Merge pull request #97 from kozistr/feature/shampoo-optimizer

[Feature] Re-Implement Shampoo Optimizer w/ Grafting & Partitioner

Author: kozistr

.pylintrc

@@ -12,9 +12,8 @@ test:
 	python -m pytest -p no:pastebin -p no:nose -p no:doctest -sv -vv --cov=pytorch_optimizer --cov-report=xml ./tests
 
 check:
-	isort --check-only --profile black -l 119 pytorch_optimizer tests hubconf.py
 	black -S -l 119 --check pytorch_optimizer tests hubconf.py
-	pylint --fail-under=10.0 pytorch_optimizer
+	ruff pytorch_optimizer tests hubconf.py
 
 requirements:
 	python -m poetry export -f requirements.txt --output requirements.txt --without-hashes

Makefile

@@ -17,14 +17,6 @@ get_optimizer_parameters
 .. autoclass:: pytorch_optimizer.get_optimizer_parameters
     :members:
 
-.. _matrix_power:
-
-matrix_power
-------------
-
-.. autoclass:: pytorch_optimizer.matrix_power
-    :members:
-
 .. _normalize_gradient:
 
 normalize_gradient

docs/util_api.rst

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pytorch_optimizer"
-version = "2.2.1"
+version = "2.3.0"
 description = "optimizer & lr scheduler implementations in PyTorch with clean-code, strict types. Also, including useful optimization ideas."
 license = "Apache-2.0"
 authors = ["kozistr <[email protected]>"]
@@ -42,7 +42,7 @@ torch = { version = "^1.10", source = "torch"}
 [tool.poetry.dev-dependencies]
 isort = "^5.11.4"
 black = "^22.12.0"
-pylint = "^2.15.9"
+ruff = "^0.0.237"
 pytest = "^7.2.0"
 pytest-cov = "^4.0.0"
 
@@ -51,9 +51,50 @@ name = "torch"
 url = "https://download.pytorch.org/whl/cpu"
 secondary = true
 
+[tool.ruff]
+select = ["A", "B", "C4", "E", "F", "G", "I", "N", "S", "T", "ISC", "W", "INP", "PIE", "T20", "RET", "SIM", "ARG"]
+ignore = []
+fixable = ["A", "B", "C", "D", "E", "F"]
+unfixable = ["F401"]
+exclude = [
+    ".eggs",
+    ".git",
+    ".mypy_cache",
+    ".ruff_cache",
+    ".github",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "docs",
+    "assets",
+]
+line-length = 119
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+target-version = "py39"
+
+[tool.ruff.per-file-ignores]
+"./hubconf.py" = ["INP001"]
+"./tests/test_utils.py" = ["S101"]
+"./tests/test_gradients.py" = ["S101"]
+"./tests/test_optimizers.py" = ["S101"]
+"./tests/test_optimizer_parameters.py" = ["S101"]
+"./tests/test_load_optimizers.py" = ["S101"]
+"./tests/test_load_lr_schedulers.py" = ["S101"]
+"./tests/test_lr_scheduler_parameters.py" = ["S101"]
+"./pytorch_optimizer/__init__.py" = ["F401"]
+"./pytorch_optimizer/lr_scheduler/__init__.py" = ["F401"]
+
+[tool.ruff.mccabe]
+max-complexity = 10
+
 [tool.coverage.run]
 omit = [
     "./pytorch_optimizer/optimizer/gsam.py",
+    "./pytorch_optimizer/optimizer/fp16.py",
 ]
 
 [build-system]

poetry.lock

@@ -1,4 +1,4 @@
-# pylint: disable=unused-import
+# ruff: noqa
 from typing import Dict, List
 
 from pytorch_optimizer.base.types import OPTIMIZER, SCHEDULER
@@ -44,7 +44,6 @@
     disable_running_stats,
     enable_running_stats,
     get_optimizer_parameters,
-    matrix_power,
     normalize_gradient,
     unit_norm,
 )

pyproject.toml

@@ -2,7 +2,7 @@
 
 import torch
 
-from pytorch_optimizer.base.exception import NegativeLRError
+from pytorch_optimizer.base.exception import NegativeLRError, NegativeStepError
 from pytorch_optimizer.base.types import BETAS
 
 
@@ -90,7 +90,7 @@ def validate_reduction(reduction: str):
     @staticmethod
     def validate_update_frequency(update_frequency: int):
         if update_frequency < 1:
-            raise ValueError(f'[-] update_frequency {update_frequency} must be positive')
+            raise NegativeStepError(f'[-] update_frequency {update_frequency} must be positive')
 
     @staticmethod
     def validate_norm(norm: float):

pytorch_optimizer/__init__.py

@@ -1,2 +1,2 @@
-# pylint: disable=unused-import
+# ruff: noqa
 from torch.optim.lr_scheduler import ConstantLR, CosineAnnealingLR, CosineAnnealingWarmRestarts, CyclicLR, OneCycleLR

pytorch_optimizer/base/optimizer.py

@@ -2,12 +2,12 @@
 
 
 def chebyshev_steps(small_m: float, big_m: float, num_epochs: int) -> np.ndarray:
-    """chebyshev_steps
+    r"""chebyshev_steps
 
     :param small_m: float. stands for 'm' notation.
     :param big_m:  float. stands for 'M' notation.
     :param num_epochs: int. stands for 'T' notation.
-    :return: np.array. chebyshev_steps
+    :return: np.array. chebyshev_steps.
     """
 
     c, r = (big_m + small_m) / 2.0, (big_m - small_m) / 2.0
@@ -26,6 +26,4 @@ def chebyshev_perm(num_epochs: int) -> np.ndarray:
 def get_chebyshev_schedule(num_epochs: int) -> np.ndarray:
     steps: np.ndarray = chebyshev_steps(0.1, 1, num_epochs - 2)
     perm: np.ndarray = chebyshev_perm(num_epochs - 2)
-    chebyshev_schedule = steps[perm]
-
-    return chebyshev_schedule
+    return steps[perm]

pytorch_optimizer/lr_scheduler/__init__.py

@@ -53,15 +53,15 @@ def __init__(
 
         self.validate_parameters()
 
-        defaults: DEFAULTS = dict(
-            lr=lr,
-            betas=betas,
-            eps=eps,
-            weight_decay=weight_decay,
-            amsgrad=amsgrad,
-            adamd_debias_term=adamd_debias_term,
-            buffer=[[None, None, None] for _ in range(10)],
-        )
+        defaults: DEFAULTS = {
+            'lr': lr,
+            'betas': betas,
+            'eps': eps,
+            'weight_decay': weight_decay,
+            'amsgrad': amsgrad,
+            'adamd_debias_term': adamd_debias_term,
+            'buffer': [[None, None, None] for _ in range(10)],
+        }
         super().__init__(params, defaults)
 
     def validate_parameters(self):
@@ -71,7 +71,7 @@ def validate_parameters(self):
         self.validate_epsilon(self.eps)
 
     @property
-    def __name__(self) -> str:
+    def __str__(self) -> str:
         return 'AdaBelief'
 
     @torch.no_grad()
@@ -106,7 +106,7 @@ def step(self, closure: CLOSURE = None) -> LOSS:
 
                 grad = p.grad
                 if grad.is_sparse:
-                    raise NoSparseGradientError(self.__name__)
+                    raise NoSparseGradientError(self.__str__)
 
                 state = self.state[p]
                 if len(state) == 0: