Merge branch 'Project-MONAI:dev' into 4980-get-wsi-at-mpp

Author: NikolasSchmitz

.github/workflows/cron.yml

@@ -13,17 +13,13 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT113+CUDA118"
-          - "PT210+CUDA121"
+          - "PT230+CUDA121"
           - "PT240+CUDA126"
           - "PTLATEST+CUDA126"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT113+CUDA118
-            pytorch: "torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu121"
-            base: "nvcr.io/nvidia/pytorch:22.10-py3"  # CUDA 11.8
-          - environment: PT210+CUDA121
-            pytorch: "pytorch==2.1.0 torchvision==0.16.0 --extra-index-url https://download.pytorch.org/whl/cu121"
+          - environment: PT230+CUDA121
+            pytorch: "pytorch==2.3.0 torchvision==0.18.0 --extra-index-url https://download.pytorch.org/whl/cu121"
             base: "nvcr.io/nvidia/pytorch:23.08-py3"  # CUDA 12.1
           - environment: PT240+CUDA126
             pytorch: "pytorch==2.4.0 torchvision==0.19.0 --extra-index-url https://download.pytorch.org/whl/cu121"

.github/workflows/pythonapp-gpu.yml

@@ -22,19 +22,21 @@ jobs:
     strategy:
       matrix:
         environment:
-          - "PT113+CUDA116"
-          - "PT210+CUDA121DOCKER"
+          - "PT230+CUDA124DOCKER"
+          - "PT240+CUDA125DOCKER"
+          - "PT250+CUDA126DOCKER"
         include:
           # https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes
-          - environment: PT113+CUDA116
-            pytorch: "torch==1.13.1 torchvision==0.14.1"
-            base: "nvcr.io/nvidia/cuda:11.6.1-devel-ubuntu18.04"
-          - environment: PT210+CUDA121DOCKER
-            # 23.08: 2.1.0a0+29c30b1
+          - environment: PT230+CUDA124DOCKER
+            # 24.04: 2.3.0a0+6ddf5cf85e
             pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
-            base: "nvcr.io/nvidia/pytorch:23.08-py3"
-          - environment: PT210+CUDA121DOCKER
-            # 24.08: 2.3.0a0+40ec155e58.nv24.3
+            base: "nvcr.io/nvidia/pytorch:24.04-py3"
+          - environment: PT240+CUDA125DOCKER
+            # 24.06: 2.4.0a0+f70bd71a48
+            pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
+            base: "nvcr.io/nvidia/pytorch:24.06-py3"
+          - environment: PT250+CUDA126DOCKER
+            # 24.08: 2.5.0a0+872d972e41
             pytorch: "-h"  # we explicitly set pytorch to -h to avoid pip install error
             base: "nvcr.io/nvidia/pytorch:24.08-py3"
     container:
@@ -49,7 +51,7 @@ jobs:
         apt-get update
         apt-get install -y wget
 
-        if [ ${{ matrix.environment }} = "PT113+CUDA116" ]
+        if [ ${{ matrix.environment }} = "PT230+CUDA124" ]
         then
         PYVER=3.9 PYSFX=3 DISTUTILS=python3-distutils && \
         apt-get update && apt-get install -y --no-install-recommends \
@@ -114,7 +116,7 @@ jobs:
         # build for the current self-hosted CI Tesla V100
         BUILD_MONAI=1 TORCH_CUDA_ARCH_LIST="7.0" ./runtests.sh --build --disttests
         ./runtests.sh --quick --unittests
-        if [ ${{ matrix.environment }} = "PT113+CUDA116" ]; then
+        if [ ${{ matrix.environment }} = "PT230+CUDA124" ]; then
           # test the clang-format tool downloading once
           coverage run -m tests.clang_format_utils
         fi

.github/workflows/pythonapp-min.yml

@@ -124,7 +124,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        pytorch-version: ['1.13.1', '2.0.1', '2.2.2', '2.3.1', '2.4.1', 'latest']
+        pytorch-version: ['2.3.1', '2.4.1', '2.5.1', 'latest']
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v4

.github/workflows/pythonapp.yml

@@ -94,7 +94,7 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==1.13.1+cpu torchvision==0.14.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==2.4.1 torchvision==0.19.1+cpu --index-url https://download.pytorch.org/whl/cpu
     - if: runner.os == 'Linux'
       name: Install itk pre-release (Linux only)
       run: |
@@ -103,7 +103,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --user --upgrade pip wheel
-        python -m pip install torch==1.13.1 torchvision==0.14.1
+        python -m pip install torch==2.4.1 torchvision==0.19.1
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list
@@ -155,7 +155,7 @@ jobs:
         # install the latest pytorch for testing
         # however, "pip install monai*.tar.gz" will build cpp/cuda with an isolated
         # fresh torch installation according to pyproject.toml
-        python -m pip install torch>=1.13.1 torchvision
+        python -m pip install torch>=2.3.0 torchvision
     - name: Check packages
       run: |
         pip uninstall monai

docs/requirements.txt

@@ -1,5 +1,5 @@
--f https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl
-torch>=1.13.1
+-f https://download.pytorch.org/whl/cpu/torch-2.3.0%2Bcpu-cp39-cp39-linux_x86_64.whl
+torch>=2.3.0
 pytorch-ignite==0.4.11
 numpy>=1.20
 itk>=5.2

environment-dev.yml

@@ -5,8 +5,8 @@ channels:
   - nvidia
   - conda-forge
 dependencies:
-  - numpy>=1.24,<2.0
-  - pytorch>=1.13.1
+  - numpy>=1.24,<3.0
+  - pytorch>=2.3.0
   - torchio
   - torchvision
   - pytorch-cuda>=11.6

monai/engines/evaluator.py

@@ -28,7 +28,7 @@
 from monai.utils import ForwardMode, IgniteInfo, ensure_tuple, min_version, optional_import
 from monai.utils.enums import CommonKeys as Keys
 from monai.utils.enums import EngineStatsKeys as ESKeys
-from monai.utils.module import look_up_option, pytorch_after
+from monai.utils.module import look_up_option
 
 if TYPE_CHECKING:
     from ignite.engine import Engine, EventEnum
@@ -269,13 +269,8 @@ def __init__(
             amp_kwargs=amp_kwargs,
         )
         if compile:
-            if pytorch_after(2, 1):
-                compile_kwargs = {} if compile_kwargs is None else compile_kwargs
-                network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
-            else:
-                warnings.warn(
-                    "Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done"
-                )
+            compile_kwargs = {} if compile_kwargs is None else compile_kwargs
+            network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
         self.network = network
         self.compile = compile
         self.inferer = SimpleInferer() if inferer is None else inferer

monai/engines/trainer.py

@@ -27,7 +27,6 @@
 from monai.utils import AdversarialIterationEvents, AdversarialKeys, GanKeys, IgniteInfo, min_version, optional_import
 from monai.utils.enums import CommonKeys as Keys
 from monai.utils.enums import EngineStatsKeys as ESKeys
-from monai.utils.module import pytorch_after
 
 if TYPE_CHECKING:
     from ignite.engine import Engine, EventEnum
@@ -183,13 +182,8 @@ def __init__(
             amp_kwargs=amp_kwargs,
         )
         if compile:
-            if pytorch_after(2, 1):
-                compile_kwargs = {} if compile_kwargs is None else compile_kwargs
-                network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
-            else:
-                warnings.warn(
-                    "Network compilation (compile=True) not supported for Pytorch versions before 2.1, no compilation done"
-                )
+            compile_kwargs = {} if compile_kwargs is None else compile_kwargs
+            network = torch.compile(network, **compile_kwargs)  # type: ignore[assignment]
         self.network = network
         self.compile = compile
         self.optimizer = optimizer

monai/networks/blocks/crossattention.py

@@ -17,7 +17,7 @@
 import torch.nn as nn
 
 from monai.networks.layers.utils import get_rel_pos_embedding_layer
-from monai.utils import optional_import, pytorch_after
+from monai.utils import optional_import
 
 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
 
@@ -84,11 +84,6 @@ def __init__(
         if causal and sequence_length is None:
             raise ValueError("sequence_length is necessary for causal attention.")
 
-        if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
-            raise ValueError(
-                "use_flash_attention is only supported for PyTorch versions >= 2.0."
-                "Upgrade your PyTorch or set the flag to False."
-            )
         if use_flash_attention and save_attn:
             raise ValueError(
                 "save_attn has been set to True, but use_flash_attention is also set"

monai/networks/blocks/selfattention.py

@@ -18,7 +18,7 @@
 import torch.nn.functional as F
 
 from monai.networks.layers.utils import get_rel_pos_embedding_layer
-from monai.utils import optional_import, pytorch_after
+from monai.utils import optional_import
 
 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
 
@@ -90,11 +90,6 @@ def __init__(
         if causal and sequence_length is None:
             raise ValueError("sequence_length is necessary for causal attention.")
 
-        if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
-            raise ValueError(
-                "use_flash_attention is only supported for PyTorch versions >= 2.0."
-                "Upgrade your PyTorch or set the flag to False."
-            )
         if use_flash_attention and save_attn:
             raise ValueError(
                 "save_attn has been set to True, but use_flash_attention is also set"