jiangjiajun commented on code in PR #14160:
URL: https://github.com/apache/tvm/pull/14160#discussion_r1125835333
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python/tvm/relay/frontend/paddlepaddle.py:
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@@ -1950,6 +1958,30 @@ def convert_softsign(g, op, block):
g.add_node(op.output("Out")[0], out)
+def convert_softshrink(g, op, block):
+ """Operator converter for softshrink."""
+
+ threshold = op.attr("lambda")
+ x = g.get_node(op.input("X")[0])
+ dtype = infer_type(x).checked_type.dtype
+ threshold_right = _expr.const(threshold, dtype)
+ threshold_left = _expr.const(-1.0 * threshold, dtype)
+ middle = _expr.const(0.0, dtype)
+ condition_0 = tvm.relay.logical_and(
+ tvm.relay.less_equal(x, threshold_right), tvm.relay.greater_equal(x,
threshold_left)
+ )
+ calc_middle = tvm.relay.where(condition_0, middle, x)
+
+ sub_threshold = _op.subtract(x, threshold_right)
+ condition_1 = tvm.relay.greater(calc_middle, threshold_right)
+ calc_right = tvm.relay.where(condition_1, sub_threshold, calc_middle)
+
+ add_threshold = _op.add(x, threshold_right)
+ condition_2 = tvm.relay.less(calc_right, threshold_left)
+ calc_left = tvm.relay.where(condition_2, add_threshold, calc_right)
+ g.add_node(op.output("Out")[0], calc_left)
Review Comment:
Can you refer the following code to optimize this implementation
https://github.com/apache/tvm/blob/befdc4e63100bdf25b10ae7a54b40408e2e1805a/python/tvm/relay/frontend/onnx.py#L2280-L2291
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