From e8dfe5f5eef84b8ea6c6c2603f04ba107b6b665a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 29 Jul 2024 06:16:06 +0000 Subject: [PATCH 1/2] Bump torch from 2.3.1+cpu to 2.4.0+cpu Bumps torch from 2.3.1+cpu to 2.4.0+cpu. --- updated-dependencies: - dependency-name: torch dependency-type: direct:development update-type: version-update:semver-minor ... 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f"is not a subclass of `{typ.__name__}`." + f"is not a subclass of `{typ_name}`." ) else: raise ValueError( diff --git a/ranzen/torch/optimizers/adafactor.py b/ranzen/torch/optimizers/adafactor.py index 830c2597..546ee2ad 100644 --- a/ranzen/torch/optimizers/adafactor.py +++ b/ranzen/torch/optimizers/adafactor.py @@ -6,7 +6,7 @@ import torch from torch import Tensor -from torch.optim import Optimizer +from torch.optim.optimizer import Optimizer from .common import LossClosure diff --git a/ranzen/torch/schedulers.py b/ranzen/torch/schedulers.py index 7fc81621..6cd9383c 100644 --- a/ranzen/torch/schedulers.py +++ b/ranzen/torch/schedulers.py @@ -6,8 +6,8 @@ import torch from torch import Tensor -from torch.optim import Optimizer from torch.optim.lr_scheduler import CosineAnnealingLR, _LRScheduler +from torch.optim.optimizer import Optimizer __all__ = [ "CosineLRWithLinearWarmup", @@ -128,7 +128,7 @@ def scheduler(self) -> Union[LinearWarmupLR, CosineAnnealingLR]: self._scheduler = CosineAnnealingLR( optimizer=self.optimizer, T_max=self.total_iters - self.warmup_iters + 1, - eta_min=self.lr_min, + eta_min=self.lr_min, # type: ignore ) return self._scheduler diff --git a/tests/optimizers_test.py b/tests/optimizers_test.py index 800461b9..5565edb1 100644 --- a/tests/optimizers_test.py +++ b/tests/optimizers_test.py @@ -1,7 +1,7 @@ import pytest import torch from torch import Tensor -from torch.optim import AdamW +from torch.optim.adamw import AdamW from ranzen.torch.optimizers import Adafactor, LAMB, SAM diff --git a/tests/scheduler_test.py b/tests/scheduler_test.py index b625971f..2ea78a34 100644 --- a/tests/scheduler_test.py +++ b/tests/scheduler_test.py @@ -1,6 +1,7 @@ import pytest import torch -from torch import optim +from torch.optim.adamw import AdamW +from torch.optim.sgd import SGD from ranzen.torch.schedulers import LinearWarmupLR @@ -9,7 +10,7 @@ def test_linear_warmup_lr() -> None: params = (torch.randn(1, 1, requires_grad=True),) base_lr = 1.0 lr_start = 1.0e-1 - optimizer = optim.SGD(params, lr=base_lr) + optimizer = SGD(params, lr=base_lr) scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=1) for group in optimizer.param_groups: assert group["lr"] == lr_start @@ -22,7 +23,7 @@ def _step(): for group in optimizer.param_groups: assert group["lr"] == base_lr - optimizer = optim.AdamW(params, lr=base_lr) + optimizer = AdamW(params, lr=base_lr) scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=0) for group in optimizer.param_groups: assert group["lr"] == base_lr @@ -31,7 +32,7 @@ def _step(): for group in optimizer.param_groups: assert group["lr"] == base_lr - optimizer = optim.AdamW(params, lr=base_lr) + optimizer = AdamW(params, lr=base_lr) scheduler = LinearWarmupLR(optimizer=optimizer, lr_start=lr_start, warmup_iters=2) _step() expected_lr_after_one_step = lr_start + 0.5 * (base_lr - lr_start)