Add static analysis using fortitude, clang, and ruff

.github/workflows/linting.yml

@@ -0,0 +1,97 @@
+# workflow to run static-analysis and linting checks on source
+
+name: StaticAnalysis
+
+# Controls when the workflow will run
+on:
+  # Triggers the workflow on pushes to the "main" branch and any pull request events
+  push:
+    branches: [ "main"]
+  pull_request:
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Workflow run - one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "static-analysis"
+  static-analysis:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.x'
+
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m venv ftorch_venv
+          . ftorch_venv/bin/activate
+          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+          pip install -r requirements.txt
+
+      # Run CMake build to get compile commands for clang
+      - name: FTorch CMake
+        run: |
+          . ftorch_venv/bin/activate
+          export FT_DIR=$(pwd)
+          VN=$(python -c "import sys; print('.'.join(sys.version.split('.')[:2]))")
+          export Torch_DIR=${VIRTUAL_ENV}/lib/python${VN}/site-packages
+          export BUILD_DIR=$(pwd)/src/build
+          mkdir ${BUILD_DIR}
+          cd ${BUILD_DIR}
+          cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=${BUILD_DIR} -DCMAKE_Fortran_FLAGS="-std=f2008" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
+
+      # Apply Fortran linter, fortitude
+      # Configurable using the fortitude.toml file if present
+      - name: fortitude source
+        if: always()
+        run: |
+          cd ${{ github.workspace }}
+          . ftorch_venv/bin/activate  # Uses .clang-tidy config file if present
+          fortitude check src/
+
+      # Apply C++ and C linter and formatter, clang
+      # Configurable using the .clang-format and .clang-tidy config files if present
+      - name: clang source
+        if: always()
+        uses: cpp-linter/cpp-linter-action@v2
+        id: linter
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          style: 'file'
+          tidy-checks: ''
+          # Use the compile_commands.json from CMake to locate headers
+          database: ${{ github.workspace }}/src/build
+          # only 'update' a single comment in a pull request thread.
+          thread-comments: ${{ github.event_name == 'pull_request' && 'update' }}
+      - name: Fail fast?!
+        if: steps.linter.outputs.checks-failed > 0
+        run: exit 1
+
+      # Apply Fortran linter, fortitude to examples
+      - name: fortitude examples
+        if: always()
+        run: |
+          cd ${{ github.workspace }}
+          . ftorch_venv/bin/activate
+          fortitude check examples
+
+      - name: ruff
+        if: always()
+        run: |
+          cd ${{ github.workspace }}
+          . ftorch_venv/bin/activate
+          ruff format --diff ./
+          ruff check --diff ./

.gitignore

@@ -157,6 +157,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+ftorch_venv/
 
 # Spyder project settings
 .spyderproject

examples/1_SimpleNet/pt2ts.py

@@ -4,12 +4,12 @@
 import os
 import sys
 from typing import Optional
-import torch
 
 # FPTLIB-TODO
 # Add a module import with your model here:
 # This example assumes the model architecture is in an adjacent module `my_ml_model.py`
 import simplenet
+import torch
 
 
 def script_to_torchscript(
@@ -120,10 +120,10 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
     # Set the name of the file you want to save the torchscript model to:
     saved_ts_filename = "saved_simplenet_model_cpu.pt"
     # A filepath may also be provided. To do this, pass the filepath as an argument to
-    # this script when it is run from the command line, i.e., `./pt2ts.py path/to/model`.
+    # this script when it is run from the command line, i.e. `./pt2ts.py path/to/model`.
 
     # FPTLIB-TODO
-    # Save the PyTorch model using either scripting (recommended where possible) or tracing
+    # Save the PyTorch model using either scripting (recommended if possible) or tracing
     # -----------
     # Scripting
     # -----------
@@ -132,7 +132,9 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
     # -----------
     # Tracing
     # -----------
-    # trace_to_torchscript(trained_model, trained_model_dummy_input, filename=saved_ts_filename)
+    # trace_to_torchscript(
+    #     trained_model, trained_model_dummy_input, filename=saved_ts_filename
+    # )
 
     print(f"Saved model to TorchScript in '{saved_ts_filename}'.")
 
@@ -161,11 +163,17 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
             print("Saved TorchScript model working as expected in a basic test.")
             print("Users should perform further validation as appropriate.")
         else:
-            raise RuntimeError(
+            model_error = (
                 "Saved Torchscript model is not performing as expected.\n"
                 "Consider using scripting if you used tracing, or investigate further."
             )
+            raise RuntimeError(model_error)
 
     # Check that the model file is created
     filepath = os.path.dirname(__file__) if len(sys.argv) == 1 else sys.argv[1]
-    assert os.path.exists(os.path.join(filepath, saved_ts_filename))
+    if not os.path.exists(os.path.join(filepath, saved_ts_filename)):
+        torchscript_file_error = (
+            f"Saved TorchScript file {os.path.join(filepath, saved_ts_filename)} "
+            "cannot be found."
+        )
+        raise FileNotFoundError(torchscript_file_error)

examples/1_SimpleNet/simplenet.py

@@ -51,4 +51,9 @@ def forward(self, batch: torch.Tensor) -> torch.Tensor:
         output_tensor = model(input_tensor)
 
     print(output_tensor)
-    assert torch.allclose(output_tensor, 2 * input_tensor)
+    if not torch.allclose(output_tensor, 2 * input_tensor):
+        result_error = (
+            f"result:\n{output_tensor}\ndoes not match expected value:\n"
+            f"{2 * input_tensor}"
+        )
+        raise ValueError(result_error)

examples/1_SimpleNet/simplenet_infer_fortran.f90

@@ -4,13 +4,14 @@ program inference
    use, intrinsic :: iso_fortran_env, only : sp => real32
 
    ! Import our library for interfacing with PyTorch
-   use ftorch
+   use ftorch, only : torch_model, torch_tensor, torch_kCPU, torch_delete, &
+                      torch_tensor_from_array, torch_model_load, torch_model_forward
 
    ! Import our tools module for testing utils
    use ftorch_test_utils, only : assert_allclose
 
    implicit none
- 
+
    ! Set working precision for reals
    integer, parameter :: wp = sp
 
@@ -21,7 +22,7 @@ program inference
    real(wp), dimension(5), target :: in_data
    real(wp), dimension(5), target :: out_data
    real(wp), dimension(5), target :: expected
-   integer :: tensor_layout(1) = [1]
+   integer, parameter :: tensor_layout(1) = [1]
 
    ! Set up Torch data structures
    ! The net, a vector of input tensors (in this case we only have one), and the output tensor
@@ -40,7 +41,7 @@ program inference
    end do
 
    ! Initialise data
-   in_data = [0.0, 1.0, 2.0, 3.0, 4.0]
+   in_data = [0.0_wp, 1.0_wp, 2.0_wp, 3.0_wp, 4.0_wp]
 
    ! Create Torch input/output tensors from the above arrays
    call torch_tensor_from_array(in_tensors(1), in_data, tensor_layout, torch_kCPU)
@@ -54,7 +55,7 @@ program inference
    write (*,*) out_data(:)
 
    ! Check output tensor matches expected value
-   expected = [0.0, 2.0, 4.0, 6.0, 8.0]
+   expected = [0.0_wp, 2.0_wp, 4.0_wp, 6.0_wp, 8.0_wp]
    test_pass = assert_allclose(out_data, expected, test_name="SimpleNet", rtol=1e-5)
 
    ! Cleanup

examples/1_SimpleNet/simplenet_infer_python.py

@@ -3,6 +3,7 @@
 
 import os
 import sys
+
 import torch
 
 
@@ -42,7 +43,8 @@ def deploy(saved_model: str, device: str, batch_size: int = 1) -> torch.Tensor:
         output = output_gpu.to(torch.device("cpu"))
 
     else:
-        raise ValueError(f"Device '{device}' not recognised.")
+        device_error = f"Device '{device}' not recognised."
+        raise ValueError(device_error)
 
     return output
 
@@ -60,4 +62,9 @@ def deploy(saved_model: str, device: str, batch_size: int = 1) -> torch.Tensor:
 
     print(result)
 
-    assert torch.allclose(result, torch.Tensor([0.0, 2.0, 4.0, 6.0, 8.0]))
+    if not torch.allclose(result, torch.Tensor([0.0, 2.0, 4.0, 6.0, 8.0])):
+        result_error = (
+            f"result:\n{result}\ndoes not match expected value:\n"
+            f"{torch.Tensor([0.0, 2.0, 4.0, 6.0, 8.0])}"
+        )
+        raise ValueError(result_error)

examples/2_ResNet18/pt2ts.py

@@ -4,12 +4,12 @@
 import os
 import sys
 from typing import Optional
-import torch
 
 # FPTLIB-TODO
 # Add a module import with your model here:
 # This example assumes the model architecture is in an adjacent module `my_ml_model.py`
 import resnet18
+import torch
 
 
 def script_to_torchscript(
@@ -126,10 +126,10 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
     # Set the name of the file you want to save the torchscript model to:
     saved_ts_filename = "saved_resnet18_model_cpu.pt"
     # A filepath may also be provided. To do this, pass the filepath as an argument to
-    # this script when it is run from the command line, i.e., `./pt2ts.py path/to/model`.
+    # this script when it is run from the command line, i.e. `./pt2ts.py path/to/model`.
 
     # FPTLIB-TODO
-    # Save the PyTorch model using either scripting (recommended where possible) or tracing
+    # Save the PyTorch model using either scripting (recommended if possible) or tracing
     # -----------
     # Scripting
     # -----------
@@ -138,7 +138,9 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
     # -----------
     # Tracing
     # -----------
-    # trace_to_torchscript(trained_model, trained_model_dummy_input, filename=saved_ts_filename)
+    # trace_to_torchscript(
+    #     trained_model, trained_model_dummy_input, filename=saved_ts_filename
+    # )
 
     print(f"Saved model to TorchScript in '{saved_ts_filename}'.")
 
@@ -167,11 +169,17 @@ def load_torchscript(filename: Optional[str] = "saved_model.pt") -> torch.nn.Mod
             print("Saved TorchScript model working as expected in a basic test.")
             print("Users should perform further validation as appropriate.")
         else:
-            raise RuntimeError(
+            model_error = (
                 "Saved Torchscript model is not performing as expected.\n"
                 "Consider using scripting if you used tracing, or investigate further."
             )
+            raise RuntimeError(model_error)
 
     # Check that the model file is created
     filepath = os.path.dirname(__file__) if len(sys.argv) == 1 else sys.argv[1]
-    assert os.path.exists(os.path.join(filepath, saved_ts_filename))
+    if not os.path.exists(os.path.join(filepath, saved_ts_filename)):
+        torchscript_file_error = (
+            f"Saved TorchScript file {os.path.join(filepath, saved_ts_filename)} "
+            "cannot be found."
+        )
+        raise FileNotFoundError(torchscript_file_error)