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[0-size Tensor No.106、115] Add 0-size Tensor support for slogdet/det #73125

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Merged
luotao1 merged 3 commits into from
Jun 11, 2025
Merged

[0-size Tensor No.106、115] Add 0-size Tensor support for slogdet/det #73125

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8657c73
Fix
co63oc Jun 6, 2025
0437ba9
Merge branch 'develop' into b68
co63oc Jun 10, 2025
ffa70e8
Fix
co63oc Jun 10, 2025
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4 changes: 4 additions & 0 deletions paddle/phi/kernels/gpu/determinant_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -218,6 +218,10 @@ template <typename T, typename Context>
void DeterminantKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
auto input_dim = common::vectorize(x.dims());
auto input_dim_size = input_dim.size();

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19 changes: 19 additions & 0 deletions paddle/phi/kernels/gpu/slogdeterminant_kernel.cu
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Expand Up @@ -215,6 +215,25 @@ void SlogDeterminantKernel(const Context& dev_ctx,
auto input_dim = common::vectorize(x.dims());
auto input_dim_size = input_dim.size();

// shape [*, M, M], check whether it contains 0 in '*'.
if (input_dim.size() > 2) {
bool size_0 = false;
std::vector<int> tmp_dim_vec(input_dim.begin(), input_dim.end() - 2);
for (size_t i = 0; i < tmp_dim_vec.size(); ++i) {
if (tmp_dim_vec[i] == 0) {
size_0 = true;
break;
}
}
if (size_0) {
tmp_dim_vec.insert(tmp_dim_vec.begin(),
2); // make the output dims as same as numpy
out->Resize(common::make_ddim(tmp_dim_vec));
dev_ctx.template Alloc<T>(out);
return;
}
}

@DanielSun11 DanielSun11 Jun 10, 2025

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不建议在kernel中重新进行shape推导和Resize操作。如果可以请修改到infermeta中以及同步修改到符号推导中。

@co63oc co63oc Jun 10, 2025

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原kernel是在结尾做的Resize，如果修改到infermeta，需要kernel都修改，改动较多
image


auto batch_count = detail::GetBatchCount(x.dims());
VLOG(2) << "input dim:" << x.dims();
PADDLE_ENFORCE_GE(
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4 changes: 4 additions & 0 deletions paddle/phi/kernels/impl/determinant_grad_kernel_impl.h
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Expand Up @@ -82,6 +82,10 @@ void DeterminantGradKernel(const Context& dev_ctx,
const DenseTensor& out,
const DenseTensor& out_grad,
DenseTensor* x_grad) {
if (x_grad && x_grad->numel() == 0) {
dev_ctx.template Alloc<T>(x_grad);
return;
}
auto input_dims_size = x.dims().size();
if (input_dims_size > 2) {
PADDLE_ENFORCE_EQ(
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4 changes: 4 additions & 0 deletions paddle/phi/kernels/impl/determinant_kernel_impl.h
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Expand Up @@ -136,6 +136,10 @@ template <typename T, typename Context>
void DeterminantKernel(const Context& dev_ctx,
const DenseTensor& x,
DenseTensor* out) {
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
auto input_dim = common::vectorize(x.dims());
auto input_dim_size = input_dim.size();

Expand Down
4 changes: 4 additions & 0 deletions paddle/phi/kernels/impl/slogdeterminant_grad_kernel_impl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,10 @@ void SlogDeterminantGradKernel(const Context& dev_ctx,
const DenseTensor& out,
const DenseTensor& out_grad,
DenseTensor* x_grad) {
if (x_grad && x_grad->numel() == 0) {
dev_ctx.template Alloc<T>(x_grad);
return;
}
PADDLE_ENFORCE_EQ(
out_grad.dims()[0],
2,
Expand Down
19 changes: 19 additions & 0 deletions paddle/phi/kernels/impl/slogdeterminant_kernel_impl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -127,6 +127,25 @@ void SlogDeterminantKernel(const Context& dev_ctx,
auto input_dim = common::vectorize(x.dims());
auto input_dim_size = input_dim.size();

// shape [*, M, M], check whether it contains 0 in '*'.
if (input_dim.size() > 2) {
bool size_0 = false;
std::vector<int> tmp_dim_vec(input_dim.begin(), input_dim.end() - 2);
for (size_t i = 0; i < tmp_dim_vec.size(); ++i) {
if (tmp_dim_vec[i] == 0) {
size_0 = true;
break;
}
}
if (size_0) {
tmp_dim_vec.insert(tmp_dim_vec.begin(),
2); // make the output dims as same as numpy
out->Resize(common::make_ddim(tmp_dim_vec));
dev_ctx.template Alloc<T>(out);
return;
}
}

@DanielSun11 DanielSun11 Jun 10, 2025

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和paddle/phi/kernels/gpu/slogdeterminant_kernel.cu中的修改同理。

@co63oc co63oc Jun 10, 2025

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原kernel是在结尾做的Resize，如果修改到infermeta，需要kernel都修改，改动较多

auto batch_count = detail::GetBatchCount(x.dims());
VLOG(2) << "input dim:" << x.dims();
PADDLE_ENFORCE_GE(
Expand Down
32 changes: 32 additions & 0 deletions test/legacy_test/test_determinant_op.py
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Expand Up @@ -136,6 +136,22 @@ def init_data(self):
self.target = np.linalg.det(self.case)


class TestDeterminantOp_ZeroSize(TestDeterminantOp):
def init_data(self):
np.random.seed(0)
self.case = np.random.rand(0, 10, 10)
self.inputs = {'Input': self.case}
self.target = np.linalg.det(self.case)


class TestDeterminantOp_ZeroSize2(TestDeterminantOp):
def init_data(self):
np.random.seed(0)
self.case = np.random.rand(0, 0, 0)
self.inputs = {'Input': self.case}
self.target = np.linalg.det(self.case)


class TestDeterminantAPI(unittest.TestCase):
def setUp(self):
np.random.seed(0)
Expand Down Expand Up @@ -324,6 +340,22 @@ def init_data(self):
self.target = np.array(np.linalg.slogdet(self.case))


class TestSlogDeterminantOp_ZeroSize(TestSlogDeterminantOp):
def init_data(self):
np.random.seed(0)
self.case = np.random.rand(0, 5, 5).astype('float64')
self.inputs = {'Input': self.case}
self.target = np.array(np.linalg.slogdet(self.case))


class TestSlogDeterminantOp_ZeroSize2(TestSlogDeterminantOp):
def init_data(self):
np.random.seed(0)
self.case = np.random.rand(0, 0, 0).astype('float64')
self.inputs = {'Input': self.case}
self.target = np.array(np.linalg.slogdet(self.case))


class TestSlogDeterminantAPI(unittest.TestCase):
def setUp(self):
np.random.seed(0)
Expand Down