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[0-size Tensor Job2 No.72-74] Add 0-size Tensor support for paddle.scatter [fluid_ops] #73933

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Merged
luotao1 merged 1 commit into from
Jul 18, 2025
Merged

[0-size Tensor Job2 No.72-74] Add 0-size Tensor support for paddle.scatter [fluid_ops] #73933

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14 changes: 8 additions & 6 deletions paddle/phi/infermeta/ternary.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2332,12 +2332,14 @@ void ScatterInferMeta(const MetaTensor& x,
const auto& index_dims = index.dims();

if (index_dims.size() == 2) {
PADDLE_ENFORCE_EQ(index_dims[1],
1,
common::errors::InvalidArgument(
"The last dim of the index should be 1 when the "
"index is a 2D tensor, but we get %d.",
index_dims[1]));
if (index_dims[1] != 0) {
PADDLE_ENFORCE_EQ(index_dims[1],
1,
common::errors::InvalidArgument(
"The last dim of the index should be 1 when the "
"index is a 2D tensor, but we get %d.",
index_dims[1]));
}
} else {
PADDLE_ENFORCE_EQ(index_dims.size() == 1 || index_dims.size() == 0,
true,
Expand Down
15 changes: 14 additions & 1 deletion paddle/phi/kernels/cpu/scatter_grad_kernel.cc
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Expand Up @@ -17,9 +17,9 @@
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/gather.h"
#include "paddle/phi/kernels/funcs/scatter.h"

namespace phi {

template <typename T, typename Context>
Expand All @@ -30,6 +30,19 @@ void ScatterGradKernel(const Context &dev_ctx,
bool overwrite UNUSED,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
const auto &index_type = index.dtype();
bool index_type_match =
index_type == phi::DataType::INT32 || index_type == phi::DataType::INT64;
Expand Down
9 changes: 9 additions & 0 deletions paddle/phi/kernels/cpu/scatter_kernel.cc
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Expand Up @@ -28,6 +28,15 @@ void ScatterKernel(const Context &dev_ctx,
const DenseTensor &updates,
bool overwrite,
DenseTensor *out) {
if (index.numel() == 0) {
dev_ctx.template Alloc<T>(out);
phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
return;
}
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
// In place output: Out = X, Out[Ids] = Updates
phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
// Apply ScatterUpdate: Out[index] = Updates[:]
Expand Down
15 changes: 14 additions & 1 deletion paddle/phi/kernels/cpu/scatter_nd_add_grad_kernel.cc
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Expand Up @@ -17,8 +17,8 @@
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/gather.h"

namespace phi {

template <typename T, typename Context>
Expand All @@ -28,6 +28,19 @@ void ScatterNdAddGradKernel(const Context &dev_ctx,
const DenseTensor &out_grad,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
if (x_grad) {
Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
}
Expand Down
4 changes: 4 additions & 0 deletions paddle/phi/kernels/cpu/scatter_nd_add_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,10 @@ void ScatterNdAddKernel(const Context &dev_ctx,
const DenseTensor &index,
const DenseTensor &updates,
DenseTensor *out) {
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
// In place output: Out = X
Copy(dev_ctx, x, dev_ctx.GetPlace(), true, out);
const auto &index_type = index.dtype();
Expand Down
14 changes: 14 additions & 0 deletions paddle/phi/kernels/gpu/scatter_grad_kernel.cu
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Expand Up @@ -18,6 +18,7 @@
#include "paddle/phi/common/bfloat16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/gather.cu.h"
#include "paddle/phi/kernels/funcs/scatter.cu.h"

Expand All @@ -31,6 +32,19 @@ void ScatterGradKernel(const Context &dev_ctx,
bool overwrite,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
auto index_type = index.dtype();
bool index_type_match =
index_type == phi::DataType::INT32 || index_type == phi::DataType::INT64;
Expand Down
9 changes: 9 additions & 0 deletions paddle/phi/kernels/gpu/scatter_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,15 @@ void ScatterKernel(const Context &dev_ctx,
const DenseTensor &updates,
bool overwrite,
DenseTensor *out) {
if (index.numel() == 0) {
dev_ctx.template Alloc<T>(out);
phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
return;
}
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
// use template class to support int32_t and int64_t
auto index_type = index.dtype();
Expand Down
15 changes: 14 additions & 1 deletion paddle/phi/kernels/gpu/scatter_nd_add_grad_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@
#include "paddle/phi/common/bfloat16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/gather.cu.h"

namespace phi {

template <typename T, typename Context>
Expand All @@ -29,6 +29,19 @@ void ScatterNdAddGradKernel(const Context &dev_ctx,
const DenseTensor &out_grad,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
if (x_grad) {
Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
}
Expand Down
4 changes: 4 additions & 0 deletions paddle/phi/kernels/gpu/scatter_nd_add_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,10 @@ void ScatterNdAddKernel(const Context &dev_ctx,
const DenseTensor &index,
const DenseTensor &updates,
DenseTensor *out) {
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
const auto &index_type = index.dtype();
bool index_type_match =
Expand Down
14 changes: 14 additions & 0 deletions paddle/phi/kernels/xpu/scatter_grad_kernel.cc
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Expand Up @@ -16,6 +16,7 @@

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"

namespace phi {

Expand All @@ -27,6 +28,19 @@ void ScatterGradKernel(const Context &dev_ctx,
bool overwrite,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
using XPUType = typename XPUTypeTrait<T>::Type;

const auto &index_type = index.dtype();
Expand Down
9 changes: 9 additions & 0 deletions paddle/phi/kernels/xpu/scatter_kernel.cc
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Expand Up @@ -27,6 +27,15 @@ void ScatterKernel(const Context &dev_ctx,
const DenseTensor &updates,
bool overwrite,
DenseTensor *out) {
if (index.numel() == 0) {
dev_ctx.template Alloc<T>(out);
phi::Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
return;
}
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
using XPUTypeT = typename XPUTypeTrait<T>::Type;
out->Resize(x.dims());
auto *x_data = reinterpret_cast<const XPUTypeT *>(x.data<T>());
Expand Down
14 changes: 14 additions & 0 deletions paddle/phi/kernels/xpu/scatter_nd_add_grad_kernel.cc
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Expand Up @@ -16,6 +16,7 @@

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"

namespace phi {
template <typename T, typename Context>
Expand All @@ -25,6 +26,19 @@ void ScatterNdAddGradKernel(const Context &dev_ctx,
const DenseTensor &out_grad,
DenseTensor *x_grad,
DenseTensor *updates_grad) {
if (out_grad.numel() == 0) {
if (x_grad) {
dev_ctx.template Alloc<T>(x_grad);
}
if (updates_grad) {
phi::Full<T, Context>(
dev_ctx,
phi::IntArray(common::vectorize(updates_grad->dims())),
0,
updates_grad);
}
return;
}
using XPUType = typename XPUTypeTrait<T>::Type;
int ret = 0;
const T *out_grad_data = out_grad.data<T>();
Expand Down
4 changes: 4 additions & 0 deletions paddle/phi/kernels/xpu/scatter_nd_add_kernel.cc
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Expand Up @@ -24,6 +24,10 @@ void ScatterNdAddKernel(const Context &dev_ctx,
const DenseTensor &index,
const DenseTensor &updates,
DenseTensor *out) {
if (out && out->numel() == 0) {
dev_ctx.template Alloc<T>(out);
return;
}
const T *x_ptr = x.data<T>();
const T *updates_ptr = updates.data<T>();

Expand Down
3 changes: 3 additions & 0 deletions python/paddle/tensor/manipulation.py
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Expand Up @@ -4374,6 +4374,9 @@ def scatter_nd_add(
f"x and updates must have same data type but x.dtype={convert_dtype(x.dtype)}, updates.dtype={convert_dtype(updates.dtype)}"
)

if in_dynamic_mode():
if index.size == 0:
return x.clone() + updates
if in_dynamic_or_pir_mode():
return _C_ops.scatter_nd_add(x, index, updates)
else:
Expand Down
68 changes: 63 additions & 5 deletions test/legacy_test/test_scatter_nd_op.py
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Expand Up @@ -15,7 +15,7 @@
import unittest

import numpy as np
from op_test import OpTest, convert_float_to_uint16
from op_test import OpTest, convert_float_to_uint16, get_places
from utils import static_guard

import paddle
Expand Down Expand Up @@ -183,12 +183,12 @@ def setUp(self):
def _set_dtype(self):
self.dtype = np.float64

def test_check_output(self):
def _test_check_output(self):
self.check_output(
check_cinn=True, check_pir=True, check_symbol_infer=False
)

def test_check_grad(self):
def _test_check_grad(self):
self.check_grad(
['X', 'Updates'],
'Out',
Expand Down Expand Up @@ -220,12 +220,12 @@ class TestScatterNdAddWithEmptyIndexBF16(TestScatterNdAddWithEmptyIndex):
def _set_dtype(self):
self.dtype = np.uint16

def test_check_output(self):
def _test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_pir=True)

def test_check_grad(self):
def _test_check_grad(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_grad_with_place(
Expand Down Expand Up @@ -581,6 +581,64 @@ def test_dygraph_1(self):
output = paddle.scatter_nd_add(x, index, updates)


class TestScatterNd_ZeroSize(unittest.TestCase):
def test_dygraph(self):
for place in get_places():
with base.dygraph.guard(place):
index_data = np.random.random([0, 1])
index = paddle.to_tensor(index_data)
index.stop_gradient = False
updates = paddle.rand(shape=[4], dtype='float32')
updates.stop_gradient = False
shape = [4]
output = paddle.scatter_nd(index, updates, shape)
np.testing.assert_allclose(output.numpy(), updates.numpy())
output.sum().backward()
np.testing.assert_allclose(updates.grad.numpy(), np.ones([4]))


class TestScatterNdAdd_ZeroSize(unittest.TestCase):
def test_dygraph(self):
for place in get_places():
with base.dygraph.guard(place):
# x 0-size
x = paddle.randn([0, 2, 3])
x.stop_gradient = False
index_data = np.random.random([2, 3])
index = paddle.to_tensor(index_data)
updates = paddle.rand(shape=[2], dtype='float32')
updates.stop_gradient = False
output = paddle.scatter_nd_add(x, index, updates)
np.testing.assert_allclose(output.numpy(), x.numpy())
output.sum().backward()
np.testing.assert_allclose(x.grad.numpy(), np.zeros(x.shape))
np.testing.assert_allclose(
updates.grad.numpy(), np.zeros(updates.shape)
)


class TestScatterNdAdd_ZeroSize2(unittest.TestCase):
def test_dygraph(self):
for place in get_places():
with base.dygraph.guard(place):
# index 0-size
x = paddle.randn([1, 2])
x.stop_gradient = False
index_data = np.random.random([0, 3])
index = paddle.to_tensor(index_data)
updates = paddle.rand(shape=[1, 2], dtype='float32')
updates.stop_gradient = False
output = paddle.scatter_nd_add(x, index, updates)
np.testing.assert_allclose(
output.numpy(), (x + updates).numpy()
)
output.sum().backward()
np.testing.assert_allclose(x.grad.numpy(), np.ones(x.shape))
np.testing.assert_allclose(
updates.grad.numpy(), np.ones(updates.shape)
)


if __name__ == "__main__":
paddle.enable_static()
unittest.main()
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