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Bump torch minimum to mitigate CVE-2024-31580 & CVE-2024-31583 and enable numpy 2 compatibility (#8368)
This is a follow-up to the comments made in #8296 (comment). ### Description This bumps the minimum required `torch` version from 1.13.1 to 2.2.0 in the first commit. See GHSA-5pcm-hx3q-hm94 and GHSA-pg7h-5qx3-wjr3 for more details regarding the "High" severity scoring. - https://nvd.nist.gov/vuln/detail/CVE-2024-31580 - https://nvd.nist.gov/vuln/detail/CVE-2024-31583 Additionally, PyTorch added support for numpy 2 starting with PyTorch 2.3.0. The second commit in this PR allows for numpy 1 or numpy 2 to be used with torch>=2.3.0. I have included this commit in this PR as upgrading to torch 2.2 means you might as well update to 2.3 to get the numpy 2 compatibility. A special case is being handled on Windows as PyTorch Windows binaries had compatibilities issues with numpy 2 that were fixed in torch 2.4.1 (see pytorch/pytorch#131668 (comment)). Maintainers will need to update the required status checks for the [`dev`](https://github.com/Project-MONAI/MONAI/tree/dev) branch to: - Remove min-dep-pytorch (2.0.1) ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [X] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. --------- Signed-off-by: James Butler <[email protected]>
1 parent a09c1f0 commit 2e391c8

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-79
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‎.github/workflows/cron.yml

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strategy:
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matrix:
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environment:
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- "PT113+CUDA118"
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- "PT210+CUDA121"
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- "PT230+CUDA121"
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- "PT240+CUDA126"
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- "PTLATEST+CUDA126"
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include:
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
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- environment: PT113+CUDA118
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pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121"
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base: "nvcr.io/nvidia/pytorch:22.10-py3" # CUDA 11.8
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- environment: PT210+CUDA121
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pytorch: "pytorch==2.1.0 torchvision==0.16.0 --extra-index-url https://download.pytorch.org/whl/cu121"
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- environment: PT230+CUDA121
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pytorch: "pytorch==2.3.0 torchvision==0.18.0 --extra-index-url https://download.pytorch.org/whl/cu121"
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base: "nvcr.io/nvidia/pytorch:23.08-py3" # CUDA 12.1
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- environment: PT240+CUDA126
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pytorch: "pytorch==2.4.0 torchvision==0.19.0 --extra-index-url https://download.pytorch.org/whl/cu121"

‎.github/workflows/pythonapp-gpu.yml

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strategy:
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matrix:
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environment:
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- "PT113+CUDA116"
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- "PT210+CUDA121DOCKER"
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- "PT230+CUDA124DOCKER"
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- "PT240+CUDA125DOCKER"
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- "PT250+CUDA126DOCKER"
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include:
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
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- environment: PT113+CUDA116
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pytorch: "torch==1.13.1 torchvision==0.14.1"
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base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
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- environment: PT210+CUDA121DOCKER
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# 23.08: 2.1.0a0+29c30b1
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- environment: PT230+CUDA124DOCKER
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# 24.04: 2.3.0a0+6ddf5cf85e
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pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error
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base: "nvcr.io/nvidia/pytorch:23.08-py3"
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- environment: PT210+CUDA121DOCKER
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# 24.08: 2.3.0a0+40ec155e58.nv24.3
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base: "nvcr.io/nvidia/pytorch:24.04-py3"
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- environment: PT240+CUDA125DOCKER
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# 24.06: 2.4.0a0+f70bd71a48
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pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error
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base: "nvcr.io/nvidia/pytorch:24.06-py3"
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- environment: PT250+CUDA126DOCKER
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# 24.08: 2.5.0a0+872d972e41
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pytorch: "-h" # we explicitly set pytorch to -h to avoid pip install error
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base: "nvcr.io/nvidia/pytorch:24.08-py3"
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container:
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apt-get update
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apt-get install -y wget
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if [ ${{ matrix.environment }} = "PT113+CUDA116" ]
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if [ ${{ matrix.environment }} = "PT230+CUDA124" ]
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then
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PYVER=3.9 PYSFX=3 DISTUTILS=python3-distutils && \
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apt-get update && apt-get install -y --no-install-recommends \
@@ -114,7 +116,7 @@ jobs:
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# build for the current self-hosted CI Tesla V100
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BUILD_MONAI=1 TORCH_CUDA_ARCH_LIST="7.0" ./runtests.sh --build --disttests
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./runtests.sh --quick --unittests
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if [ ${{ matrix.environment }} = "PT113+CUDA116" ]; then
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if [ ${{ matrix.environment }} = "PT230+CUDA124" ]; then
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# test the clang-format tool downloading once
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coverage run -m tests.clang_format_utils
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fi

‎.github/workflows/pythonapp-min.yml

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strategy:
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fail-fast: false
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matrix:
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pytorch-version: ['1.13.1', '2.0.1', '2.2.2', '2.3.1', '2.4.1', 'latest']
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pytorch-version: ['2.3.1', '2.4.1', '2.5.1', 'latest']
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timeout-minutes: 40
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steps:
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- uses: actions/checkout@v4

‎.github/workflows/pythonapp.yml

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- if: runner.os == 'windows'
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name: Install torch cpu from pytorch.org (Windows only)
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run: |
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python -m pip install torch==1.13.1+cpu torchvision==0.14.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
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python -m pip install torch==2.4.1 torchvision==0.19.1+cpu --index-url https://download.pytorch.org/whl/cpu
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- if: runner.os == 'Linux'
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name: Install itk pre-release (Linux only)
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run: |
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- name: Install the dependencies
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run: |
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python -m pip install --user --upgrade pip wheel
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python -m pip install torch==1.13.1 torchvision==0.14.1
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python -m pip install torch==2.4.1 torchvision==0.19.1
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cat "requirements-dev.txt"
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python -m pip install -r requirements-dev.txt
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python -m pip list
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# install the latest pytorch for testing
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# however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
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# fresh torch installation according to pyproject.toml
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python -m pip install torch>=1.13.1 torchvision
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python -m pip install torch>=2.3.0 torchvision
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- name: Check packages
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run: |
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pip uninstall monai

‎docs/requirements.txt

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-f https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl
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torch>=1.13.1
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-f https://download.pytorch.org/whl/cpu/torch-2.3.0%2Bcpu-cp39-cp39-linux_x86_64.whl
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torch>=2.3.0
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pytorch-ignite==0.4.11
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numpy>=1.20
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itk>=5.2

‎environment-dev.yml

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- nvidia
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- conda-forge
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dependencies:
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- numpy>=1.24,<2.0
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- pytorch>=1.13.1
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- numpy>=1.24,<3.0
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- pytorch>=2.3.0
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- torchio
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- torchvision
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- pytorch-cuda>=11.6

‎monai/engines/evaluator.py

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from monai.utils import ForwardMode, IgniteInfo, ensure_tuple, min_version, optional_import
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from monai.utils.enums import CommonKeys as Keys
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from monai.utils.enums import EngineStatsKeys as ESKeys
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from monai.utils.module import look_up_option, pytorch_after
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from monai.utils.module import look_up_option
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if TYPE_CHECKING:
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from ignite.engine import Engine, EventEnum
@@ -269,13 +269,8 @@ def __init__(
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amp_kwargs=amp_kwargs,
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)
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if compile:
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if pytorch_after(2, 1):
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compile_kwargs = {} if compile_kwargs is None else compile_kwargs
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network = torch.compile(network, **compile_kwargs) # type: ignore[assignment]
275-
else:
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warnings.warn(
277-
"Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done"
278-
)
272+
compile_kwargs = {} if compile_kwargs is None else compile_kwargs
273+
network = torch.compile(network, **compile_kwargs) # type: ignore[assignment]
279274
self.network = network
280275
self.compile = compile
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self.inferer = SimpleInferer() if inferer is None else inferer

‎monai/engines/trainer.py

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from monai.utils import AdversarialIterationEvents, AdversarialKeys, GanKeys, IgniteInfo, min_version, optional_import
2828
from monai.utils.enums import CommonKeys as Keys
2929
from monai.utils.enums import EngineStatsKeys as ESKeys
30-
from monai.utils.module import pytorch_after
3130

3231
if TYPE_CHECKING:
3332
from ignite.engine import Engine, EventEnum
@@ -183,13 +182,8 @@ def __init__(
183182
amp_kwargs=amp_kwargs,
184183
)
185184
if compile:
186-
if pytorch_after(2, 1):
187-
compile_kwargs = {} if compile_kwargs is None else compile_kwargs
188-
network = torch.compile(network, **compile_kwargs) # type: ignore[assignment]
189-
else:
190-
warnings.warn(
191-
"Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done"
192-
)
185+
compile_kwargs = {} if compile_kwargs is None else compile_kwargs
186+
network = torch.compile(network, **compile_kwargs) # type: ignore[assignment]
193187
self.network = network
194188
self.compile = compile
195189
self.optimizer = optimizer

‎monai/networks/blocks/crossattention.py

+1-6
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@@ -17,7 +17,7 @@
1717
import torch.nn as nn
1818

1919
from monai.networks.layers.utils import get_rel_pos_embedding_layer
20-
from monai.utils import optional_import, pytorch_after
20+
from monai.utils import optional_import
2121

2222
Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
2323

@@ -84,11 +84,6 @@ def __init__(
8484
if causal and sequence_length is None:
8585
raise ValueError("sequence_length is necessary for causal attention.")
8686

87-
if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
88-
raise ValueError(
89-
"use_flash_attention is only supported for PyTorch versions >= 2.0."
90-
"Upgrade your PyTorch or set the flag to False."
91-
)
9287
if use_flash_attention and save_attn:
9388
raise ValueError(
9489
"save_attn has been set to True, but use_flash_attention is also set"

‎monai/networks/blocks/selfattention.py

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1818
import torch.nn.functional as F
1919

2020
from monai.networks.layers.utils import get_rel_pos_embedding_layer
21-
from monai.utils import optional_import, pytorch_after
21+
from monai.utils import optional_import
2222

2323
Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
2424

@@ -90,11 +90,6 @@ def __init__(
9090
if causal and sequence_length is None:
9191
raise ValueError("sequence_length is necessary for causal attention.")
9292

93-
if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
94-
raise ValueError(
95-
"use_flash_attention is only supported for PyTorch versions >= 2.0."
96-
"Upgrade your PyTorch or set the flag to False."
97-
)
9893
if use_flash_attention and save_attn:
9994
raise ValueError(
10095
"save_attn has been set to True, but use_flash_attention is also set"

‎monai/networks/blocks/upsample.py

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1717
import torch.nn as nn
1818

1919
from monai.networks.layers.factories import Conv, Pad, Pool
20-
from monai.networks.utils import CastTempType, icnr_init, pixelshuffle
21-
from monai.utils import InterpolateMode, UpsampleMode, ensure_tuple_rep, look_up_option, pytorch_after
20+
from monai.networks.utils import icnr_init, pixelshuffle
21+
from monai.utils import InterpolateMode, UpsampleMode, ensure_tuple_rep, look_up_option
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2323
__all__ = ["Upsample", "UpSample", "SubpixelUpsample", "Subpixelupsample", "SubpixelUpSample"]
2424

@@ -164,15 +164,7 @@ def __init__(
164164
align_corners=align_corners,
165165
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166166

167-
# Cast to float32 as 'upsample_nearest2d_out_frame' op does not support bfloat16
168-
# https://github.com/pytorch/pytorch/issues/86679. This issue is solved in PyTorch 2.1
169-
if pytorch_after(major=2, minor=1):
170-
self.add_module("upsample_non_trainable", upsample)
171-
else:
172-
self.add_module(
173-
"upsample_non_trainable",
174-
CastTempType(initial_type=torch.bfloat16, temporary_type=torch.float32, submodule=upsample),
175-
)
167+
self.add_module("upsample_non_trainable", upsample)
176168
if post_conv:
177169
self.add_module("postconv", post_conv)
178170
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‎pyproject.toml

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requires = [
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"wheel",
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"setuptools",
5-
"torch>=1.13.1",
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"torch>=2.3.0",
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"ninja",
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"packaging"
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]

‎requirements.txt

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torch>=1.13.1,<2.6
2-
numpy>=1.24,<2.0
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torch>=2.3.0,<2.6; sys_platform != 'win32'
2+
torch>=2.4.1,<2.6; sys_platform == 'win32'
3+
numpy>=1.24,<3.0

‎setup.cfg

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ninja
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packaging
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install_requires =
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numpy>=1.24,<2.0
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torch>=2.3.0; sys_platform != 'win32'
46+
torch>=2.4.1; sys_platform == 'win32'
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numpy>=1.24,<3.0
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[options.extras_require]
4950
all =

‎tests/integration/test_integration_bundle_run.py

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7676
)
7777
with open(meta_file, "w") as f:
7878
json.dump(
79-
{"version": "0.1.0", "monai_version": "1.1.0", "pytorch_version": "1.13.1", "numpy_version": "1.22.2"},
80-
f,
79+
{"version": "0.1.0", "monai_version": "1.1.0", "pytorch_version": "2.3.0", "numpy_version": "1.22.2"}, f
8180
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8281
cmd = ["coverage", "run", "-m", "monai.bundle"]
8382
# test both CLI entry "run" and "run_workflow"
@@ -114,8 +113,7 @@ def test_scripts_fold(self):
114113
)
115114
with open(meta_file, "w") as f:
116115
json.dump(
117-
{"version": "0.1.0", "monai_version": "1.1.0", "pytorch_version": "1.13.1", "numpy_version": "1.22.2"},
118-
f,
116+
{"version": "0.1.0", "monai_version": "1.1.0", "pytorch_version": "2.3.0", "numpy_version": "1.22.2"}, f
119117
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121119
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‎tests/metrics/test_surface_dice.py

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8282
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8383
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8484
np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
85-
np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
85+
np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
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np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
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def test_tolerance_euclidean_distance(self):
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126126
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127127
for b, c in np.ndindex(batch_size, n_class):
128128
np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
129-
np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
129+
np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
130130
np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
131131

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def test_tolerance_euclidean_distance_3d(self):
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173173
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174174
for b, c in np.ndindex(batch_size, n_class):
175175
np.testing.assert_allclose(expected_res0[b, c], res0[b, c].cpu())
176-
np.testing.assert_array_equal(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
176+
np.testing.assert_allclose(agg0.cpu(), np.nanmean(np.nanmean(expected_res0, axis=1), axis=0))
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np.testing.assert_equal(not_nans.cpu(), torch.tensor(2))
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def test_tolerance_all_distances(self):

‎tests/nonconfig_workflow.py

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self._monai_version = "1.1.0"
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68+
self._pytorch_version = "2.3.0"
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7070
if self._numpy_version is None:
7171
self._numpy_version = "1.22.2"

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