jwfromm commented on a change in pull request #7519:
URL: https://github.com/apache/tvm/pull/7519#discussion_r583264216
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File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -1867,6 +1867,23 @@ def verify_unary_ops(op, x, rtol=1e-5, atol=1e-5):
verify_unary_ops("Sigmoid", x)
verify_unary_ops("Softsign", x)
+ def verify_unary_ops_fp16(op, x, rtol=1e-5, atol=1e-5):
+ z = helper.make_node(op, ["in1"], ["out"])
+ graph = helper.make_graph(
+ [z],
+ "_test",
+ inputs=[
+ helper.make_tensor_value_info("in1", TensorProto.FLOAT16,
list(in_shape)),
+ ],
+ outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT16,
list(out_shape))],
+ )
+ model = helper.make_model(graph, producer_name="_test")
+ verify_with_ort_with_inputs(model, [x], rtol=rtol, atol=atol)
+
+ dtype = "float16"
+ x = np.random.uniform(size=in_shape).astype(dtype)
+ verify_unary_ops_fp16("Reciprocal", x)
+
Review comment:
I think you could pretty cleanly fuse this into `verify_unary_ops` by
adding an optional `dtype` argument. You can use
`mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]` to convert that dtype string
into the proper ONNX proto type.
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