Overhaul pt2ts

.github/workflows/static_analysis.yml

@@ -20,7 +20,7 @@ on:
       - '**.py'
       - '**.sh'
       - '**CMakeLists.txt'
-      - 'requirements-dev.txt'
+      - 'pyproject.toml'
 
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
@@ -66,7 +66,7 @@ jobs:
           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-dev.txt
+          pip install -e .[dev]
 
       # Run CMake build to get compile commands for clang
       - name: FTorch CMake

.github/workflows/test_suite_ubuntu_cpu_gnu.yml

@@ -124,6 +124,11 @@ jobs:
           . ftorch/bin/activate
           pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
 
+      - name: Install ftorch_utils
+        run: |
+          . ftorch/bin/activate
+          python -m pip install .
+
       - name: Install OpenMPI
         run: |
           sudo apt update
@@ -148,12 +153,12 @@ jobs:
           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)/build
+          mkdir -p ${BUILD_DIR}
           # Install to /tmp/ to ensure independent from the CMake build in standalone check.
           export INSTALL_DIR=/tmp/ftorch-install
           mkdir -p ${INSTALL_DIR}
           # NOTE: The pFUnit version (pinned during installation above) is used in the install path.
           export PFUNIT_DIR=$(pwd)/pFUnit/build/installed/PFUNIT-4.12
-          mkdir ${BUILD_DIR}
           cd ${BUILD_DIR}
 
           cmake .. \

CHANGELOG.md

@@ -11,12 +11,19 @@ For specific details see the [FTorch online documentation](https://cambridge-icc
 
 ### Added
 
+- Introduced a central, installable `pt2ts` script
+  [#555](https://github.com/Cambridge-ICCS/FTorch/pull/555)
+
 ### Changed
 
-### Removed
+- Renamed `utils` as `ftorch_utils` and made it a proper Python module
+  [#555](https://github.com/Cambridge-ICCS/FTorch/pull/555)
 
 ### Removed
 
+- Removed `pt2ts.py` scripts from `utils` and all examples
+  [#555](https://github.com/Cambridge-ICCS/FTorch/pull/555)
+
 ### Fixed
 
 ### Patch Releases

examples/2_SimpleNet/CMakeLists.txt

@@ -37,17 +37,25 @@ target_link_libraries(simplenet_infer_fortran PRIVATE FTorch::ftorch)
 if(CMAKE_BUILD_TESTS)
   include(CTest)
 
-  # 1. Check the PyTorch model runs and its outputs meet expectations
-  add_test(NAME example_simplenet_simplenet
-           COMMAND ${Python_EXECUTABLE} ${PROJECT_SOURCE_DIR}/simplenet.py)
+  # 1. Write the PyTorch model out and check it runs and its outputs meet
+  #    expectations
+  add_test(
+    NAME example_simplenet_simplenet
+    COMMAND ${Python_EXECUTABLE} ${PROJECT_SOURCE_DIR}/simplenet.py
+    WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
 
-  # 2. Check the model is saved to file in the expected location with the
-  #   pt2ts.py script
+  # 2. Run the pt2ts script to convert the model file to TorchScript format
+  #    and run basic checks
   add_test(
     NAME example_simplenet_pt2ts
-    COMMAND ${Python_EXECUTABLE} ${PROJECT_SOURCE_DIR}/pt2ts.py --filepath
-            ${PROJECT_BINARY_DIR}
-    WORKING_DIRECTORY ${PROJECT_BINARY_DIR})
+    COMMAND pt2ts ${PROJECT_SOURCE_DIR}/simplenet.py
+                  SimpleNet
+                  ${PROJECT_SOURCE_DIR}/saved_simplenet_model_cpu.pt
+                  --input_tensor_file
+                  ${PROJECT_SOURCE_DIR}/saved_simplenet_input_tensor_cpu.pt
+                  --output_model_file
+                  ${PROJECT_BINARY_DIR}/saved_simplenet_model_cpu.pt
+                  --test)
 
   # 3. Check the model can be loaded from file and run in Python and that its
   #   outputs meet expectations

examples/2_SimpleNet/README.md

@@ -9,10 +9,12 @@ covered in later examples.
 
 ## Description
 
-A Python file `simplenet.py` is provided that defines a very simple PyTorch 'net' that takes an input
-vector of length 5 and applies a single `Linear` layer to multiply it by 2.
+A Python file `simplenet.py` is provided that defines a very simple PyTorch
+'net' that takes an input vector of length 5 and applies a single `Linear` layer
+to multiply it by 2. Running this file as a script will write the model out in
+PyTorch's `.pt` file format.
 
-A modified version of the `pt2ts.py` tool saves this simple net to TorchScript.
+The `pt2ts` tool is used to convert the model file to the TorchScript format.
 
 A series of files `simplenet_infer_<LANG>` then bind from other languages to run the
 TorchScript model in inference mode.
@@ -45,13 +47,15 @@ This defines the net and runs it with an input tensor [0.0, 1.0, 2.0, 3.0, 4.0]
 ```
 Model output: tensor([[0., 2., 4., 6., 8.]])
 ```
+You should find that a PyTorch model file `saved_simplenet_model_cpu.pt` is
+created.
 
-To save the SimpleNet model to TorchScript run the modified version of the
-`pt2ts.py` tool:
+To convert the SimpleNet model to TorchScript run the `pt2ts` script:
 ```
-python3 pt2ts.py
+pt2ts simplenet.py SimpleNet saved_simplenet_model_cpu.pt
 ```
-which will generate `saved_simplenet_model_cpu.pt` - the TorchScript instance of the net.
+Without any further arguments, this will overwrite the
+`saved_simplenet_model_cpu.pt` model file so you will receive a warning.
 
 You can check that everything is working by running the `simplenet_infer_python.py` script:
 ```

examples/2_SimpleNet/simplenet.py

@@ -42,15 +42,39 @@ def forward(self, batch: torch.Tensor) -> torch.Tensor:
 
 
 if __name__ == "__main__":
-    model = SimpleNet()
+    import argparse
+
+    # Parse user input
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--device_type",
+        help="Device type to run the inference on",
+        type=str,
+        choices=["cpu", "cuda", "hip", "xpu", "mps"],
+        default="cpu",
+    )
+    parsed_args = parser.parse_args()
+    device_type = parsed_args.device_type
+
+    # Construct an instance of the SimpleNet model on the specified device
+    model = SimpleNet().to(device_type)
     model.eval()
 
-    input_tensor = torch.Tensor([0.0, 1.0, 2.0, 3.0, 4.0])
-    with torch.inference_mode():
-        output_tensor = model(input_tensor)
+    # Save the model in PyTorch format
+    torch.save(model.state_dict(), f"saved_simplenet_model_{device_type}.pt")
 
+    # Create an arbitrary input tensor and save it in PyTorch format
+    input_tensor = torch.Tensor([0.0, 1.0, 2.0, 3.0, 4.0]).to(device_type)
+    torch.save(input_tensor, f"saved_simplenet_input_tensor_{device_type}.pt")
+
+    # Propagate the input tensor through the model
+    with torch.inference_mode():
+        output_tensor = model(input_tensor).to("cpu")
     print(f"Model output: {output_tensor}")
 
+    # Perform a basic check of the model output
     if not torch.allclose(output_tensor, 2 * input_tensor):
         result_error = (
             f"result:\n{output_tensor}\ndoes not match expected value:\n"