AndrewZhaoLuo commented on a change in pull request #9063:
URL: https://github.com/apache/tvm/pull/9063#discussion_r714277773
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File path: python/tvm/relay/frontend/onnx.py
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@@ -3430,6 +3430,25 @@ def _impl_v10(cls, inputs, attr, params):
return _qnn.op.quantize(out, y_scale, y_zero_point, out_dtype=dtype)
+class QLinearLeakyRelu(OnnxOpConverter):
+ """Operator converter for QLinearLeakyRelu from Microsoft onnxruntime
contrib opset."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+
+ a_scale = get_scalar(inputs[1], params)
+ a_zero_point = get_scalar(inputs[2], params, "int32")
+ y_scale = fold_constant(get_scalar(inputs[3], params))
+ y_zero_point = get_scalar(inputs[4], params, "int32")
+ alpha = float(attr.get("alpha", 1.0))
+
+ dtype = infer_type(inputs[0]).checked_type.dtype
+
+ a = _qnn.op.dequantize(inputs[0], a_scale, a_zero_point)
+ out = _op.nn.leaky_relu(a, alpha)
+ return _qnn.op.quantize(out, y_scale, y_zero_point, out_dtype=dtype)
Review comment:
If alpha is a power of two e.g. 1 / 16 or 1 / 8, then we can implement
this via bit shift operations assuming zero can be represented in the
quantization scheme. Not making a suggestion or anything, just thought it was
an interesting point.
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