[ONNX] Add col2im for opset 18 (#84594)

Opset 18 will be used to introduce suport for ONNX's Col2Im-18 and resolve https://github.com/pytorch/pytorch/issues/84408

Depends: https://github.com/pytorch/pytorch/pull/83201 (CI will fail until ONNX submodule is updated)

as per Faith recommendation, this PR should be merged post ORT 1.13 only
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84594
Approved by: https://github.com/justinchuby, https://github.com/titaiwangms, https://github.com/abock, https://github.com/BowenBao

test/onnx/test_pytorch_onnx_no_runtime.py

@@ -1156,6 +1156,39 @@ class TestONNXExport(pytorch_test_common.ExportTestCase):
                     dim,
                 )
 
+    def test_col2im(self):
+        # This test can be moved to test/onnx/test_pytorch_onnx_onnxruntime.py when ORT implement ::Col2Im
+
+        # Random batched RGB 32x32 image-shaped input tensor of batch size 64
+        original_image_inputs = torch.randn((64, 3, 32, 32))
+        output_size = tuple(original_image_inputs.shape[2:])
+        kernel_size = (1, 2)
+        dilation = 3
+        padding = 2
+        stride = 1
+        model_im2col = torch.nn.Unfold(
+            kernel_size, dilation=dilation, padding=padding, stride=stride
+        )
+        blocks = model_im2col(original_image_inputs)
+
+        model = torch.nn.Fold(
+            output_size=output_size,
+            kernel_size=kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride,
+        )
+        f = io.BytesIO()
+        torch.onnx.export(model, (blocks,), f, opset_version=18)
+
+        onnx_model = onnx.load(io.BytesIO(f.getvalue()))
+        self.assertEqual(onnx_model.graph.node[-1].op_type, "Col2Im")
+        self.assertEqual(onnx_model.graph.node[-1].domain, "")
+        self.assertEqual(len(onnx_model.graph.node[-1].input), 3)
+        self.assertEqual(onnx_model.graph.node[-1].attribute[0].name, "dilations")
+        self.assertEqual(onnx_model.graph.node[-1].attribute[1].name, "pads")
+        self.assertEqual(onnx_model.graph.node[-1].attribute[2].name, "strides")
+
 
 class TestQuantizeEagerONNXExport(common_utils.TestCase):
     def _test_lower_graph_impl(self, model, data):

test/onnx/test_pytorch_onnx_onnxruntime.py

@@ -44,7 +44,9 @@ from torch.testing._internal.common_utils import skipIfNoLapack
 # The min onnx opset version to test for
 MIN_ONNX_OPSET_VERSION = 9
 # The max onnx opset version to test for
-MAX_ONNX_OPSET_VERSION = _constants.ONNX_MAX_OPSET
+MAX_ONNX_OPSET_VERSION = (
+    _constants.ONNX_MAX_OPSET - 1
+)  # TODO: ORT does not support opset 18 yet
 
 
 def _init_test_generalized_rcnn_transform():

torch/csrc/jit/serialization/export.cpp

@@ -59,7 +59,7 @@ namespace onnx_torch = ::torch::onnx;
 namespace onnx = ::ONNX_NAMESPACE;
 
 const static int kInvalidOpsetVersion = -1;
-const static int kMainOpsetVersion = 17;
+const static int kMainOpsetVersion = 18;
 // Based on OP_SET_ID_VERSION_MAP in
 // https://github.com/onnx/onnx/blob/master/onnx/helper.py.
 constexpr static std::array<int64_t, kMainOpsetVersion + 1>
@@ -82,6 +82,7 @@ constexpr static std::array<int64_t, kMainOpsetVersion + 1>
         8, // opset 15
         8, // opset 16
         8, // opset 17
+        8, // opset 18
 };
 
 std::string getNodeStackTraceString(const Node* n) {

torch/onnx/__init__.py

@@ -25,6 +25,7 @@ from . import (  # usort:skip. Keep the order instead of sorting lexicographical
     symbolic_opset15,
     symbolic_opset16,
     symbolic_opset17,
+    symbolic_opset18,
     utils,
 )
 
@@ -62,6 +63,7 @@ __all__ = [
     "symbolic_opset15",
     "symbolic_opset16",
     "symbolic_opset17",
+    "symbolic_opset18",
     # Enums
     "ExportTypes",
     "OperatorExportTypes",

torch/onnx/_constants.py

@@ -4,7 +4,7 @@ ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"
 
 ONNX_BASE_OPSET = 9
 ONNX_MIN_OPSET = 7
-ONNX_MAX_OPSET = 17
+ONNX_MAX_OPSET = 18
 # ONNX_DEFAULT_OPSET generated by tools/onnx/update_default_opset_version.py
 ONNX_DEFAULT_OPSET = 14
 ONNX_CONSTANT_FOLDING_MIN_OPSET = 9

torch/onnx/symbolic_opset18.py

@@ -0,0 +1,70 @@
+"""This file exports ONNX ops for opset 18.
+
+Note [ONNX Operators that are added/updated in opset 18]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-18-of-the-default-onnx-operator-set
+New operators:
+    CenterCropPad
+    Col2Im
+    Mish
+    OptionalGetElement
+    OptionalHasElement
+    Pad
+    Resize
+    ScatterElements
+    ScatterND
+"""
+
+import functools
+from typing import Sequence
+
+from torch import _C
+from torch.onnx import symbolic_helper
+from torch.onnx._internal import _beartype, registration
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in symbolic_helper.py
+
+__all__ = ["col2im"]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=18)
+
+
+@_onnx_symbolic("aten::col2im")
+@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is")
+@_beartype.beartype
+def col2im(
+    g,
+    input: _C.Value,
+    output_size: _C.Value,
+    kernel_size: _C.Value,
+    dilation: Sequence[int],
+    padding: Sequence[int],
+    stride: Sequence[int],
+):
+    # convert [i0, i1, ..., in] into [i0, i0, i1, i1, ..., in, in]
+    adjusted_padding = []
+    for pad in padding:
+        for _ in range(2):
+            adjusted_padding.append(pad)
+
+    num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
+    if not adjusted_padding:
+        adjusted_padding = [0, 0] * num_dimensional_axis
+
+    if not dilation:
+        dilation = [1] * num_dimensional_axis
+
+    if not stride:
+        stride = [1] * num_dimensional_axis
+
+    return g.op(
+        "Col2Im",
+        input,
+        output_size,
+        kernel_size,
+        dilations_i=dilation,
+        pads_i=adjusted_padding,
+        strides_i=stride,
+    )