sdpython · sdpython · Feb 15, 2024 · Feb 2, 2024 · Feb 2, 2024 · Feb 2, 2024
diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
@@ -4,6 +4,7 @@ Change Logs
 0.2.0
 +++++
 
+* :pr:`75`: add QuickGelu to ExtendedReferenceEvaluator
 * :pr:`71`: adds tools to compare two onnx graphs
 * :pr:`61`: adds function to plot onnx model as graphs
 * :pr:`60`: supports translation of local functions

diff --git a/_unittests/ut_reference/test_reference_ops.py b/_unittests/ut_reference/test_reference_ops.py
@@ -59,6 +59,88 @@ def test_fused_matmul11(self):
         got = ref.run(None, {"X": a, "Y": a})
         self.assertEqualArray(a.T @ a.T, got[0])
 
+    def test_memcpy(self):
+        model = make_model(
+            make_graph(
+                [
+                    make_node("MemcpyToHost", ["X"], ["Z"]),
+                    make_node("MemcpyFromHost", ["X"], ["Z"]),
+                ],
+                "name",
+                [make_tensor_value_info("X", TensorProto.FLOAT, None)],
+                [make_tensor_value_info("Z", TensorProto.FLOAT, None)],
+            ),
+            opset_imports=[make_opsetid("", 18), make_opsetid("com.microsoft", 1)],
+            ir_version=9,
+        )
+        a = np.arange(4).reshape(-1, 2).astype(np.float32)
+        ref = ExtendedReferenceEvaluator(model)
+        got = ref.run(None, {"X": a})
+        self.assertEqualArray(a, got[0])
+
+    def test_quick_gelu(self):
+        from onnxruntime import InferenceSession
+
+        for alpha in [0.0, 2.0]:
+            model = make_model(
+                make_graph(
+                    [
+                        make_node(
+                            "QuickGelu",
+                            ["X"],
+                            ["Z"],
+                            domain="com.microsoft",
+                            alpha=alpha,
+                        )
+                    ],
+                    "name",
+                    [make_tensor_value_info("X", TensorProto.FLOAT, None)],
+                    [make_tensor_value_info("Z", TensorProto.FLOAT, None)],
+                ),
+                opset_imports=[make_opsetid("", 18), make_opsetid("com.microsoft", 1)],
+                ir_version=9,
+            )
+            sess = InferenceSession(
+                model.SerializeToString(), providers=["CPUExecutionProvider"]
+            )
+            a = np.arange(4).reshape(-1, 2).astype(np.float32)
+            expected = sess.run(None, {"X": a})
+            ref = ExtendedReferenceEvaluator(model)
+            got = ref.run(None, {"X": a})
+            self.assertEqualArray(expected[0], got[0])
+
+    def test_scatter_elements(self):
+        model = make_model(
+            make_graph(
+                [
+                    make_node(
+                        "ScatterElements",
+                        ["data", "indices", "updates"],
+                        ["Z"],
+                        axis=3,
+                        reduction="add",
+                    )
+                ],
+                "name",
+                [
+                    make_tensor_value_info("data", TensorProto.FLOAT, None),
+                    make_tensor_value_info("indices", TensorProto.INT64, None),
+                    make_tensor_value_info("updates", TensorProto.FLOAT, None),
+                ],
+                [make_tensor_value_info("Z", TensorProto.FLOAT, None)],
+            ),
+            opset_imports=[make_opsetid("", 18)],
+        )
+        data = np.zeros(2**4, dtype=np.float32).reshape((2, 2, 2, 2))
+        indices = np.array([[[[0]]]], dtype=np.int64)
+        updates = np.array([[[[1]]]], dtype=np.float32)
+        y = np.array(
+            [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.float32
+        ).reshape((2, 2, 2, 2))
+        ref = ExtendedReferenceEvaluator(model)
+        got = ref.run(None, {"data": data, "indices": indices, "updates": updates})
+        self.assertEqualArray(y, got[0])
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)
diff --git a/onnx_array_api/reference/evaluator.py b/onnx_array_api/reference/evaluator.py
@@ -8,6 +8,9 @@
 from .ops.op_concat import Concat
 from .ops.op_constant_of_shape import ConstantOfShape
 from .ops.op_fused_matmul import FusedMatMul
+from .ops.op_memcpy_host import MemcpyFromHost, MemcpyToHost
+from .ops.op_quick_gelu import QuickGelu
+from .ops.op_scatter_elements import ScatterElements
 
 
 logger = getLogger("onnx-array-api-eval")
@@ -34,6 +37,10 @@ class ExtendedReferenceEvaluator(ReferenceEvaluator):
         CastLike_19,
         ConstantOfShape,
         FusedMatMul,
+        MemcpyFromHost,
+        MemcpyToHost,
+        QuickGelu,
+        ScatterElements,
     ]
 
     @staticmethod

diff --git a/onnx_array_api/reference/ops/op_memcpy_host.py b/onnx_array_api/reference/ops/op_memcpy_host.py
@@ -0,0 +1,11 @@
+from onnx.reference.op_run import OpRun
+
+
+class MemcpyFromHost(OpRun):
+    def _run(self, x):
+        return (x,)
+
+
+class MemcpyToHost(OpRun):
+    def _run(self, x):
+        return (x,)
diff --git a/onnx_array_api/reference/ops/op_quick_gelu.py b/onnx_array_api/reference/ops/op_quick_gelu.py
@@ -0,0 +1,23 @@
+import numpy as np
+from onnx.reference.op_run import OpRun
+
+
+def sigmoid(x):  # type: ignore
+    if x > 0:
+        return 1 / (1 + np.exp(-x))
+    return np.exp(x) / (1 + np.exp(x))
+
+
+class QuickGelu(OpRun):
+    op_domain = "com.microsoft"
+
+    def __init__(self, onnx_node, run_params):  # type: ignore
+        OpRun.__init__(self, onnx_node, run_params)
+        self.vf = np.vectorize(sigmoid)
+
+    def _run(self, X, alpha=1.0):
+        if len(X.shape) == 0:
+            return ((X * sigmoid(X * alpha)).astype(X.dtype),)
+        if X.size == 0:
+            return (X,)
+        return ((X * self.vf(X * alpha)).astype(X.dtype),)
diff --git a/onnx_array_api/reference/ops/op_scatter_elements.py b/onnx_array_api/reference/ops/op_scatter_elements.py
@@ -0,0 +1,98 @@
+import numpy as np
+
+from onnx.reference.op_run import OpRun
+
+
+def scatter_elements(data, indices, updates, axis=0, reduction=None):  # type: ignore
+    if reduction == "add":
+
+        def f(x, y):
+            return x + y
+
+    elif reduction == "min":
+
+        def f(x, y):
+            return min(x, y)
+
+    elif reduction == "max":
+
+        def f(x, y):
+            return max(x, y)
+
+    else:
+
+        def f(x, y):
+            return y
+
+    if axis < 0:
+        axis = data.ndim + axis
+
+    if len(data.shape) == 1 and axis == 0:
+        scattered = np.copy(data)
+        for pos, up in zip(indices, updates):
+            scattered[pos] = f(scattered[pos], up)
+        return scattered
+
+    if len(indices.shape) == 2:
+        scattered = np.copy(data)
+        if axis == 0:
+            for i in range(indices.shape[0]):
+                for j in range(indices.shape[1]):
+                    scattered[indices[i, j], j] = f(
+                        scattered[indices[i, j], j], updates[i, j]
+                    )
+        else:
+            for i in range(indices.shape[0]):
+                for j in range(indices.shape[1]):
+                    scattered[i, indices[i, j]] = f(
+                        scattered[i, indices[i, j]], updates[i, j]
+                    )
+        return scattered
+
+    if len(indices.shape) == 3:
+        scattered = np.copy(data)
+        if axis == 0:
+            for i in range(indices.shape[0]):
+                for j in range(indices.shape[1]):
+                    for k in range(indices.shape[2]):
+                        scattered[indices[i, j, k], j, k] = f(
+                            scattered[indices[i, j, k], j, k], updates[i, j, k]
+                        )
+        elif axis == 1:
+            for i in range(indices.shape[0]):
+                for j in range(indices.shape[1]):
+                    for k in range(indices.shape[2]):
+                        scattered[i, indices[i, j, k], k] = f(
+                            scattered[i, indices[i, j, k], k], updates[i, j, k]
+                        )
+        elif axis == 2:
+            for i in range(indices.shape[0]):
+                for j in range(indices.shape[1]):
+                    for k in range(indices.shape[2]):
+                        scattered[i, j, indices[i, j, k]] = f(
+                            scattered[i, j, indices[i, j, k]], updates[i, j, k]
+                        )
+        return scattered
+
+    if len(indices.shape) == 4:
+        scattered = np.copy(data)
+        if axis == 3:
+            for a in range(indices.shape[0]):
+                for i in range(indices.shape[1]):
+                    for j in range(indices.shape[2]):
+                        for k in range(indices.shape[3]):
+                            scattered[a, i, j, indices[a, i, j, k]] = f(
+                                scattered[a, i, j, indices[a, i, j, k]],
+                                updates[a, i, j, k],
+                            )
+            return scattered
+
+    raise RuntimeError(
+        f"Not implemented for indices.shape={indices.shape} and axis={axis}"
+    )
+
+
+class ScatterElements(OpRun):
+    def _run(self, data, indices, updates, axis=None, reduction=None):  # type: ignore
+        res = scatter_elements(data, indices, updates, axis=axis, reduction=reduction)
+        return (res,)