sdpython · sdpython · Feb 22, 2024 · Feb 2, 2024 · Feb 2, 2024 · Feb 2, 2024
diff --git a/_unittests/ut_light_api/test_light_api.py b/_unittests/ut_light_api/test_light_api.py
@@ -2,7 +2,7 @@
 import unittest
 from typing import Callable, Optional
 import numpy as np
-from onnx import GraphProto, ModelProto
+from onnx import GraphProto, ModelProto, TensorProto
 from onnx.defs import (
     get_all_schemas_with_history,
     onnx_opset_version,
@@ -526,6 +526,18 @@ def test_input_shape(self):
         i = str(model.graph.input[0]).replace("\n", "").replace(" ", "")
         self.assertNotIn("shape{}", i)
 
+    def test_constant_of_shape(self):
+        onx = (
+            start()
+            .vin("X", TensorProto.INT64, shape=[None, None])
+            .ConstantOfShape()
+            .vout(shape=[])
+            .to_onnx()
+        )
+        ref = ReferenceEvaluator(onx)
+        got = ref.run(None, {"X": np.array([2, 3], dtype=np.int64)})[0]
+        self.assertEqualArray(np.zeros((2, 3), dtype=np.float32), got)
+
 
 if __name__ == "__main__":
     TestLightApi().test_add()

diff --git a/onnx_array_api/light_api/__init__.py b/onnx_array_api/light_api/__init__.py
@@ -8,12 +8,14 @@
 def start(
     opset: Optional[int] = None,
     opsets: Optional[Dict[str, int]] = None,
+    ir_version: Optional[int] = None,
 ) -> OnnxGraph:
     """
     Starts an onnx model.
 
     :param opset: main opset version
     :param opsets: others opsets as a dictionary
+    :param ir_version: specify the ir_version as well
     :return: an instance of :class:`onnx_array_api.light_api.OnnxGraph`
 
     A very simple model:
@@ -45,7 +47,7 @@ def start(
         )
         print(onx)
     """
-    return OnnxGraph(opset=opset, opsets=opsets)
+    return OnnxGraph(opset=opset, opsets=opsets, ir_version=ir_version)
 
 
 def g() -> OnnxGraph:

diff --git a/onnx_array_api/light_api/_op_var.py b/onnx_array_api/light_api/_op_var.py
@@ -1,4 +1,6 @@
 from typing import List, Optional, Union
+import numpy as np
+from ..reference import from_array_extended
 from ..annotations import AI_ONNX_ML, domain
 
 
@@ -69,6 +71,11 @@ def Cast(self, saturate: int = 1, to: int = 0) -> "Var":
     def Celu(self, alpha: float = 1.0) -> "Var":
         return self.make_node("Celu", self, alpha=alpha)
 
+    def ConstantOfShape(self, value: Optional[np.array] = None) -> "Var":
+        if value is None:
+            return self.make_node("ConstantOfShape", self)
+        return self.make_node("ConstantOfShape", self, value=from_array_extended(value))
+
     def DepthToSpace(self, blocksize: int = 0, mode: str = "DCR") -> "Var":
         return self.make_node("DepthToSpace", self, blocksize=blocksize, mode=mode)
 

diff --git a/onnx_array_api/light_api/model.py b/onnx_array_api/light_api/model.py
@@ -42,13 +42,15 @@ class OnnxGraph:
 
     :param opset: main opset version
     :param opsets: other opsets as a dictionary
+    :param ir_version: to specify an ir_version
     :param is_function: a :class:`onnx.ModelProto` or a :class:`onnx.FunctionProto`
     """
 
     def __init__(
         self,
         opset: Optional[int] = None,
         opsets: Optional[Dict[str, int]] = None,
+        ir_version: Optional[int] = None,
         proto_type: ProtoType = ProtoType.MODEL,
     ):
         if opsets is not None and "" in opsets:
@@ -65,6 +67,7 @@ def __init__(
         self.proto_type = proto_type
         self.opsets = opsets
         self.opset = opset
+        self.ir_version = ir_version
         self.nodes: List[Union[NodeProto, TensorProto]] = []
         self.inputs: List[ValueInfoProto] = []
         self.outputs: List[ValueInfoProto] = []
@@ -402,6 +405,8 @@ def to_onnx(self) -> GRAPH_PROTO:
             # If no opsets, it a subgraph, not a model.
             return graph
         model = make_model(graph, opset_imports=opsets)
+        if self.ir_version:
+            model.ir_version = ir_version
         if not is_windows() or not is_azure():
             # check_model fails sometimes on Windows
             check_model(model)