mbrookhart commented on a change in pull request #8305:
URL: https://github.com/apache/tvm/pull/8305#discussion_r660676638
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -4640,6 +4641,63 @@ def repeat(N, D):
)
+def verify_qlinearadd(a_shape, b_shape, c_shape):
+
+ a_array = np.random.random(a_shape).astype("float32")
+ b_array = np.random.random(b_shape).astype("float32")
+
+ input_nodes = [
+ helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
+ helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
+ ]
+ input_names = [
+ "a",
+ "b",
+ ]
+ input_values = [a_array, b_array]
+
+ node = helper.make_node("QLinearAdd", inputs=input_names, outputs=["C"])
+
+ node = helper.make_node("Add", ["a", "b"], ["C"])
+ graph = helper.make_graph(
+ [node],
+ "qlinearadd_test",
+ inputs=input_nodes,
+ outputs=[helper.make_tensor_value_info("C", TensorProto.FLOAT,
list(c_shape))],
+ )
+ model = helper.make_model(graph, producer_name="qlinearconv_test")
+ from onnxruntime.quantization import quantize_static,
CalibrationDataReader, QuantType
+
+ class RandomDataReader(CalibrationDataReader):
+ def __init__(self, n=10):
+ self.data = iter(
+ [
+ {
+ "a": np.random.random(a_shape).astype("float32"),
+ "b": np.random.random(b_shape).astype("float32"),
+ }
+ for _ in range(n)
+ ]
+ )
+
+ def get_next(self):
+ return next(self.data, None)
+
+ model_fp32 = "/tmp/model.onnx"
Review comment:
I couldn't figure out a way to create the op in memory. :( ONNX's python
helper throws a fit since it isn't a standard op, and onnx runtime's
quantization API demands files on disk. I wrote it to /tmp so it wouldn't stick
around.
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