diff --git a/CHANGELOGS.rst b/CHANGELOGS.rst
index d0b6445..e139c0a 100644
--- 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
index 6a44d64..9ae6fec 100644
--- 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
index e6ab25f..89b5a84 100644
--- 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
new file mode 100644
index 0000000..ac365e7
--- /dev/null
+++ 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
new file mode 100644
index 0000000..e30c5ec
--- /dev/null
+++ 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
new file mode 100644
index 0000000..c4b0efa
--- /dev/null
+++ 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,)
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