Repository: kozistr/pytorch_optimizer

Issue:
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA. Our approach reduces memory usage by up to 65.5% in optimizer states while maintaining both efficiency and performance for pre-training on LLaMA 1B and 7B architectures with C4 dataset with up to 19.7B tokens, and on fine-tuning RoBERTa on GLUE tasks. Our 8-bit GaLore further reduces optimizer memory by up to 82.5% and total training memory by 63.3%, compared to a BF16 baseline. Notably, we demonstrate, for the first time, the feasibility of pre-training a 7B model on consumer GPUs with 24GB memory (e.g., NVIDIA RTX 4090) without model parallel, checkpointing, or offloading strategies.

Paper: https://arxiv.org/abs/2403.03507
Code: https://github.com/jiaweizzhao/GaLore/tree/master


Discussion:

Patch:
diff --git a/README.md b/README.md
index 0633c374..dbfc1a1d 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@
 
 **pytorch-optimizer** is optimizer & lr scheduler collections in PyTorch. 
 I just re-implemented (speed & memory tweaks, plug-ins) the algorithm while based on the original paper. Also, It includes useful and practical optimization ideas.  
-Currently, **62 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
+Currently, **63 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
 
 Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer).
 
@@ -160,6 +160,7 @@ supported_optimizers = get_supported_optimizers()
 | CAME         | *Confidence-guided Adaptive Memory Efficient Optimization*                                        | [github](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/CAME)                            | <https://aclanthology.org/2023.acl-long.243/>                                              | [cite](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/CAME#citation)                        |
 | WSAM         | *Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term*             | [github](https://github.com/intelligent-machine-learning/dlrover/blob/master/atorch/atorch/optimizers/wsam.py) | <https://arxiv.org/abs/2305.15817>                                                         | [cite](https://github.com/intelligent-machine-learning/dlrover)                                                   |
 | Aida         | *A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range*      | [github](https://github.com/guoqiang-zhang-x/Aida-Optimizer)                                                   | <https://arxiv.org/abs/2203.13273>                                                         | [cite](https://github.com/guoqiang-zhang-x/Aida-Optimizer?tab=readme-ov-file#1-brief-description-of-aida)         |
+| GaLore       | *Memory-Efficient LLM Training by Gradient Low-Rank Projection*                                   | [github](https://github.com/jiaweizzhao/GaLore)                                                                | <https://arxiv.org/abs/2403.03507>                                                         | [cite](https://github.com/jiaweizzhao/GaLore/tree/master?tab=readme-ov-file#citation)                             |
 
 ## Supported LR Scheduler
 
diff --git a/docs/changelogs/v3.0.0.md b/docs/changelogs/v3.0.0.md
index 7d4980be..0de82f3c 100644
--- a/docs/changelogs/v3.0.0.md
+++ b/docs/changelogs/v3.0.0.md
@@ -10,21 +10,28 @@ Major version is updated! (`v2.12.0` -> `v3.0.0`) (#164)
   * [A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range](https://arxiv.org/abs/2203.13273)
 * Implement `WSAM` optimizer. (#213, #216)
   * [Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term](https://arxiv.org/abs/2305.15817)
+* Implement `GaLore` optimizer. (#224, #228)
+  * [Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://arxiv.org/abs/2403.03507)
 
-## Dependency
+### Fix
+
+* Fix SRMM to allow operation beyond memory_length. (#227)
+
+### Dependency
 
 * Drop `Python 3.7` support officially. (#221)
   * Please check the [README](https://github.com/kozistr/pytorch_optimizer?tab=readme-ov-file#getting-started).
+* Update `bitsandbytes` to `0.43.0`. (#228)
 
-## Docs
+### Docs
 
 * Add missing parameters in `Ranger21 optimizer` document. (#214, #215)
 * Fix `WSAM` optimizer paper link. (#219)
 
-### Contributions
+## Contributions
 
-thanks to @sdbds
+thanks to @sdbds, @i404788
 
-### Diff
+## Diff
 
 [2.12.0...3.0.0](https://github.com/kozistr/pytorch_optimizer/compare/v2.12.0...v3.0.0)
diff --git a/docs/optimizer.md b/docs/optimizer.md
index 776ea8b7..14d326b4 100644
--- a/docs/optimizer.md
+++ b/docs/optimizer.md
@@ -132,6 +132,10 @@
     :docstring:
     :members:
 
+::: pytorch_optimizer.GaLoreProjector
+    :docstring:
+    :members:
+
 ::: pytorch_optimizer.centralize_gradient
     :docstring:
     :members:
diff --git a/poetry.lock b/poetry.lock
index f40e104c..f8cc6931 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,47 +2,52 @@
 
 [[package]]
 name = "bitsandbytes"
-version = "0.42.0"
+version = "0.43.0"
 description = "k-bit optimizers and matrix multiplication routines."
 optional = true
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
-scipy = "*"
+numpy = "*"
+torch = "*"
+
+[package.extras]
+benchmark = ["matplotlib", "pandas"]
+test = ["scipy"]
 
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+version = "24.3.0"
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 [package.dependencies]
@@ -87,63 +92,63 @@ files = [
 
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+version = "7.4.4"
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 [package.extras]
@@ -413,13 +418,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -465,13 +470,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pytest"
-version = "8.0.2"
+version = "8.1.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -479,21 +484,21 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
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 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.3.0,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.4,<2.0"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
-version = "4.1.0"
+version = "5.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -501,72 +506,34 @@ coverage = {version = ">=5.2.1", extras = ["toml"]}
 pytest = ">=4.6"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
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 name = "ruff"
-version = "0.3.0"
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 description = "An extremely fast Python linter and code formatter, written in Rust."
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 python-versions = ">=3.7"
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-[package.dependencies]
-numpy = ">=1.18.5,<1.26.0"
-
-[package.extras]
-dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
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-
 [[package]]
 name = "sympy"
 version = "1.12"
@@ -594,21 +561,21 @@ files = [
 
 [[package]]
 name = "torch"
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 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
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+    {file = "torch-2.2.2+cpu-cp311-cp311-win_amd64.whl", hash = "sha256:88e63c916e3275fa30a220ee736423a95573b96072ded85e5c0171fd8f37a755"},
+    {file = "torch-2.2.2+cpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:431a747b5a880cf8e1fb6d58db6bfafa6768cbec76517d046854537c03323edf"},
+    {file = "torch-2.2.2+cpu-cp312-cp312-win_amd64.whl", hash = "sha256:2b0cf041f878607a361116945f82ce2dba4b7a747151da7619a63cb5fccb72df"},
+    {file = "torch-2.2.2+cpu-cp38-cp38-linux_x86_64.whl", hash = "sha256:8914ce932168e572a09b4a7e5b0806d279f771dfe58d7e1d8de2291fac4ce69b"},
+    {file = "torch-2.2.2+cpu-cp38-cp38-win_amd64.whl", hash = "sha256:4ef2911ffde6d86f643c23aa99f25f1a1df8bee93bf8d0c69cf1b9ba0ca521dc"},
+    {file = "torch-2.2.2+cpu-cp39-cp39-linux_x86_64.whl", hash = "sha256:6e3d323a21df22415770e88d39e13591079b9356dabb8b394d1ee29ac6c92481"},
+    {file = "torch-2.2.2+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:c2c9e7d5e3c7d58e4b78d6aebfa8002af7cda16cde08d0e3ed00300dc21a8efc"},
 ]
 
 [package.dependencies]
@@ -630,13 +597,13 @@ reference = "torch"
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [extras]
@@ -645,4 +612,4 @@ bitsandbytes = ["bitsandbytes"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<4.0.0"
-content-hash = "d7d8dc0d66f37e2f167551d9be0775c778f251db9faeef427a5d66c2a2396d38"
+content-hash = "d51586f8352db14a18dd407b19285c9649564b029e6e6aae52a0d566515e5c81"
diff --git a/pyproject.toml b/pyproject.toml
index 57eadffc..64659710 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,11 +13,11 @@ keywords = [
     "pytorch", "deep-learning", "optimizer", "lr scheduler", "A2Grad", "ASGD", "AccSGD", "AdaBelief", "AdaBound",
     "AdaDelta", "AdaFactor", "AdaMax", "AdaMod", "AdaNorm", "AdaPNM", "AdaSmooth", "AdaHessian", "Adai", "AdamP",
     "AdamS", "Adan", "AggMo", "Aida", "AliG", "Amos", "Apollo", "AvaGrad", "CAME", "DAdaptAdaGrad", "DAdaptAdam",
-    "DAdaptAdan", "DAdaptSGD", "DAdaptLion", "DiffGrad", "Fromage", "Gravity", "GSAM", "LARS", "Lamb", "Lion", "LOMO",
-    "Lookahead", "MADGRAD", "MSVAG", "Nero", "NovoGrad", "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "QHAdam", "QHM",
-    "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM", "SGDP", "Shampoo", "ScalableShampoo", "SGDW", "SignSGD", "SM3",
-    "SopihaH", "SRMM", "SWATS", "Tiger", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine", "SoftF1", "Dice",
-    "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", "bitsandbytes",
+    "DAdaptAdan", "DAdaptSGD", "DAdaptLion", "DiffGrad", "Fromage", "GaLore", "Gravity", "GSAM", "LARS", "Lamb", "Lion",
+    "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Nero", "NovoGrad", "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "QHAdam",
+    "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM", "SGDP", "Shampoo", "ScalableShampoo", "SGDW", "SignSGD",
+    "SM3", "SopihaH", "SRMM", "SWATS", "Tiger", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine", "SoftF1",
+    "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", "bitsandbytes",
 ]
 classifiers = [
     "License :: OSI Approved :: Apache Software License",
@@ -45,14 +45,14 @@ classifiers = [
 python = ">=3.8,<4.0.0"
 numpy = { version = "*", python = ">=3.8" }
 torch = { version = ">=1.10", python = ">=3.8", source = "torch" }
-bitsandbytes = { version = "^0.42", optional = true }
+bitsandbytes = { version = "^0.43", optional = true }
 
 [tool.poetry.dev-dependencies]
 isort = { version = "^5", python = ">=3.8" }
 black = { version = "^24", python = ">=3.8"}
-ruff = "^0.3"
-pytest = "^8"
-pytest-cov = "^4"
+ruff = "*"
+pytest = "*"
+pytest-cov = "*"
 
 [tool.poetry.extras]
 bitsandbytes = ["bitsandbytes"]
diff --git a/pytorch_optimizer/__init__.py b/pytorch_optimizer/__init__.py
index 8ccdb7c3..f0f1c24e 100644
--- a/pytorch_optimizer/__init__.py
+++ b/pytorch_optimizer/__init__.py
@@ -55,6 +55,7 @@ from pytorch_optimizer.optimizer.dadapt import DAdaptAdaGrad, DAdaptAdam, DAdapt
 from pytorch_optimizer.optimizer.diffgrad import DiffGrad
 from pytorch_optimizer.optimizer.fp16 import DynamicLossScaler, SafeFP16Optimizer
 from pytorch_optimizer.optimizer.fromage import Fromage
+from pytorch_optimizer.optimizer.galore import GaLore, GaLoreProjector
 from pytorch_optimizer.optimizer.gc import centralize_gradient
 from pytorch_optimizer.optimizer.gravity import Gravity
 from pytorch_optimizer.optimizer.lamb import Lamb
@@ -182,6 +183,7 @@ OPTIMIZER_LIST: List[OPTIMIZER] = [
     CAME,
     DAdaptLion,
     Aida,
+    GaLore,
 ]
 OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST}
 
diff --git a/pytorch_optimizer/optimizer/galore.py b/pytorch_optimizer/optimizer/galore.py
new file mode 100644
index 00000000..ced850c2
--- /dev/null
+++ b/pytorch_optimizer/optimizer/galore.py
@@ -0,0 +1,249 @@
+import math
+from typing import Literal, Optional, Tuple, Union
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+from pytorch_optimizer.base.exception import NoSparseGradientError
+from pytorch_optimizer.base.optimizer import BaseOptimizer
+from pytorch_optimizer.base.types import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
+
+PROJECTION_TYPE = Literal['std', 'reverse_std', 'right', 'left', 'full']
+
+
+class GaLoreProjector:
+    r"""Memory-Efficient LLM Training by Gradient Low-Rank Projection.
+
+    :param rank: int. low rank to project.
+    :param update_proj_gap: int. num steps to update the projection.
+    :param scale: float. scale factor.
+    :param projection_type: PROJECTION_TYPE. type of projection. 'std', 'reverse_std', 'right', 'left', 'full' are
+        supported.
+    """
+
+    def __init__(
+        self, rank: int = 128, update_proj_gap: int = 50, scale: float = 1.0, projection_type: PROJECTION_TYPE = 'std'
+    ):
+        self.rank = rank
+        self.update_proj_gap = update_proj_gap
+        self.scale = scale
+        self.projection_type = projection_type
+
+        self.ortho_matrix: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None
+
+    @staticmethod
+    def get_orthogonal_matrix(
+        weights: torch.Tensor, rank: int, projection_type: str
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        if projection_type not in {'right', 'left', 'full'}:
+            raise ValueError('projection_type should be one of left, right or full')
+
+        original_type = weights.data.dtype
+        original_device = weights.data.device
+        is_float: bool = original_type == torch.float
+
+        u, s, vh = torch.linalg.svd(weights if is_float else weights.float(), full_matrices=False)
+
+        if projection_type == 'right':
+            b = vh[:rank, :]
+            return b if is_float else b.to(original_device).type(original_type)
+        if projection_type == 'left':
+            a = u[:, :rank]
+            return a if is_float else a.to(original_device).type(original_type)
+
+        a = u[:, :rank]
+        b = vh[:rank, :]
+
+        return (
+            (a, b)
+            if is_float
+            else (a.to(original_device).type(original_type), b.to(original_device).type(original_type))
+        )
+
+    def get_low_rank_grad_std(self, grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if grad.shape[0] >= grad.shape[1]:
+            if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+                self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='right')
+            return torch.matmul(grad, self.ortho_matrix.t())
+
+        if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+            self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='left')
+
+        return torch.matmul(self.ortho_matrix.t(), grad)
+
+    def get_low_rank_grad_reverse_std(self, grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if grad.shape[0] >= grad.shape[1]:
+            if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+                self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='left')
+            return torch.matmul(self.ortho_matrix.t(), grad)
+
+        if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+            self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='right')
+
+        return torch.matmul(grad, self.ortho_matrix.t())
+
+    def get_low_rank_grad_right(self, grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+            self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='right')
+        return torch.matmul(grad, self.ortho_matrix.t())
+
+    def get_low_rank_grad_left(self, grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+            self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='left')
+        return torch.matmul(self.ortho_matrix.t(), grad)
+
+    def get_low_rank_grad_full(self, grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if self.ortho_matrix is None or steps % self.update_proj_gap == 0:
+            self.ortho_matrix = self.get_orthogonal_matrix(grad, self.rank, projection_type='full')
+        return torch.matmul(self.ortho_matrix[0].t(), grad) @ self.ortho_matrix[1].t()
+
+    def project(self, full_rank_grad: torch.Tensor, steps: int) -> torch.Tensor:
+        if self.projection_type == 'std':
+            return self.get_low_rank_grad_std(full_rank_grad, steps)
+        if self.projection_type == 'reverse_std':
+            return self.get_low_rank_grad_reverse_std(full_rank_grad, steps)
+        if self.projection_type == 'right':
+            return self.get_low_rank_grad_right(full_rank_grad, steps)
+        if self.projection_type == 'left':
+            return self.get_low_rank_grad_left(full_rank_grad, steps)
+        if self.projection_type == 'full':
+            return self.get_low_rank_grad_full(full_rank_grad, steps)
+        raise NotImplementedError
+
+    def project_back(self, low_rank_grad: torch.Tensor) -> torch.Tensor:
+        if self.projection_type == 'std':
+            return (
+                torch.matmul(low_rank_grad, self.ortho_matrix)
+                if low_rank_grad.shape[0] >= low_rank_grad.shape[1]
+                else torch.matmul(self.ortho_matrix, low_rank_grad)
+            ) * self.scale
+        if self.projection_type == 'reverse_std':
+            return (
+                torch.matmul(self.ortho_matrix, low_rank_grad.t())
+                if low_rank_grad.shape[0] <= low_rank_grad.shape[1]
+                else torch.matmul(low_rank_grad, self.ortho_matrix.t())
+            ) * self.scale
+        if self.projection_type == 'right':
+            return torch.matmul(low_rank_grad, self.ortho_matrix.t()) * self.scale
+        if self.projection_type == 'left':
+            return torch.matmul(self.ortho_matrix, low_rank_grad) * self.scale
+        if self.projection_type == 'full':
+            return torch.matmul(self.ortho_matrix[0], low_rank_grad) @ self.ortho_matrix[1].t() * self.scale
+
+        raise NotImplementedError
+
+
+class GaLore(Optimizer, BaseOptimizer):
+    r"""AdamW optimizer with GaLore projector.
+
+    :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
+    :param lr: float. learning rate.
+    :param betas: BETAS. coefficients used for computing running averages of gradient and the squared hessian trace.
+    :param weight_decay: float. weight decay (L2 penalty).
+    :param eps: float. term added to the denominator to improve numerical stability.
+    """
+
+    def __init__(
+        self,
+        params: PARAMETERS,
+        lr: float = 1e-3,
+        betas: BETAS = (0.9, 0.999),
+        weight_decay: float = 0.0,
+        eps: float = 1e-6,
+        **kwargs,
+    ):
+        self.validate_learning_rate(lr)
+        self.validate_betas(betas)
+        self.validate_non_negative(weight_decay, 'weight_decay')
+        self.validate_non_negative(eps, 'eps')
+
+        defaults: DEFAULTS = {
+            'lr': lr,
+            'betas': betas,
+            'weight_decay': weight_decay,
+            'eps': eps,
+            **kwargs,
+        }
+
+        super().__init__(params, defaults)
+
+    def __str__(self) -> str:
+        return 'GaLore'
+
+    @torch.no_grad()
+    def reset(self):
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+
+                state['exp_avg'] = torch.zeros_like(p)
+                state['exp_avg_sq'] = torch.zeros_like(p)
+
+    @torch.no_grad()
+    def step(self, closure: CLOSURE = None) -> LOSS:
+        loss: LOSS = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            beta1, beta2 = group['betas']
+
+            bias_correction1: float = 1.0 - beta1 ** group['step']
+            bias_correction2_sq: float = math.sqrt(1.0 - beta2 ** group['step'])
+
+            step_size: float = group['lr'] * bias_correction2_sq / bias_correction1
+
+            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['exp_avg'] = torch.zeros_like(p)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
+
+                if 'rank' in group and p.dim() > 1:
+                    if 'projector' not in state:
+                        state['projector'] = GaLoreProjector(
+                            rank=group['rank'],
+                            update_proj_gap=group['update_proj_gap'],
+                            scale=group['scale'],
+                            projection_type=group['projection_type'],
+                        )
+
+                    grad = state['projector'].project(grad, group['step'])
+
+                self.apply_weight_decay(
+                    p=p,
+                    grad=None,
+                    lr=group['lr'],
+                    weight_decay=group['weight_decay'],
+                    weight_decouple=True,
+                    fixed_decay=False,
+                )
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
+
+                de_nom = exp_avg_sq.sqrt().add_(group['eps'])
+
+                norm_grad = exp_avg / de_nom
+
+                if 'rank' in group and p.dim() > 1:
+                    norm_grad = state['projector'].project_back(norm_grad)
+
+                p.add_(norm_grad, alpha=-step_size)
+
+        return loss
diff --git a/requirements-dev.txt b/requirements-dev.txt
index c6157e8f..6ed086a4 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,12 +1,12 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
 
-black==24.2.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+black==24.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 click==8.1.7 ; python_version >= "3.8" and python_full_version < "4.0.0"
 colorama==0.4.6 ; python_version >= "3.8" and python_full_version < "4.0.0" and (sys_platform == "win32" or platform_system == "Windows")
-coverage[toml]==7.4.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
+coverage[toml]==7.4.4 ; python_version >= "3.8" and python_full_version < "4.0.0"
 exceptiongroup==1.2.0 ; python_version >= "3.8" and python_version < "3.11"
-filelock==3.13.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
-fsspec==2024.2.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+filelock==3.13.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
+fsspec==2024.3.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 iniconfig==2.0.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 isort==5.13.2 ; python_version >= "3.8" and python_full_version < "4.0.0"
 jinja2==3.1.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
@@ -15,14 +15,14 @@ mpmath==1.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 mypy-extensions==1.0.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 networkx==3.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 numpy==1.24.4 ; python_version >= "3.8" and python_full_version < "4.0.0"
-packaging==23.2 ; python_version >= "3.8" and python_full_version < "4.0.0"
+packaging==24.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 pathspec==0.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 platformdirs==4.2.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 pluggy==1.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
-pytest-cov==4.1.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
-pytest==8.0.2 ; python_version >= "3.8" and python_full_version < "4.0.0"
-ruff==0.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+pytest-cov==5.0.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+pytest==8.1.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
+ruff==0.3.5 ; python_version >= "3.8" and python_full_version < "4.0.0"
 sympy==1.12 ; python_version >= "3.8" and python_full_version < "4.0.0"
 tomli==2.0.1 ; python_version >= "3.8" and python_full_version <= "3.11.0a6"
-torch==2.2.1+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
-typing-extensions==4.10.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+torch==2.2.2+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
+typing-extensions==4.11.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
diff --git a/requirements.txt b/requirements.txt
index b6e6a1df..fe27e8f9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,12 +1,12 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
 
-filelock==3.13.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
-fsspec==2024.2.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+filelock==3.13.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
+fsspec==2024.3.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 jinja2==3.1.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
 markupsafe==2.1.5 ; python_version >= "3.8" and python_full_version < "4.0.0"
 mpmath==1.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 networkx==3.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 numpy==1.24.4 ; python_version >= "3.8" and python_full_version < "4.0.0"
 sympy==1.12 ; python_version >= "3.8" and python_full_version < "4.0.0"
-torch==2.2.1+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
-typing-extensions==4.10.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+torch==2.2.2+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
+typing-extensions==4.11.0 ; python_version >= "3.8" and python_full_version < "4.0.0"


Test patch:
diff --git a/tests/constants.py b/tests/constants.py
index 926dfb9b..f7ffc4b4 100644
--- a/tests/constants.py
+++ b/tests/constants.py
@@ -45,6 +45,7 @@ from pytorch_optimizer import (
     DAdaptSGD,
     DiffGrad,
     Fromage,
+    GaLore,
     Gravity,
     Lamb,
     Lion,
@@ -401,6 +402,38 @@ OPTIMIZERS: List[Tuple[Any, Dict[str, Union[float, bool, int]], int]] = [
     (CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3}, 75),
     (CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3, 'ams_bound': True}, 75),
     (Aida, {'lr': 1e0, 'weight_decay': 1e-3, 'ams_bound': True}, 5),
+    (
+        GaLore,
+        {'lr': 1e0, 'weight_decay': 1e-3, 'rank': 2, 'scale': 1.0, 'update_proj_gap': 1, 'projection_type': 'std'},
+        5,
+    ),
+    (
+        GaLore,
+        {
+            'lr': 1e0,
+            'weight_decay': 1e-3,
+            'rank': 2,
+            'scale': 1.0,
+            'update_proj_gap': 1,
+            'projection_type': 'reverse_std',
+        },
+        5,
+    ),
+    (
+        GaLore,
+        {'lr': 5e-1, 'weight_decay': 1e-3, 'rank': 2, 'scale': 1.0, 'update_proj_gap': 2, 'projection_type': 'left'},
+        5,
+    ),
+    (
+        GaLore,
+        {'lr': 1e0, 'weight_decay': 1e-3, 'rank': 2, 'scale': 1.0, 'update_proj_gap': 1, 'projection_type': 'right'},
+        5,
+    ),
+    (
+        GaLore,
+        {'lr': 5e-1, 'weight_decay': 1e-3, 'rank': 2, 'scale': 1.0, 'update_proj_gap': 2, 'projection_type': 'full'},
+        5,
+    ),
 ]
 ADANORM_SUPPORTED_OPTIMIZERS: List[Tuple[Any, Dict[str, Union[float, bool, int]], int]] = [
     (AdaBelief, {'lr': 5e-1, 'weight_decay': 1e-3, 'adanorm': True}, 10),
diff --git a/tests/test_load_modules.py b/tests/test_load_modules.py
index 57b4fd8d..b7812180 100644
--- a/tests/test_load_modules.py
+++ b/tests/test_load_modules.py
@@ -38,7 +38,7 @@ def test_load_lr_scheduler_invalid(invalid_lr_scheduler_names):
 
 
 def test_get_supported_optimizers():
-    assert len(get_supported_optimizers()) == 61
+    assert len(get_supported_optimizers()) == 62
 
 
 def test_get_supported_lr_schedulers():
diff --git a/tests/test_optimizer_parameters.py b/tests/test_optimizer_parameters.py
index 26d7ff59..433b3d98 100644
--- a/tests/test_optimizer_parameters.py
+++ b/tests/test_optimizer_parameters.py
@@ -2,7 +2,16 @@ import pytest
 import torch
 from torch import nn
 
-from pytorch_optimizer import SAM, WSAM, Lookahead, PCGrad, Ranger21, SafeFP16Optimizer, load_optimizer
+from pytorch_optimizer import (
+    SAM,
+    WSAM,
+    GaLoreProjector,
+    Lookahead,
+    PCGrad,
+    Ranger21,
+    SafeFP16Optimizer,
+    load_optimizer,
+)
 from tests.constants import PULLBACK_MOMENTUM
 from tests.utils import Example, simple_parameter, simple_zero_rank_parameter
 
@@ -254,3 +263,16 @@ def test_ranger_parameters():
     # test lookahead step `k`
     with pytest.raises(ValueError):
         opt(None, k=-1)
+
+
+def test_galore_projection_type():
+    p = torch.tensor([[1.0, 2.0], [3.0, 4.0]], dtype=torch.float32)
+
+    with pytest.raises(NotImplementedError):
+        GaLoreProjector(projection_type='invalid').project(p, 1)
+
+    with pytest.raises(NotImplementedError):
+        GaLoreProjector(projection_type='invalid').project_back(p)
+
+    with pytest.raises(ValueError):
+        GaLoreProjector.get_orthogonal_matrix(p, 1, projection_type='std')