add support for pytorch 2.2 (2.1 is still supported)

also bump all dev dependencies to their latest version

pyproject.toml

@@ -54,11 +54,11 @@ build-backend = "hatchling.build"
 [tool.rye]
 managed = true
 dev-dependencies = [
-    "pyright == 1.1.342",
-    "ruff>=0.0.292",
+    "pyright==1.1.349",
+    "ruff>=0.1.15",
     "docformatter>=1.7.5",
-    "pytest>=7.4.2",
-    "mkdocs-material>=9.5.3",
+    "pytest>=8.0.0",
+    "mkdocs-material>=9.5.6",
     "coverage>=7.4.1",
     "mkdocstrings[python]>=0.24.0",
 ]

requirements.lock

@@ -8,38 +8,38 @@
 #   with-sources: false
 
 -e file:.
-aiohttp==3.9.1
+aiohttp==3.9.3
 aiosignal==1.3.1
 annotated-types==0.6.0
 appdirs==1.4.4
 async-timeout==4.0.3
-attrs==23.1.0
-bitsandbytes==0.41.3
+attrs==23.2.0
+bitsandbytes==0.42.0
 certifi==2023.11.17
 charset-normalizer==3.3.2
 click==8.1.7
-datasets==2.15.0
-diffusers==0.24.0
+datasets==2.16.1
+diffusers==0.25.1
 dill==0.3.7
 docker-pycreds==0.4.0
 filelock==3.13.1
-frozenlist==1.4.0
+frozenlist==1.4.1
 fsspec==2023.10.0
 gitdb==4.0.11
-gitpython==3.1.40
-huggingface-hub==0.19.4
+gitpython==3.1.41
+huggingface-hub==0.20.3
 idna==3.6
-importlib-metadata==7.0.0
+importlib-metadata==7.0.1
 invisible-watermark==0.2.0
-jaxtyping==0.2.24
-jinja2==3.1.2
+jaxtyping==0.2.25
+jinja2==3.1.3
 loguru==0.7.2
-markupsafe==2.1.3
+markupsafe==2.1.4
 mpmath==1.3.0
 multidict==6.0.4
 multiprocess==0.70.15
 networkx==3.2.1
-numpy==1.26.2
+numpy==1.26.3
 nvidia-cublas-cu12==12.1.3.1
 nvidia-cuda-cupti-cu12==12.1.105
 nvidia-cuda-nvrtc-cu12==12.1.105
@@ -49,49 +49,49 @@ nvidia-cufft-cu12==11.0.2.54
 nvidia-curand-cu12==10.3.2.106
 nvidia-cusolver-cu12==11.4.5.107
 nvidia-cusparse-cu12==12.1.0.106
-nvidia-nccl-cu12==2.18.1
+nvidia-nccl-cu12==2.19.3
 nvidia-nvjitlink-cu12==12.3.101
 nvidia-nvtx-cu12==12.1.105
-opencv-python==4.8.1.78
+opencv-python==4.9.0.80
 packaging==23.2
-pandas==2.1.4
-pillow==10.1.0
+pandas==2.2.0
+pillow==10.2.0
 piq==0.8.0
 prodigyopt==1.0
-protobuf==4.25.1
-psutil==5.9.6
-pyarrow==14.0.1
+protobuf==4.25.2
+psutil==5.9.8
+pyarrow==15.0.0
 pyarrow-hotfix==0.6
-pydantic==2.5.2
-pydantic-core==2.14.5
+pydantic==2.6.0
+pydantic-core==2.16.1
 python-dateutil==2.8.2
-pytz==2023.3.post1
+pytz==2023.4
 pywavelets==1.5.0
 pyyaml==6.0.1
-regex==2023.10.3
+regex==2023.12.25
 requests==2.31.0
-safetensors==0.4.1
-scipy==1.11.4
+safetensors==0.4.2
+scipy==1.12.0
 segment-anything-py==1.0
-sentry-sdk==1.38.0
+sentry-sdk==1.40.0
 setproctitle==1.3.3
 six==1.16.0
 smmap==5.0.1
 sympy==1.12
-tokenizers==0.15.0
+tokenizers==0.15.1
 tomli==2.0.1
-torch==2.1.1
-torchvision==0.16.1
+torch==2.2.0
+torchvision==0.17.0
 tqdm==4.66.1
-transformers==4.35.2
-triton==2.1.0
+transformers==4.37.2
+triton==2.2.0
 typeguard==2.13.3
-typing-extensions==4.8.0
-tzdata==2023.3
-urllib3==2.1.0
-wandb==0.16.1
+typing-extensions==4.9.0
+tzdata==2023.4
+urllib3==2.2.0
+wandb==0.16.2
 xxhash==3.4.1
 yarl==1.9.4
 zipp==3.17.0
 # The following packages are considered to be unsafe in a requirements file:
-setuptools==69.0.2
+setuptools==69.0.3

src/refiners/foundationals/clip/concepts.py

@@ -21,10 +21,11 @@ class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
     ) -> None:
         with self.setup_adapter(target):
             super().__init__(fl.Lambda(func=self.lookup))
-        self.old_weight = cast(Parameter, target.weight)
-        self.new_weight = Parameter(
+        p = Parameter(
             zeros([0, target.embedding_dim], device=target.device, dtype=target.dtype)
         )  # requires_grad=True by default
+        self.old_weight = cast(Parameter, target.weight)
+        self.new_weight = cast(Parameter, p)  # PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
 
     # Use F.embedding instead of nn.Embedding to make sure that gradients can only be computed for the new embeddings
     def lookup(self, x: Tensor) -> Tensor:
@@ -33,9 +34,8 @@ class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):
 
     def add_embedding(self, embedding: Tensor) -> None:
         assert embedding.shape == (self.old_weight.shape[1],)
-        self.new_weight = Parameter(
-            cat([self.new_weight, embedding.unsqueeze(0).to(self.new_weight.device, self.new_weight.dtype)])
-        )
+        p = Parameter(cat([self.new_weight, embedding.unsqueeze(0).to(self.new_weight.device, self.new_weight.dtype)]))
+        self.new_weight = cast(Parameter, p)  # PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
 
     @property
     def num_embeddings(self) -> int:

src/refiners/foundationals/dinov2/vit.py

@@ -1,3 +1,5 @@
+from typing import cast
+
 import torch
 from torch import Tensor
 
@@ -60,18 +62,18 @@ class LayerScale(fl.WeightedModule):
         super().__init__()
         self.embedding_dim = embedding_dim
 
-        self.register_parameter(
-            name="weight",
-            param=torch.nn.Parameter(
-                torch.full(
-                    size=(embedding_dim,),
-                    fill_value=init_value,
-                    dtype=dtype,
-                    device=device,
-                ),
+        p = torch.nn.Parameter(
+            torch.full(
+                size=(embedding_dim,),
+                fill_value=init_value,
+                dtype=dtype,
+                device=device,
             ),
         )
 
+        # cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
+        self.register_parameter(name="weight", param=cast(torch.nn.Parameter, p))
+
     def forward(self, x: Tensor) -> Tensor:
         return x * self.weight
 

src/refiners/foundationals/segment_anything/prompt_encoder.py

@@ -1,5 +1,6 @@
 from collections.abc import Sequence
 from enum import Enum, auto
+from typing import cast
 
 import torch
 from jaxtyping import Float, Int
@@ -180,9 +181,9 @@ class MaskEncoder(fl.Chain):
                 dtype=dtype,
             ),
         )
-        self.register_parameter(
-            "no_mask_embedding", nn.Parameter(torch.randn(1, embedding_dim, device=device, dtype=dtype))
-        )
+        p = nn.Parameter(torch.randn(1, embedding_dim, device=device, dtype=dtype))
+        # cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
+        self.register_parameter("no_mask_embedding", cast(nn.Parameter, p))
 
     def get_no_mask_dense_embedding(
         self, image_embedding_size: tuple[int, int], batch_size: int = 1

tests/training_utils/test_trainer.py

@@ -1,6 +1,7 @@
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
+from typing import cast
 from warnings import warn
 
 import pytest
@@ -100,7 +101,8 @@ def test_count_learnable_parameters_with_params() -> None:
         nn.Parameter(torch.randn(5), requires_grad=False),
         nn.Parameter(torch.randn(3, 3), requires_grad=True),
     ]
-    assert count_learnable_parameters(params) == 13
+    # cast because of PyTorch 2.2, see https://github.com/pytorch/pytorch/issues/118736
+    assert count_learnable_parameters(cast(list[nn.Parameter], params)) == 13
 
 
 def test_count_learnable_parameters_with_model(mock_model: fl.Chain) -> None: