[BREAKING] Add asymmetric padding support for conv and pool operations

crates/burn-import/onnx-tests/build.rs

@@ -41,6 +41,8 @@ fn main() {
         .input("tests/avg_pool1d_ceil_mode/avg_pool1d_ceil_mode.onnx")
         .input("tests/avg_pool2d/avg_pool2d.onnx")
         .input("tests/avg_pool2d_ceil_mode/avg_pool2d_ceil_mode.onnx")
+        .input("tests/avg_pool/avg_pool1d_asymmetric_padding.onnx")
+        .input("tests/avg_pool/avg_pool2d_asymmetric_padding.onnx")
         .input("tests/batch_norm/batch_norm.onnx")
         .input("tests/bitshift/bitshift_left.onnx")
         .input("tests/bitshift/bitshift_left_scalar.onnx")
@@ -101,6 +103,8 @@ fn main() {
         .input("tests/conv1d/conv1d.onnx")
         .input("tests/conv2d/conv2d.onnx")
         .input("tests/conv3d/conv3d.onnx")
+        .input("tests/conv/conv1d_asymmetric_padding.onnx")
+        .input("tests/conv/conv2d_asymmetric_padding.onnx")
         .input("tests/conv_transpose1d/conv_transpose1d.onnx")
         .input("tests/conv_transpose2d/conv_transpose2d.onnx")
         .input("tests/conv_transpose3d/conv_transpose3d.onnx")
@@ -237,6 +241,8 @@ fn main() {
         .input("tests/maxpool1d_ceil_mode/maxpool1d_ceil_mode.onnx")
         .input("tests/maxpool2d/maxpool2d.onnx")
         .input("tests/maxpool2d_ceil_mode/maxpool2d_ceil_mode.onnx")
+        .input("tests/maxpool/maxpool1d_asymmetric_padding.onnx")
+        .input("tests/maxpool/maxpool2d_asymmetric_padding.onnx")
         .input("tests/min/min.onnx")
         .input("tests/mean/mean.onnx")
         .input("tests/mul/mul.onnx")

crates/burn-import/onnx-tests/pyproject.toml

@@ -7,6 +7,7 @@ dependencies = [
     "torch>=2.3.1",
     "onnx>=1.16.1",
     "onnxruntime>=1.18.0",
+    "onnxscript>=0.1.0",
 ]
 readme = "README.md"
 requires-python = ">= 3.8"

crates/burn-import/onnx-tests/tests/avg_pool/avg_pool1d_asymmetric_padding.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+
+# used to generate model: avg_pool1d_asymmetric_padding.onnx
+
+import numpy as np
+import onnx
+from onnx import helper, TensorProto
+from onnx.reference import ReferenceEvaluator
+
+
+def main():
+    # Input: [batch=2, channels=4, width=10]
+    # Asymmetric padding: left=1, right=2
+    # kernel=3, stride=1
+    # Output width = (10 + 1 + 2 - 3) / 1 + 1 = 11
+
+    X = helper.make_tensor_value_info("x", TensorProto.FLOAT, [2, 4, 10])
+    Y = helper.make_tensor_value_info("y", TensorProto.FLOAT, [2, 4, 11])
+
+    # Create AveragePool node with asymmetric padding (left=1, right=2)
+    # ONNX pads format for 1D: [start, end] = [left, right]
+    avg_pool_node = helper.make_node(
+        "AveragePool",
+        inputs=["x"],
+        outputs=["y"],
+        kernel_shape=[3],
+        strides=[1],
+        pads=[1, 2],  # [left, right] asymmetric padding
+        count_include_pad=1,  # Include padding in average calculation
+    )
+
+    graph = helper.make_graph(
+        [avg_pool_node],
+        "avg_pool1d_asymmetric_padding",
+        [X],
+        [Y],
+    )
+
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
+    model.ir_version = 8
+
+    onnx.checker.check_model(model)
+    file_name = "avg_pool1d_asymmetric_padding.onnx"
+    onnx.save(model, file_name)
+
+    print("Finished exporting model to {}".format(file_name))
+    print("Ops in graph: {}".format([n.op_type for n in model.graph.node]))
+
+    # Verify with ReferenceEvaluator
+    test_input = np.ones((2, 4, 10), dtype=np.float32)
+    ref = ReferenceEvaluator(file_name)
+    ref_output = ref.run(None, {"x": test_input})[0]
+
+    print("Test input shape: {}".format(test_input.shape))
+    print("Test output shape: {}".format(ref_output.shape))
+    print("ReferenceEvaluator output sum: {}".format(ref_output.sum()))
+
+
+if __name__ == "__main__":
+    main()

crates/burn-import/onnx-tests/tests/avg_pool/avg_pool2d_asymmetric_padding.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+# used to generate model: avg_pool2d_asymmetric_padding.onnx
+
+import numpy as np
+import onnx
+from onnx import helper, TensorProto
+from onnx.reference import ReferenceEvaluator
+
+
+def main():
+    # Input: [batch=2, channels=4, height=10, width=15]
+    # Asymmetric padding: top=1, left=1, bottom=2, right=2
+    # kernel=[3,3], stride=[1,1]
+    # Output height = (10 + 1 + 2 - 3) / 1 + 1 = 11
+    # Output width = (15 + 1 + 2 - 3) / 1 + 1 = 16
+
+    X = helper.make_tensor_value_info("x", TensorProto.FLOAT, [2, 4, 10, 15])
+    Y = helper.make_tensor_value_info("y", TensorProto.FLOAT, [2, 4, 11, 16])
+
+    # Create AveragePool node with asymmetric padding
+    # ONNX pads format for 2D: [top, left, bottom, right]
+    avg_pool_node = helper.make_node(
+        "AveragePool",
+        inputs=["x"],
+        outputs=["y"],
+        kernel_shape=[3, 3],
+        strides=[1, 1],
+        pads=[1, 1, 2, 2],  # [top, left, bottom, right] asymmetric padding
+        count_include_pad=1,  # Include padding in average calculation
+    )
+
+    graph = helper.make_graph(
+        [avg_pool_node],
+        "avg_pool2d_asymmetric_padding",
+        [X],
+        [Y],
+    )
+
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 18)])
+    model.ir_version = 8
+
+    onnx.checker.check_model(model)
+    file_name = "avg_pool2d_asymmetric_padding.onnx"
+    onnx.save(model, file_name)
+
+    print("Finished exporting model to {}".format(file_name))
+    print("Ops in graph: {}".format([n.op_type for n in model.graph.node]))
+
+    # Verify with ReferenceEvaluator
+    test_input = np.ones((2, 4, 10, 15), dtype=np.float32)
+    ref = ReferenceEvaluator(file_name)
+    ref_output = ref.run(None, {"x": test_input})[0]
+
+    print("Test input shape: {}".format(test_input.shape))
+    print("Test output shape: {}".format(ref_output.shape))
+    print("ReferenceEvaluator output sum: {}".format(ref_output.sum()))
+
+
+if __name__ == "__main__":
+    main()

crates/burn-import/onnx-tests/tests/avg_pool/mod.rs

@@ -2,8 +2,10 @@
 use crate::include_models;
 include_models!(
     avg_pool1d,
+    avg_pool1d_asymmetric_padding,
     avg_pool1d_ceil_mode,
     avg_pool2d,
+    avg_pool2d_asymmetric_padding,
     avg_pool2d_ceil_mode
 );
 
@@ -181,4 +183,58 @@ mod tests {
             .to_data()
             .assert_approx_eq::<FT>(&expected, tolerance);
     }
+
+    #[test]
+    fn avg_pool1d_asymmetric_padding() {
+        // Test asymmetric padding (left=1, right=2) for AvgPool1d
+        let device = Default::default();
+        let model: avg_pool1d_asymmetric_padding::Model<TestBackend> =
+            avg_pool1d_asymmetric_padding::Model::new(&device);
+
+        // Run the model with ones as input for easier testing
+        let input = Tensor::<TestBackend, 3>::ones([2, 4, 10], &device);
+        let output = model.forward(input);
+
+        // With asymmetric padding (1, 2), input length 10 becomes 10+1+2=13
+        // After pool with kernel 3, stride 1, output length is 13-3+1=11
+        let expected_shape = Shape::from([2, 4, 11]);
+        assert_eq!(output.shape(), expected_shape);
+
+        // Verify the sum matches PyTorch output
+        let output_sum = output.sum().into_scalar();
+        let expected_sum = 77.333_33; // from pytorch
+        assert!(
+            (output_sum - expected_sum).abs() < 0.1,
+            "Expected sum ~{}, got {}",
+            expected_sum,
+            output_sum
+        );
+    }
+
+    #[test]
+    fn avg_pool2d_asymmetric_padding() {
+        // Test asymmetric padding (left=1, right=2, top=1, bottom=2) for AvgPool2d
+        let device = Default::default();
+        let model: avg_pool2d_asymmetric_padding::Model<TestBackend> =
+            avg_pool2d_asymmetric_padding::Model::new(&device);
+
+        // Run the model with ones as input for easier testing
+        let input = Tensor::<TestBackend, 4>::ones([2, 4, 10, 15], &device);
+        let output = model.forward(input);
+
+        // With asymmetric padding (1, 1, 2, 2), input (10, 15) becomes (13, 18)
+        // After pool with kernel (3, 3), stride (1, 1), output is (11, 16)
+        let expected_shape = Shape::from([2, 4, 11, 16]);
+        assert_eq!(output.shape(), expected_shape);
+
+        // Verify the sum matches ReferenceEvaluator output
+        let output_sum = output.sum().into_scalar();
+        let expected_sum = 1134.222; // from ReferenceEvaluator
+        assert!(
+            (output_sum - expected_sum).abs() < 1.0,
+            "Expected sum ~{}, got {}",
+            expected_sum,
+            output_sum
+        );
+    }
 }

crates/burn-import/onnx-tests/tests/conv/conv1d_asymmetric_padding.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+
+# used to generate model: conv1d_asymmetric_padding.onnx
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import onnx
+from onnx.reference import ReferenceEvaluator
+
+# must set for testing against crate
+torch.manual_seed(0)
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        # Create a Conv1d without padding - we'll apply asymmetric padding manually
+        self.conv1 = nn.Conv1d(4, 6, kernel_size=3, stride=1, padding=0)
+
+    def forward(self, x):
+        # Apply asymmetric padding: (left=1, right=2)
+        # PyTorch F.pad takes (left, right) for 1D
+        x = F.pad(x, (1, 2), mode='constant', value=0)
+        x = self.conv1(x)
+        return x
+
+
+def main():
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+
+    # Export to onnx
+    model = Model()
+    model.eval()
+    device = torch.device("cpu")
+
+    file_name = "conv1d_asymmetric_padding.onnx"
+    test_input = torch.ones(2, 4, 10, device=device)
+
+    # Export with dynamo exporter (opset 18)
+    torch.onnx.export(model, test_input, file_name, verbose=False, opset_version=18)
+
+    # Load model and convert external data to embedded
+    onnx_model = onnx.load(file_name, load_external_data=True)
+    # Save with all data embedded
+    onnx.save(onnx_model, file_name, save_as_external_data=False)
+
+    print("Finished exporting model to {}".format(file_name))
+
+    # Output some test data for use in the test
+    print("Test input data shape of ones: {}".format(test_input.shape))
+    output = model.forward(test_input)
+    print("Test output data shape: {}".format(output.shape))
+
+    # Verify with ONNX ReferenceEvaluator
+    ref = ReferenceEvaluator(file_name)
+    ref_output = ref.run(None, {"x": test_input.numpy()})[0]
+
+    output_sum = output.sum().item()
+    ref_sum = ref_output.sum()
+
+    print("PyTorch output sum: {}".format(output_sum))
+    print("ReferenceEvaluator output sum: {}".format(ref_sum))
+
+
+if __name__ == "__main__":
+    main()

crates/burn-import/onnx-tests/tests/conv/conv2d_asymmetric_padding.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+
+# used to generate model: conv2d_asymmetric_padding.onnx
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import onnx
+from onnx.reference import ReferenceEvaluator
+
+# must set for testing against crate
+torch.manual_seed(0)
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        # Create a Conv2d without padding - we'll apply asymmetric padding manually
+        self.conv1 = nn.Conv2d(4, 6, kernel_size=3, stride=1, padding=0)
+
+    def forward(self, x):
+        # Apply asymmetric padding: (left=1, right=2, top=1, bottom=3)
+        # PyTorch F.pad takes (left, right, top, bottom) for 2D
+        x = F.pad(x, (1, 2, 1, 3), mode='constant', value=0)
+        x = self.conv1(x)
+        return x
+
+
+def main():
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+
+    # Export to onnx
+    model = Model()
+    model.eval()
+    device = torch.device("cpu")
+
+    file_name = "conv2d_asymmetric_padding.onnx"
+    test_input = torch.ones(2, 4, 10, 15, device=device)
+
+    # Export with dynamo exporter (opset 18)
+    torch.onnx.export(model, test_input, file_name, verbose=False, opset_version=18)
+
+    # Load model and convert external data to embedded
+    onnx_model = onnx.load(file_name, load_external_data=True)
+    # Save with all data embedded
+    onnx.save(onnx_model, file_name, save_as_external_data=False)
+
+    print("Finished exporting model to {}".format(file_name))
+
+    # Output some test data for use in the test
+    print("Test input data shape of ones: {}".format(test_input.shape))
+    output = model.forward(test_input)
+    print("Test output data shape: {}".format(output.shape))
+
+    # Verify with ONNX ReferenceEvaluator
+    ref = ReferenceEvaluator(file_name)
+    ref_output = ref.run(None, {"x": test_input.numpy()})[0]
+
+    output_sum = output.sum().item()
+    ref_sum = ref_output.sum()
+
+    print("PyTorch output sum: {}".format(output_sum))
+    print("ReferenceEvaluator output sum: {}".format(ref_sum))
+
+
+if __name__ == "__main__":
+    main()