Remove more training API stuff

- Removed testing data that was used only with
   onnxruntime_training_test.go, which was already removed.

 - Renamed legacy_types.go to legacy_code.go, since it's now a parking
   ground for legacy code that's not just "types".

 - Fixed some references to the training API in CONTRIBUTING.md.

CONTRIBUTING.md

@@ -41,8 +41,7 @@ Tests
 -----
 
  - All new features and bugfixes must include a basic unit test (in
-   `onnxruntime_test.go` or `onnxruntime_training_test.go`) to serve as a
-   sanity check.
+   `onnxruntime_test.go`) to serve as a sanity check.
 
  - If a test is for a platform-dependent or execution-provider-dependent
    feature, the test must be skipped if run on an unsupported system.
@@ -62,8 +61,8 @@ Adding New Files
 
  - Apart from testing data, try not to add new source files.
 
- - Do not add third-party code or headers.  The only exceptions for now are
-   `onnxruntime_c_api.h` and `onnxruntime_training_c_api.h`.
+ - Do not add third-party code or headers.  The only exception for now is
+   `onnxruntime_c_api.h`.
 
  - No C++ at all. Developing Go-to-C wrappers is annoying enough as it is.
 

legacy_code.go

@@ -1,8 +1,7 @@
 package onnxruntime_go
 
-// This file contains Session types that we maintain for compatibility
-// purposes; the main onnxruntime_go.go file is dedicated to AdvancedSession
-// now.
+// This file contains code and types that we maintain for compatibility
+// purposes, but is not expected to be regularly maintained or udpated.
 
 import (
 	"fmt"

onnxruntime_go.go

@@ -882,8 +882,7 @@ func (t *CustomDataTensor) GetData() []byte {
 
 // Scalar is like a tensor but the underlying go slice is of length 1 and it
 // has no dimension. It was introduced for use with the training API, but
-// remains supported since it conceivable will have use outside of the training
-// API.
+// remains supported since it may be useful apart from the training API.
 type Scalar[T TensorData] struct {
 	data     []T
 	dataSize uintptr

test_data/generate_training_assets.py

@@ -1,55 +0,0 @@
-import torch
-from torch.nn.functional import relu
-from pathlib import Path
-import onnx
-import onnxruntime.training.artifacts as artifacts
-
-class SumAndDiffModel(torch.nn.Module):
-    """ Just a standard, fairly minimal, pytorch model for generating the NN.
-    """
-    def __init__(self):
-        super().__init__()
-        # We'll do four 1x4 convolutions to make the network more interesting.
-        self.conv = torch.nn.Conv1d(1, 4, 4)
-        # We'll follow the conv with a FC layer to produce the outputs. The
-        # input to the FC layer are the 4 conv outputs concatenated with the
-        # original input.
-        self.fc = torch.nn.Linear(8, 2)
-
-    def forward(self, data):
-        batch_size = data.shape[0]
-        conv_out = relu(self.conv(data))
-        conv_flattened = torch.flatten(conv_out, start_dim=1)
-        data_flattened = torch.flatten(data, start_dim=1)
-        combined = torch.cat((conv_flattened, data_flattened), dim=1)
-        output = relu(self.fc(combined))
-        output = output.view(batch_size, 1, 2)
-        return output
-    
-def main():
-    model = SumAndDiffModel()
-
-    # Export the model to ONNX.
-    training_artifacts_path = Path(".", "training_test")
-    training_artifacts_path.mkdir(exist_ok=True, parents=True)
-    model_name = "training_network"
-    torch.onnx.export(model, torch.zeros(10, 1, 4),
-                Path(".", "training_test", f"{model_name}.onnx").__str__(),
-                input_names=["input"], output_names=["output"])
-
-    # Load the onnx model and generate artifacts
-    onnx_model = onnx.load(Path("training_test", "training_network.onnx"))
-    requires_grad = ["conv.weight", "conv.bias", "fc.weight", "fc.bias"]
-    model.train()
-
-    # Generate the training artifacts.
-    artifacts.generate_artifacts(
-    onnx_model,
-    requires_grad=requires_grad,
-    frozen_params=[],
-    loss=artifacts.LossType.L1Loss,
-    optimizer=artifacts.OptimType.AdamW,
-    artifact_directory=Path(".", "training_test"))
-
-if __name__ == "__main__":
-    main()