zhuzilin commented on a change in pull request #8056:
URL: https://github.com/apache/tvm/pull/8056#discussion_r634912195
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File path: python/tvm/relay/op/nn/nn.py
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@@ -2973,6 +2973,40 @@ def cross_entropy_with_logits(predictions, targets):
return _make.cross_entropy_with_logits(predictions, targets)
+def nll_loss(predictions, targets, weights, reduction="mean",
ignore_index=-100):
+ """Negative log likelihood loss.
+
+ output{n, i_1, i_2, ..., i_k} = predictions{n, t, i_1, i_2, i_k}
+ where t = target{n, i_1, i_2, ..., i_k}
+
+ result = reduction(output)
+
+ Parameters
+ ----------
+ predictions : tvm.relay.Expr
+ The predictions.
+
+ targets : tvm.relay.Expr
+ The target value of each prediction.
+
+ weights : tvm.relay.Expr
Review comment:
@altanh We can make weights an optional parameter. I wonder if there are
any example of a relay op with an optional tensor parameter that I can learn
from. And also, how should we deal with gradient of an optional parameter? BTW,
is there any better way we can mark a parameter as "no need for gradient"
instead of returning an `one_like` grad?
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