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[Phi]Fix multi_margin_loss for big tensor #74134

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Merged
merged 2 commits into from
Jul 22, 2025
Merged

[Phi]Fix multi_margin_loss for big tensor #74134

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9bc31ed
fix multi_margin_loss for big tensor
ggggxm Jul 21, 2025
2731726
refine
ggggxm Jul 21, 2025
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10 changes: 5 additions & 5 deletions paddle/phi/infermeta/binary.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2067,7 +2067,7 @@ void GatherInferMeta(const MetaTensor& x,
} else {
if (axis.FromTensor() || axis_v == 0) {
// decrease the output dimension
std::vector<int> out_dim_vec;
std::vector<int64_t> out_dim_vec;
for (int i = 1; i < input_dim.size(); ++i) {
out_dim_vec.emplace_back(input_dim[i]);
}
Expand All @@ -2076,7 +2076,7 @@ void GatherInferMeta(const MetaTensor& x,
out->set_dtype(x.dtype());
out->share_lod(x);
} else {
std::vector<int> out_dim_vec;
std::vector<int64_t> out_dim_vec;
for (int i = 0; i < axis_v; i++) {
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
Expand All @@ -2092,7 +2092,7 @@ void GatherInferMeta(const MetaTensor& x,
} else {
if (axis.FromTensor() || axis_v == 0) {
// if axis.FromTensor(), we can not obtain correct shape of output
int batch_size = static_cast<int>(index_dims[0]);
int64_t batch_size = static_cast<int64_t>(index_dims[0]);
if (index_dims.size() == 2 && index_dims[1] == 0) {
batch_size = 0;
}
Expand All @@ -2102,11 +2102,11 @@ void GatherInferMeta(const MetaTensor& x,
out->set_dtype(x.dtype());
out->share_lod(x);
} else {
int index_size = static_cast<int>(index_dims[0]);
int64_t index_size = static_cast<int64_t>(index_dims[0]);
if (index_dims.size() == 2 && index_dims[1] == 0) {
index_size = 0;
}
std::vector<int> out_dim_vec;
std::vector<int64_t> out_dim_vec;
for (int i = 0; i < axis_v; i++) {
out_dim_vec.push_back(input_dim[i]); // NOLINT
}
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19 changes: 11 additions & 8 deletions python/paddle/nn/functional/loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4167,14 +4167,17 @@ def multi_margin_loss(
f"but received weight's shape[0]: {weight.shape[0]} and input's shape[1]: {input.shape[1]}"
)
weight = paddle.gather(weight, label, axis=0).reshape((-1, 1))
loss = paddle.mean(
weight
* paddle.pow(
paddle.clip((margin - index_sample + input), min=0.0),
p,
),
axis=1,
) - weight * (margin**p / paddle.shape(input)[1])
loss = (
paddle.mean(
weight
* paddle.pow(
paddle.clip((margin - index_sample + input), min=0.0),
p,
),
axis=1,
)
- (weight * (margin**p / paddle.shape(input)[1])).squeeze()
)
else:
loss = (
paddle.mean(
Expand Down