[ONNX] Add Squeeze/Unsqueeze dynamic dimensions support when opset >= 13 (#71158)

* Add Squeeze/Unsqueeze dynamic axes support when opset >= 13

Co-authored-by: hwangdeyu <dejack953outlook.com>
Co-authored-by: Gary Miguel <garymmgarymm.org>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73104

test/onnx/test_pytorch_onnx_onnxruntime.py

@@ -1525,6 +1525,16 @@ class TestONNXRuntime(unittest.TestCase):
         x = torch.randn(2, 1, 4)
         self.run_test(Squeeze(), x)
 
+    @skipIfUnsupportedMinOpsetVersion(13)
+    def test_squeeze_dynamic_dim(self):
+        class Squeeze(torch.nn.Module):
+            def forward(self, x, dim: int):
+                return torch.squeeze(x, dim)
+
+        x = torch.randn(2, 1, 4)
+        dim = 1
+        self.run_test(Squeeze(), (x, dim))
+
     def test_unsqueeze(self):
         class Unsqueeze(torch.nn.Module):
             def forward(self, x):
@@ -1533,6 +1543,16 @@ class TestONNXRuntime(unittest.TestCase):
         x = torch.randn(2, 3, 4)
         self.run_test(Unsqueeze(), x)
 
+    @skipIfUnsupportedMinOpsetVersion(13)
+    def test_unsqueeze_dynamic_dim(self):
+        class Unsqueeze(torch.nn.Module):
+            def forward(self, x, dim: int):
+                return torch.unsqueeze(x, dim)
+
+        x = torch.randn(2, 1, 4)
+        dim = -1
+        self.run_test(Unsqueeze(), (x, dim))
+
     def test_maxpool_default_stride(self):
         class MaxPoolModel(torch.nn.Module):
             def forward(self, x):

torch/onnx/symbolic_helper.py

@@ -476,19 +476,38 @@ def _interpolate_warning(interpolate_mode):
                   "to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).\n"
                   "We recommend using opset 11 and above for models using this operator.")
 
+
 def _unsqueeze_helper(g, input, axes_i):
+    if _is_constant(axes_i[0]):
         if _export_onnx_opset_version >= 13:
             axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
             return g.op("Unsqueeze", input, axes)
-    else:
         return g.op("Unsqueeze", input, axes_i=axes_i)
+    # Tensor type
+    if _export_onnx_opset_version < 13:
+        raise ValueError(f"Opset version must be >= 13 for Unsqueeze with dynamic axes. {input.node().sourceRange()}")
+    return g.op("Unsqueeze", input, axes_i[0])
+
 
 def _squeeze_helper(g, input, axes_i):
+    if _is_constant(axes_i[0]):
         if _export_onnx_opset_version >= 13:
             axes = g.op("Constant", value_t=torch.tensor(axes_i, dtype=torch.long))
             return g.op("Squeeze", input, axes)
-    else:
         return g.op("Squeeze", input, axes_i=axes_i)
+    # Tensor type
+    if _export_onnx_opset_version < 13:
+        raise ValueError(f"Opset version must be >= 13 for Squeeze with dynamic axes. {input.node().sourceRange()}")
+    axes_t = axes_i[0]
+    axes_rank = _get_tensor_rank(axes_t)
+    if axes_rank > 1:
+        raise ValueError("For Squeeze axses as input, the axes rank must be one in ONNX spec.")
+    elif axes_rank == 0:
+        # The axes is a scalar. Unsqueeze it to a rank 1 tensor.
+        axes_t = _unsqueeze_helper(g, axes_t, [0])
+        return g.op("Squeeze", input, axes_t)
+    return g.op("Squeeze", input, axes_t)
+
 
 def _reducesum_helper(g, input, axes_i=None, keepdims_i=1, noop_with_empty_axes_i=0):
     keepdims_i = _maybe_get_const(keepdims_i, "i")
@@ -501,6 +520,7 @@ def _reducesum_helper(g, input, axes_i=None, keepdims_i=1, noop_with_empty_axes_
     else:
         return g.op("ReduceSum", input, axes_i=axes_i, keepdims_i=keepdims_i)
 
+
 def _interpolate_size_to_scales(g, input, output_size, dim):
     output_size = _maybe_get_const(output_size, "is")
     if _is_value(output_size):

torch/onnx/symbolic_opset11.py

@@ -569,6 +569,10 @@ def squeeze(g, self, dim=None):
     if dim is None:
         return g.op("Squeeze", self)
 
+    # dim as a tensor
+    if not sym_help._is_constant(dim):
+        return sym_help._squeeze_helper(g, self, [dim])
+
     dim = sym_help._get_const(dim, "i", "dim")
 
     input_rank = sym_help._get_tensor_rank(self)
@@ -606,8 +610,10 @@ def squeeze(g, self, dim=None):
     return sym_help._squeeze_helper(g, self, [dim])
 
 
-@parse_args("v", "i")
 def unsqueeze(g, self, dim):
+    if sym_help._is_constant(dim):
+        dim = sym_help._get_const(dim, "i", "dim")
+
     return sym_help._unsqueeze_helper(g, self, [dim])
 
 def mm(g, self, other):