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[TOPI] Support non-batch cases for topi.nll_loss #14060

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Merged
merged 1 commit into from
Feb 21, 2023
Merged

[TOPI] Support non-batch cases for topi.nll_loss #14060

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28 changes: 28 additions & 0 deletions include/tvm/topi/nn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -660,6 +660,32 @@ inline tvm::te::Tensor batch_to_space_nd(const tvm::te::Tensor& data,
inline Tensor nll_loss(const Tensor& predictions, const Tensor& targets, const Tensor& weights,
std::string reduction = "mean", int ignore_index = -100,
const std::string name = "nll_loss", const std::string tag = kBroadcast) {
if (predictions.ndim() == 1) {
// corner case: no batch in shape
// prediction->shape = (C,), targets->shape = (), weights->shape = (C,)
auto T = tvm::te::compute(
{},
[&](const tvm::Array<tvm::tir::Var>& target_indices) {
auto c = targets();
return tvm::tir::Select(c != ignore_index, -predictions(c) * weights(c),
tvm::tir::make_const(predictions->dtype, 0));
},
name, tag);
if (reduction == "mean") {
auto W = tvm::te::compute(
{},
[&](const tvm::Array<tvm::tir::Var>& target_indices) {
auto c = targets();
return tvm::tir::Select(c != ignore_index, weights(c),
tvm::tir::make_const(predictions->dtype, 0));
},
name, tag);
return topi::divide(T, W);
} else {
return T;
}
}

auto T = tvm::te::compute(
targets->shape,
[&](const tvm::Array<tvm::tir::Var>& target_indices) {
Expand All @@ -674,6 +700,7 @@ inline Tensor nll_loss(const Tensor& predictions, const Tensor& targets, const T
tvm::tir::make_const(predictions->dtype, 0));
},
name, tag);
ICHECK(T->shape.size() != 0);
if (reduction == "mean") {
auto W = tvm::te::compute(
targets->shape,
Expand All @@ -690,6 +717,7 @@ inline Tensor nll_loss(const Tensor& predictions, const Tensor& targets, const T
return T;
}
}

} // namespace topi
} // namespace tvm
#endif // TVM_TOPI_NN_H_
11 changes: 9 additions & 2 deletions tests/python/topi/python/test_topi_loss.py
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Expand Up @@ -32,12 +32,19 @@
((10, 5), "none", -100, "float32"),
((10, 5), "mean", 3, "float32"),
((10, 5), "mean", -100, "float64"),
((5,), "mean", -100, "float32"),
((5,), "mean", 3, "float32"),
((5,), "none", -100, "float32"),
)


def test_nll_loss(target, dev, prediction_shape, reduction, ignore_index, dtype):
C = prediction_shape[1]
target_shape = prediction_shape[:1] + prediction_shape[2:]
if len(prediction_shape) == 1:
C = prediction_shape[0]
target_shape = []
else:
C = prediction_shape[1]
target_shape = prediction_shape[:1] + prediction_shape[2:]
predictions = te.placeholder(shape=prediction_shape, name="predictions", dtype=dtype)
targets = te.placeholder(shape=target_shape, name="targets", dtype="int32")
weights = te.placeholder(shape=(C,), name="weights", dtype=dtype)
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