
index f32d888a..a9ee8666 100644
--- a/pytorch_optimizer/base/optimizer.py
+++ b/pytorch_optimizer/base/optimizer.py
@@ -338,7 +338,7 @@ def validate_step(step: int, step_type: str) -> None:
     @staticmethod
     def validate_options(x: str, name: str, options: List[str]) -> None:
         if x not in options:
-            opts: str = ' or '.join([f'\'{option}\'' for option in options]).strip()
+            opts: str = ' or '.join([f"'{option}'" for option in options]).strip()
             raise ValueError(f'[-] {name} {x} must be one of ({opts})')
 
     @staticmethod
diff --git a/pytorch_optimizer/optimizer/__init__.py b/pytorch_optimizer/optimizer/__init__.py
index a7603b37..ba6bbb9e 100644
--- a/pytorch_optimizer/optimizer/__init__.py
+++ b/pytorch_optimizer/optimizer/__init__.py
@@ -70,6 +70,7 @@
 from pytorch_optimizer.optimizer.pid import PID
 from pytorch_optimizer.optimizer.pnm import PNM
 from pytorch_optimizer.optimizer.prodigy import Prodigy
+from pytorch_optimizer.optimizer.psgd import Kron
 from pytorch_optimizer.optimizer.qhadam import QHAdam
 from pytorch_optimizer.optimizer.qhm import QHM
 from pytorch_optimizer.optimizer.radam import RAdam
@@ -293,6 +294,7 @@ def load_optimizer(optimizer: str) -> OPTIMIZER:
     FOCUS,
     Grams,
     SPAM,
+    Kron,
     Ranger25,
 ]
 OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST}
diff --git a/pytorch_optimizer/optimizer/ftrl.py b/pytorch_optimizer/optimizer/ftrl.py
index 3b1d917e..48e86b78 100644
--- a/pytorch_optimizer/optimizer/ftrl.py
+++ b/pytorch_optimizer/optimizer/ftrl.py
@@ -25,7 +25,7 @@ def __init__(
         beta: float = 0.0,
         lambda_1: float = 0.0,
         lambda_2: float = 0.0,
-        **kwargs
+        **kwargs,
     ):
         self.validate_learning_rate(lr)
         self.validate_non_negative(beta, 'beta')
diff --git a/pytorch_optimizer/optimizer/muon.py b/pytorch_optimizer/optimizer/muon.py
index 400cb095..3008df69 100644
--- a/pytorch_optimizer/optimizer/muon.py
+++ b/pytorch_optimizer/optimizer/muon.py
@@ -95,10 +95,10 @@ def get_parameters(params: PARAMETERS) -> List[torch.Tensor]:
 
         return new_params
 
-    def set_muon_state(self, params: PARAMETERS, adamw_params: PARAMETERS, threshold: int = 8192) -> None:
+    def set_muon_state(self, params: PARAMETERS, adamw_params: PARAMETERS) -> None:
         r"""Set use_muon flag."""
         for p in params:
-            self.state[p]['use_muon'] = p.ndim >= 2 and p.size(0) < threshold
+            self.state[p]['use_muon'] = p.ndim >= 2
 
         for p in adamw_params:
             self.state[p]['use_muon'] = False
diff --git a/pytorch_optimizer/optimizer/psgd.py b/pytorch_optimizer/optimizer/psgd.py
new file mode 100644
index 00000000..26934985
--- /dev/null
+++ b/pytorch_optimizer/optimizer/psgd.py
@@ -0,0 +1,375 @@
+import math
+from string import ascii_lowercase, ascii_uppercase
+from typing import Callable, List, Literal, Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from pytorch_optimizer.base.exception import NoSparseGradientError
+from pytorch_optimizer.base.optimizer import BaseOptimizer
+from pytorch_optimizer.base.types import CLOSURE, LOSS, PARAMETERS
+from pytorch_optimizer.optimizer.psgd_utils import norm_lower_bound
+
+MEMORY_SAVE_MODE_TYPE = Literal['one_diag', 'smart_one_diag', 'all_diag']
+
+
+def precondition_update_prob_schedule(
+    max_prob: float = 1.0, min_prob: float = 0.03, decay: float = 0.001, flat_start: int = 500
+) -> Callable[[int], torch.Tensor]:
+    """Anneal pre-conditioner update probability during beginning of training.
+
+    PSGD benefits from more pre-conditioner updates at the beginning of training, but once the pre-conditioner is
+    learned the update probability can drop low.
+
+    This schedule is an exponential anneal with a flat start. Default settings keep update probability at 1.0 for 200
+    steps then exponentially anneal down to `min_prob` by 4000 steps. Default settings work very well for most models
+    and training regimes.
+    """
+
+    def _schedule(n: int) -> torch.Tensor:
+        """Exponential anneal with flat start."""
+        n = torch.tensor(n, dtype=torch.float32)
+        prob = max_prob * torch.exp(-decay * (n - flat_start))
+        prob.clamp_(min=min_prob, max=max_prob)
+        return prob
+
+    return _schedule
+
+
+class Kron(BaseOptimizer):
+    """PSGD with the Kronecker product pre-conditioner.
+
+    :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
+    :param lr: float. learning rate.
+    :param momentum: float. momentum factor.
+    :param weight_decay: float. weight decay (L2 penalty).
+    :param weight_decouple: bool. the optimizer uses decoupled weight decay as in AdamW.
+    :param pre_conditioner_update_probability: Optional[Tuple[Callable, float]]. Probability of updating the
+        pre-conditioner. If None, defaults to a schedule that anneals from 1.0 to 0.03 by 4000 steps.
+    :param max_size_triangular: int. max size for dim's pre-conditioner to be triangular.
+    :param min_ndim_triangular: int. minimum number of dimensions a layer needs to have triangular pre-conditioners.
+    :param memory_save_mode: Optional[str]. None, 'one_diag', or 'all_diag', None is default to set all
+        pre-conditioners to be triangular, 'one_diag' sets the largest or last dim to be diagonal per layer, and
+        'all_diag' sets all pre-conditioners to be diagonal.
+    :param momentum_into_precondition_update: bool. whether to send momentum into pre-conditioner update instead of
+        raw gradients.
+    :param mu_dtype: Optional[torch.dtype]. dtype of the momentum accumulator.
+    :param precondition_dtype: torch.dtype. dtype of the pre-conditioner.
+    :param balance_prob: float. probability of performing balancing.
+    """
+
+    def __init__(
+        self,
+        params: PARAMETERS,
+        lr: float = 1e-3,
+        momentum: float = 0.9,
+        weight_decay: float = 0.0,
+        weight_decouple: bool = True,
+        pre_conditioner_update_probability: Optional[Tuple[Callable, float]] = None,
+        max_size_triangular: int = 8192,
+        min_ndim_triangular: int = 2,
+        memory_save_mode: Optional[MEMORY_SAVE_MODE_TYPE] = None,
+        momentum_into_precondition_update: bool = True,
+        mu_dtype: Optional[torch.dtype] = None,
+        precondition_dtype: Optional[torch.dtype] = torch.float32,
+        balance_prob: float = 0.01,
+        **kwargs,
+    ):
+        self.validate_learning_rate(lr)
+        self.validate_range(momentum, 'momentum', 0.0, 1.0)
+        self.validate_non_negative(weight_decay, 'weight_decay')
+
+        if pre_conditioner_update_probability is None:
+            pre_conditioner_update_probability = precondition_update_prob_schedule()
+
+        self.balance_prob: float = balance_prob
+        self.eps: float = torch.finfo(torch.bfloat16).tiny
+        self.prob_step: int = 0
+        self.update_counter: int = 0
+
+        defaults = {
+            'lr': lr,
+            'momentum': momentum,
+            'weight_decay': weight_decay,
+            'weight_decouple': weight_decouple,
+            'pre_conditioner_update_probability': pre_conditioner_update_probability,
+            'max_size_triangular': max_size_triangular,
+            'min_ndim_triangular': min_ndim_triangular,
+            'memory_save_mode': memory_save_mode,
+            'momentum_into_precondition_update': momentum_into_precondition_update,
+            'precondition_lr': 1e-1,
+            'precondition_init_scale': 1.0,
+            'mu_dtype': mu_dtype,
+            'precondition_dtype': precondition_dtype,
+        }
+
+        super().__init__(params, defaults)
+
+    def __str__(self) -> str:
+        return 'Kron'
+
+    @torch.no_grad()
+    def reset(self):
+        for group in self.param_groups:
+            group['step'] = 0
+            for p in group['params']:
+                state = self.state[p]
+
+                state['momentum_buffer'] = p.grad.clone()
+
+    @torch.no_grad()
+    def step(self, closure: CLOSURE = None) -> LOSS:
+        loss: LOSS = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        update_prob: Union[float, Callable] = self.param_groups[0]['pre_conditioner_update_probability']
+        if callable(update_prob):
+            update_prob = update_prob(self.prob_step)
+
+        self.update_counter += 1
+        do_update: bool = self.update_counter >= 1 / update_prob
+        if do_update:
+            self.update_counter = 0
+        self.prob_step += 1
+
+        balance: bool = np.random.random() < self.balance_prob and do_update
+
+        for group in self.param_groups:
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            bias_correction1: float = self.debias(group['momentum'], group['step'])
+
+            mu_dtype, precondition_dtype = group['mu_dtype'], group['precondition_dtype']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                if grad.is_sparse:
+                    raise NoSparseGradientError(str(self))
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['momentum_buffer'] = torch.zeros_like(p, dtype=mu_dtype or p.dtype)
+                    state['Q'], state['expressions'] = initialize_q_expressions(
+                        p,
+                        group['precondition_init_scale'],
+                        group['max_size_triangular'],
+                        group['min_ndim_triangular'],
+                        group['memory_save_mode'],
+                        dtype=precondition_dtype,
+                    )
+
+                momentum_buffer = state['momentum_buffer']
+                momentum_buffer.mul_(group['momentum']).add_(grad, alpha=1.0 - group['momentum'])
+
+                if mu_dtype is not None:
+                    momentum_buffer = momentum_buffer.to(dtype=mu_dtype, non_blocking=True)
+
+                de_biased_momentum = (momentum_buffer / bias_correction1).to(
+                    dtype=precondition_dtype, non_blocking=True
+                )
+
+                if grad.dim() > 1 and balance:
+                    balance_q(state['Q'])
+
+                if do_update:
+                    update_precondition(
+                        state['Q'],
+                        state['expressions'],
+                        torch.randn_like(de_biased_momentum, dtype=precondition_dtype),
+                        de_biased_momentum if group['momentum_into_precondition_update'] else grad,
+                        group['precondition_lr'],
+                        self.eps,
+                    )
+
+                precondition_grad = get_precondition_grad(state['Q'], state['expressions'], de_biased_momentum).to(
+                    dtype=p.dtype, non_blocking=True
+                )
+
+                precondition_grad.mul_(torch.clamp(1.1 / (precondition_grad.square().mean().sqrt() + 1e-6), max=1.0))
+
+                if group['weight_decay'] != 0 and p.dim() >= 2:
+                    precondition_grad.add_(p, alpha=group['weight_decay'])
+
+                p.add_(precondition_grad, alpha=-group['lr'])
+
+        return loss
+
+
+def initialize_q_expressions(
+    t: torch.Tensor,
+    scale: float,
+    max_size: int,
+    min_ndim_triangular: int,
+    memory_save_mode: Optional[MEMORY_SAVE_MODE_TYPE],
+    dtype: Optional[torch.dtype] = None,
+) -> Tuple[List[torch.Tensor], Tuple[str, List[str], str]]:
+    r"""Initialize Q expressions.
+
+    For a scalar or tensor t, we initialize its pre-conditioner Q and reusable einsum expressions for updating Q and
+    pre-conditioning gradient.
+    """
+    letters: str = ascii_lowercase + ascii_uppercase
+
+    dtype: torch.dtype = dtype if dtype is not None else t.dtype
+    shape = t.shape
+    if len(shape) == 0:
+        qs: list[torch.Tensor] = [scale * torch.ones_like(t, dtype=dtype)]
+        expressions_a: str = ',->'
+        expression_gr: List[str] = [',->']
+        expression_r: str = ',,->'
+
+        return qs, (expressions_a, expression_gr, expression_r)
+
+    if len(shape) > 13:
+        raise ValueError(f'got tensor with dim {len(t.shape)}. Einstein runs out of letters!')
+
+    scale = math.pow(scale, 1.0 / len(shape))
+
+    if memory_save_mode is None:
+        dim_diag = [False for _ in shape]
+    elif memory_save_mode == 'one_diag':
+        dim_diag = [False for _ in shape]
+        dim_diag[np.argsort(shape)[::-1][0]] = True
+    elif memory_save_mode == 'smart_one_diag':
+        dim_diag = [False for _ in shape]
+        sorted_shape = sorted(shape)
+        if len(shape) >= 2 and sorted_shape[-1] > sorted_shape[-2]:
+            dim_diag[np.argsort(shape)[::-1][0]] = True
+    elif memory_save_mode == 'all_diag':
+        dim_diag = [True for _ in shape]
+    else:
+        raise NotImplementedError(
+            f'invalid memory_save_mode {memory_save_mode}. '
+            'it must be one of [None, \'one_diag\', \'smart_one_diag\', \'all_diag\']'
+        )
+
+    qs: List[torch.Tensor] = []
+    expr_gr = []
+    piece_1a, piece_2a, piece_3a = [], '', ''
+    piece_1p, piece_2p, piece_3p, piece_4p = [], [], '', ''
+    for i, (size, dim_d) in enumerate(zip(shape, dim_diag)):
+        if size == 1 or size > max_size or len(shape) < min_ndim_triangular or dim_d:
+            qs.append(scale * torch.ones(size, dtype=dtype, device=t.device))
+
+            piece_1a.append(letters[i])
+            piece_2a += letters[i]
+            piece_3a += letters[i]
+
+            piece1: str = ''.join([(letters[i + 13] if j == i else letters[j]) for j in range(len(shape))])
+            expr_gr.append(f'{piece1},{piece1}->{letters[i + 13]}')
+
+            piece_1p.append(letters[i + 13])
+            piece_2p.append(letters[i + 13])
+            piece_3p += letters[i + 13]
+            piece_4p += letters[i + 13]
+        else:
+            qs.append(scale * torch.eye(size, dtype=dtype, device=t.device))
+
+            piece_1a.append(letters[i] + letters[i + 13])
+            piece_2a += letters[i + 13]
+            piece_3a += letters[i]
+
+            piece1: str = ''.join([(letters[i + 13] if j == i else letters[j]) for j in range(len(shape))])
+            piece2: str = ''.join([(letters[i + 26] if j == i else letters[j]) for j in range(len(shape))])
+            expr_gr.append(f'{piece1},{piece2}->{letters[i + 13]}{letters[i + 26]}')
+
+            a, b, c = letters[i], letters[i + 13], letters[i + 26]
+            piece_1p.append(a + b)
+            piece_2p.append(a + c)
+            piece_3p += c
+            piece_4p += b
+
+    expr_a: str = ','.join(piece_1a) + f',{piece_2a}->{piece_3a}'
+    expr_r: str = ','.join(piece_1p) + ',' + ','.join(piece_2p) + f',{piece_3p}->{piece_4p}'
+
+    return qs, (expr_a, expr_gr, expr_r)
+
+
+def balance_q(q_in: List[torch.Tensor]) -> None:
+    r"""Balance Q."""
+    norms = torch.stack([q.norm(float('inf')) for q in q_in])
+    geometric_mean = norms.prod() ** (1 / len(q_in))
+    norms = geometric_mean / norms
+    for i, q in enumerate(q_in):
+        q.mul_(norms[i])
+
+
+def solve_triangular_right(x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:
+    r"""Calculate X @ inv(A)."""
+    orig_dtype: torch.dtype = x.dtype
+    x = x.to(dtype=torch.float32, non_blocking=True)
+    a = a.to(dtype=torch.float32, non_blocking=True)
+    out = torch.linalg.solve_triangular(a, x.reshape(-1, x.size(-1)), upper=True, left=False).reshape_as(x)
+    return out.to(dtype=orig_dtype, non_blocking=True)
+
+
+def get_a_and_conj_b(
+    expr_a: List[str], g: torch.Tensor, qs: List[torch.Tensor], v: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    r"""Get A and b.conj."""
+    a = torch.einsum(expr_a, *qs, g)
+
+    order: int = g.dim()
+    p = list(range(order))
+
+    conj_b = torch.permute(v.conj(), p[1:] + p[:1])
+    for i, q in enumerate(qs):
+        conj_b = conj_b / q if q.dim() < 2 else solve_triangular_right(conj_b, q)
+        if i < order - 1:
+            conj_b = torch.transpose(conj_b, i, order - 1)
+
+    return a, conj_b
+
+
+def get_q_terms(expr_gs: List[str], a: torch.Tensor, conj_b: torch.Tensor) -> List[Tuple[torch.Tensor, torch.Tensor]]:
+    r"""Get Q terms."""
+    terms: List = []
+    for expr_g in expr_gs:
+        term1 = torch.einsum(expr_g, a, a.conj())
+        term2 = torch.einsum(expr_g, conj_b.conj(), conj_b)
+        terms.append((term1, term2))
+    return terms
+
+
+def update_precondition(
+    qs: List[torch.Tensor],
+    expressions: List[Tuple[str, List[str], str]],
+    v: torch.Tensor,
+    g: torch.Tensor,
+    step: int,
+    eps: float,
+) -> None:
+    r"""Update Kronecker product pre-conditioner Q with pair (V, G)."""
+    expr_a, expr_gs, _ = expressions
+
+    a, conj_b = get_a_and_conj_b(expr_a, g, qs, v)
+
+    q_terms: List[Tuple[torch.Tensor, torch.Tensor]] = get_q_terms(expr_gs, a, conj_b)
+
+    for q, (term1, term2) in zip(qs, q_terms):
+        tmp = term1 - term2
+        tmp *= step
+
+        if q.dim() < 2:
+            tmp *= q
+            tmp.div_((term1 + term2).norm(float('inf')).add_(eps))
+        else:
+            tmp = torch.triu(tmp)
+            tmp.div_(norm_lower_bound(term1 + term2).add_(eps))
+            tmp @= q
+
+        q.sub_(tmp)
+
+
+def get_precondition_grad(qs: list[torch.Tensor], expressions: list[str], g: torch.Tensor) -> torch.Tensor:
+    r"""Precondition gradient G with pre-conditioner Q."""
+    return torch.einsum(expressions[-1], *[x.conj() for x in qs], *qs, g)
diff --git a/pytorch_optimizer/optimizer/psgd_utils.py b/pytorch_optimizer/optimizer/psgd_utils.py
new file mode 100644
index 00000000..b80bdb35
--- /dev/null
+++ b/pytorch_optimizer/optimizer/psgd_utils.py
@@ -0,0 +1,94 @@
+from typing import List, Tuple
+
+import torch
+from torch.linalg import vector_norm
+
+
+def damped_pair_vg(g: torch.Tensor, damp: float = 2 ** -13) -> Tuple[torch.Tensor, torch.Tensor]:  # fmt: skip
+    r"""Get damped pair v and g.
+
+    Instead of return (v, g), it returns pair (v, g + sqrt(eps)*mean(abs(g))*v)
+    such that the covariance matrix of the modified g is lower bound by eps * (mean(abs(g)))**2 * I
+    This should damp the pre-conditioner to encourage numerical stability.
+    The default amount of damping is 2**(-13), slightly smaller than sqrt(eps('single')).
+
+    If v is integrated out, let's just use the modified g;
+    If hvp is used, recommend to use L2 regularization to lower bound the Hessian, although this method also works.
+
+    Please check example
+        https://github.com/lixilinx/psgd_torch/blob/master/misc/psgd_with_finite_precision_arithmetic.py
+    for the rationale to set default damping level to 2**(-13).
+    """
+    v = torch.randn_like(g)
+    return v, g + damp * torch.mean(torch.abs(g)) * v
+
+
+def norm_lower_bound(a: torch.Tensor) -> torch.Tensor:
+    r"""Get a cheap lower bound for the spectral norm of A.
+
+    Numerical results on random matrices with a wide range of distributions and sizes suggest,
+    norm(A) <= sqrt(2) * norm_lower_bound(A)
+    Looks to be a very tight lower bound.
+    """
+    max_abs = torch.max(torch.abs(a))
+    if max_abs <= 0:
+        return max_abs
+
+    a.div_(max_abs)
+
+    aa = torch.real(a * a.conj())
+    value0, i = torch.max(torch.sum(aa, dim=0), 0)
+    value1, j = torch.max(torch.sum(aa, dim=1), 0)
+
+    if value0 > value1:
+        x = a[:, i].conj() @ a
+        return max_abs * vector_norm((x / vector_norm(x)) @ a.H)
+
+    x = a @ a[j].conj()
+    return max_abs * vector_norm(a.H @ (x / vector_norm(x)))
+
+
+def woodbury_identity(inv_a: torch.Tensor, u: torch.Tensor, v: torch.Tensor) -> None:
+    r"""Get the Woodbury identity.
+
+    inv(A + U * V) = inv(A) - inv(A) * U * inv(I + V * inv(A) * U) * V * inv(A)
+
+    with inplace update of inv_a.
+
+    Note that using the Woodbury identity multiple times could accumulate numerical errors.
+    """
+    inv_au = inv_a @ u
+    v_inv_au = v @ inv_au
+
+    ident = torch.eye(v_inv_au.shape[0], dtype=v_inv_au.dtype, device=v_inv_au.device)
+    inv_a.sub_(inv_au @ torch.linalg.solve(ident + v_inv_au, v @ inv_a))
+
+
+def triu_with_diagonal_and_above(a: torch.Tensor) -> torch.Tensor:
+    r"""Get triu with diagonal and above.
+
+    It is useful as for a small A, the R of QR decomposition qr(I + A) is about I + triu(A, 0) + triu(A, 1)
+    """
+    return torch.triu(a, diagonal=0) + torch.triu(a, diagonal=1)
+
+
+def update_precondition_dense(
+    q: torch.Tensor, dxs: List[torch.Tensor], dgs: List[torch.Tensor], step: float = 0.01, eps: float = 1.2e-38
+) -> torch.Tensor:
+    r"""Update dense pre-conditioner P = Q^T * Q.
+
+    :param q: torch.Tensor. Cholesky factor of pre-conditioner with positive diagonal entries.
+    :param dxs: List[torch.Tensor]. list of perturbations of parameters.
+    :param dgs: List[torch.Tensor]. list of perturbations of gradients.
+    :param step: float. update step size normalized to range [0, 1].
+    :param eps: float. an offset to avoid division by zero.
+    """
+    dx = torch.cat([torch.reshape(x, [-1, 1]) for x in dxs])
+    dg = torch.cat([torch.reshape(g, [-1, 1]) for g in dgs])
+
+    a = q.mm(dg)
+    b = torch.linalg.solve_triangular(q.t(), dx, upper=False)
+
+    grad = torch.triu(a.mm(a.t()) - b.mm(b.t()))
+
+    return q - (step / norm_lower_bound(grad).add_(eps)) * grad.mm(q)
diff --git a/pytorch_optimizer/optimizer/rotograd.py b/pytorch_optimizer/optimizer/rotograd.py
index 4bed7224..3873383d 100644
--- a/pytorch_optimizer/optimizer/rotograd.py
+++ b/pytorch_optimizer/optimizer/rotograd.py
@@ -378,7 +378,7 @@ def _rep_grad(self):
             self.initial_grads = grad_norms
             conv_ratios = [torch.ones((1,)) for _ in range(len(self.initial_grads))]
         else:
-            conv_ratios = [x / y for x, y, in zip(grad_norms, self.initial_grads)]
+            conv_ratios = [x / y for x, y in zip(grad_norms, self.initial_grads)]
 
         self.counter += 1
 
diff --git a/pytorch_optimizer/optimizer/shampoo_utils.py b/pytorch_optimizer/optimizer/shampoo_utils.py
index d6183d0f..cb6746d7 100644
--- a/pytorch_optimizer/optimizer/shampoo_utils.py
+++ b/pytorch_optimizer/optimizer/shampoo_utils.py
@@ -186,7 +186,8 @@ def merge_partitions(self, partitions: List[torch.Tensor]) -> torch.Tensor:
             n: int = len(indices) + 1
 
             partitions: List[torch.Tensor] = [
-                torch.cat(partitions[idx:idx + n], dim=i) for idx in range(0, len(partitions), n)  # fmt: skip
+                torch.cat(partitions[idx:idx + n], dim=i)
+                for idx in range(0, len(partitions), n)  # fmt: skip
             ]
 
         return partitions[0]
@@ -367,7 +368,7 @@ def preconditioned_grad(self, grad: torch.Tensor) -> torch.Tensor:
             self.precondition_block(
                 partitioned_grad,
                 self.should_precondition_dims,
-                self.pre_conditioners[i * self.rank:(i + 1) * self.rank]  # fmt: skip
+                self.pre_conditioners[i * self.rank:(i + 1) * self.rank],  # fmt: skip
             )
             for i, partitioned_grad in enumerate(partitioned_grads)
         ]
diff --git a/pytorch_optimizer/optimizer/utils.py b/pytorch_optimizer/optimizer/utils.py
index befce42c..4c95e347 100644
--- a/pytorch_optimizer/optimizer/utils.py
+++ b/pytorch_optimizer/optimizer/utils.py
@@ -48,8 +48,7 @@ def is_deepspeed_zero3_enabled() -> bool:
             return is_deepspeed_zero3_enabled()  # pragma: no cover
 
         warnings.warn(
-            'you need to install `transformers` to use `is_deepspeed_zero3_enabled` function. '
-            'it will return False.',
+            'you need to install `transformers` to use `is_deepspeed_zero3_enabled` function. it will return False.',
             category=ImportWarning,
             stacklevel=2,
         )


Test patch:
diff --git a/tests/constants.py b/tests/constants.py
index daecbc52..f37b8e7c 100644
--- a/tests/constants.py
+++ b/tests/constants.py
@@ -63,6 +63,7 @@
     Gravity,
     GrokFastAdamW,
     Kate,
+    Kron,
     Lamb,
     LaProp,
     Lion,
@@ -557,6 +558,7 @@
     (TAM, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
     (AdaTAM, {'lr': 1e-1, 'weight_decay': 1e-3}, 5),
     (FOCUS, {'lr': 1e-1, 'weight_decay': 1e-3}, 5),
+    (Kron, {'lr': 1e0, 'weight_decay': 1e-3}, 3),
     (Ranger25, {'lr': 5e0}, 2),
     (Ranger25, {'lr': 5e0, 't_alpha_beta3': 5}, 2),
     (Ranger25, {'lr': 2e-1, 'stable_adamw': False, 'orthograd': False, 'eps': None}, 3),
diff --git a/tests/test_general_optimizer_parameters.py b/tests/test_general_optimizer_parameters.py
index af841137..932843f3 100644
--- a/tests/test_general_optimizer_parameters.py
+++ b/tests/test_general_optimizer_parameters.py
@@ -55,6 +55,7 @@ def test_epsilon(optimizer_name):
         'demo',
         'muon',
         'focus',
+        'kron',
     ):
         pytest.skip(f'skip {optimizer_name} optimizer')
 
diff --git a/tests/test_load_modules.py b/tests/test_load_modules.py
index 30746147..0aafa1fc 100644
--- a/tests/test_load_modules.py
+++ b/tests/test_load_modules.py
@@ -34,7 +34,7 @@ def test_load_lr_scheduler_invalid(invalid_lr_scheduler_names):
 
 
 def test_get_supported_optimizers():
-    assert len(get_supported_optimizers()) == 91
+    assert len(get_supported_optimizers()) == 92
     assert len(get_supported_optimizers('adam*')) == 7
     assert len(get_supported_optimizers(['adam*', 'ranger*'])) == 10
 
diff --git a/tests/test_optimizers.py b/tests/test_optimizers.py
index 50dd73bf..32b6eb84 100644
--- a/tests/test_optimizers.py
+++ b/tests/test_optimizers.py
@@ -853,3 +853,21 @@ def test_spam_optimizer():
 
     optimizer = load_optimizer('spam')([simple_parameter(True)], grad_accu_steps=0, update_proj_gap=1)
     optimizer.step()
+
+
+def test_kron_optimizer():
+    model = Example()
+
+    optimizer = load_optimizer('kron')(
+        model.parameters(),
+        weight_decay=1e-3,
+        pre_conditioner_update_probability=1.0,
+        balance_prob=1.0,
+        mu_dtype=torch.bfloat16,
+    )
+    optimizer.zero_grad()
+
+    model.fc1.weight.grad = torch.randn((1, 1))
+    model.norm1.weight.grad = torch.randn((1,))
+
+    optimizer.step()
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 19b00069..db72d7fd 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -7,6 +7,14 @@
 
 from pytorch_optimizer.optimizer import get_optimizer_parameters
 from pytorch_optimizer.optimizer.nero import neuron_mean, neuron_norm
+from pytorch_optimizer.optimizer.psgd import initialize_q_expressions
+from pytorch_optimizer.optimizer.psgd_utils import (
+    damped_pair_vg,
+    norm_lower_bound,
+    triu_with_diagonal_and_above,
+    update_precondition_dense,
+    woodbury_identity,
+)
 from pytorch_optimizer.optimizer.shampoo_utils import (
     BlockPartitioner,
     PreConditioner,
@@ -271,3 +279,59 @@ def test_orthograd_name():
     _ = optimizer.state
 
     assert str(optimizer).lower() == 'orthograd'
+
+
+def test_damped_pair_vg():
+    x = torch.zeros(2)
+    y = damped_pair_vg(x)[1]
+
+    torch.testing.assert_close(x, y)
+
+
+def test_norm_lower_bound():
+    x = torch.zeros(1)
+    y = norm_lower_bound(x)
+    torch.testing.assert_close(y, x.squeeze())
+
+    x = torch.FloatTensor([[1, 1]])
+    y = norm_lower_bound(x)
+    torch.testing.assert_close(y, torch.tensor(1.4142135))
+
+    x = torch.FloatTensor([[2, 1], [2, 1]])
+    y = norm_lower_bound(x)
+    torch.testing.assert_close(y, torch.tensor(3.16227769))
+
+
+def test_woodbury_identity():
+    x = torch.FloatTensor([[1]])
+    woodbury_identity(x, x, x)
+
+
+def test_triu_with_diagonal_and_above():
+    x = torch.FloatTensor([[1, 2], [3, 4]])
+    y = triu_with_diagonal_and_above(x)
+    torch.testing.assert_close(y, torch.FloatTensor([[1, 4], [0, 4]]))
+
+
+def test_update_precondition_dense():
+    q = torch.FloatTensor([[1]])
+    dxs = [q] * 1
+    dgs = [q] * 1
+
+    y = update_precondition_dense(q, dxs, dgs)
+
+    torch.testing.assert_close(y, q)
+
+
+def test_initialize_q_expressions():
+    x = torch.zeros(1)
+    _ = initialize_q_expressions(x.squeeze(), 0.0, 0, 0, None)
+
+    with pytest.raises(ValueError):
+        initialize_q_expressions(x.expand(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 0.0, 0, 0, None)
+
+    with pytest.raises(NotImplementedError):
+        initialize_q_expressions(x, 0.0, 0, 0, 'invalid')
+
+    for memory_save_mode in ('one_diag', 'all_diag', 'smart_one_diag'):
+        initialize_q_expressions(torch.FloatTensor([[1], [2]]), 0.0, 0, 0, memory_save_mode)