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Support 0D for paddle.sort/argsort #49501

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merged 5 commits into from
Jan 5, 2023
Merged

Support 0D for paddle.sort/argsort #49501

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35c48c7
support 0D for paddle.sort/argsort
DesmonDay Jan 3, 2023
9443bc1
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
DesmonDay Jan 3, 2023
e6254e3
support 0D tensor for paddle.sort/argsort in xpu
DesmonDay Jan 3, 2023
7d315fa
fix bug
DesmonDay Jan 3, 2023
d486046
fix grad and add value assertion
DesmonDay Jan 4, 2023
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32 changes: 20 additions & 12 deletions paddle/phi/infermeta/unary.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -220,18 +220,26 @@ void ArgsortInferMeta(const MetaTensor& input,
MetaTensor* indices) {
auto in_dims = input.dims();
auto num_dims = in_dims.size();
PADDLE_ENFORCE_GE(
axis,
-num_dims,
phi::errors::InvalidArgument("'axis'(%d) must be greater than or equal to"
" -num_dims(%d).",
axis,
-num_dims));
PADDLE_ENFORCE_LT(
axis,
num_dims,
phi::errors::InvalidArgument(
"'axis'(%d) must be less than num_dims(%d).", axis, num_dims));
if (num_dims > 0) {
PADDLE_ENFORCE_GE(axis,
-num_dims,
phi::errors::InvalidArgument(
"'axis'(%d) must be greater than or equal to"
" -num_dims(%d).",
axis,
-num_dims));
PADDLE_ENFORCE_LT(
axis,
num_dims,
phi::errors::InvalidArgument(
"'axis'(%d) must be less than num_dims(%d).", axis, num_dims));
} else { // 0-dim tensor
PADDLE_ENFORCE_EQ(
axis == 0 || axis == -1,
1,
phi::errors::InvalidArgument(
"'axis'(%d) must be 0 or -1 if input tensor is 0-dim.", axis));
}

output->share_dims(input);
output->set_dtype(input.dtype());
Expand Down
6 changes: 6 additions & 0 deletions paddle/phi/kernels/cpu/argsort_grad_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -58,13 +58,19 @@ void ArgsortGradKernel(const Context& dev_ctx,
bool descending,
DenseTensor* in_grad) {
auto in_dims = indices.dims();
auto rank = input.dims().size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;
dev_ctx.template Alloc<T>(in_grad);
auto dxt = EigenVector<T>::Flatten(*in_grad);
auto& place = *dev_ctx.eigen_device();
dxt.device(place) = dxt.constant(static_cast<T>(0));
if (out_grad.numel() == 0) return;

if (rank == 0) {
phi::Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, in_grad);
return;
}

// Do full assign
if (axis == -1 || axis + 1 == in_dims.size()) {
const int64_t input_height =
Expand Down
10 changes: 10 additions & 0 deletions paddle/phi/kernels/cpu/argsort_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/transpose_kernel.h"

namespace phi {
Expand Down Expand Up @@ -75,9 +76,18 @@ void ArgsortKernel(const Context& dev_ctx,
DenseTensor* output,
DenseTensor* indices) {
auto in_dims = input.dims();
auto rank = in_dims.size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;
T* out_data = dev_ctx.template Alloc<T>(output);

// For 0D Tensor
if (rank == 0) {
phi::Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, output);
dev_ctx.template Alloc<int64_t>(indices);
phi::funcs::set_constant(dev_ctx, indices, 0);
return;
}

// Do full sort
if (axis == -1 || axis + 1 == in_dims.size()) {
const int64_t input_height =
Expand Down
8 changes: 8 additions & 0 deletions paddle/phi/kernels/gpu/argsort_grad_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ namespace cub = hipcub;

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/primitive/functor_primitives.h"
#include "paddle/phi/kernels/transpose_kernel.h"

Expand Down Expand Up @@ -141,11 +142,18 @@ void ArgsortGradKernel(const Context& dev_ctx,
bool descending,
DenseTensor* in_grad) {
dev_ctx.template Alloc<T>(in_grad);
phi::funcs::set_constant(dev_ctx, in_grad, 0.0);
if (out_grad.numel() == 0) return;
auto in_dims = in_grad->dims();
auto rank = in_dims.size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;
int64_t size = in_grad->numel();

if (rank == 0) {
phi::Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, in_grad);
return;
}

// Parallel acceleration when the input size is equal to the length of the
// ‘axis’ dimension.
// Compared to 'special case for full sort' below, the gradient calculation
Expand Down
8 changes: 8 additions & 0 deletions paddle/phi/kernels/gpu/argsort_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ namespace cub = hipcub;
#include "paddle/phi/backends/gpu/gpu_info.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/primitive/functor_primitives.h"
#include "paddle/phi/kernels/transpose_kernel.h"

Expand Down Expand Up @@ -396,13 +397,20 @@ void ArgsortKernel(const Context &dev_ctx,
DenseTensor *output,
DenseTensor *indices) {
auto in_dims = input.dims();
auto rank = in_dims.size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;

const T *in_data = input.data<T>();
auto size = input.numel();
T *out_data = dev_ctx.template Alloc<T>(output);
int64_t *ids_data = dev_ctx.template Alloc<int64_t>(indices);

if (rank == 0) {
phi::Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, output);
phi::funcs::set_constant(dev_ctx, indices, 0);
return;
}

// Use thrust for parallel acceleration when the input size is equal to the
// length of the ‘axis’ dimension.
// Compared to the following 'Special case for full sort', ascending sort is
Expand Down
6 changes: 6 additions & 0 deletions paddle/phi/kernels/xpu/argsort_grad_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@ void ArgsortGradKernel(const Context& dev_ctx,
bool descending,
DenseTensor* in_grad) {
auto in_dims = indices.dims();
auto rank = in_dims.size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;
dev_ctx.template Alloc<T>(in_grad);

Expand All @@ -40,6 +41,11 @@ void ArgsortGradKernel(const Context& dev_ctx,

if (out_grad.numel() == 0) return;

if (rank == 0) {
phi::Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, in_grad);
return;
}

bool is_need_transpose = true;
if (axis == -1 || axis + 1 == in_dims.size()) {
is_need_transpose = false;
Expand Down
8 changes: 8 additions & 0 deletions paddle/phi/kernels/xpu/argsort_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"

namespace phi {

Expand Down Expand Up @@ -171,13 +172,20 @@ void ArgsortKernel(const Context& dev_ctx,
DenseTensor* output,
DenseTensor* indices) {
auto in_dims = input.dims();
auto rank = in_dims.size();
axis = (axis < 0) ? (in_dims.size() + axis) : axis;
int n = in_dims[axis];

auto input_data = input.data<T>();
auto output_data = dev_ctx.template Alloc<T>(output);
auto indices_data = dev_ctx.template Alloc<int64_t>(indices);

if (rank == 0) {
phi::Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, output);
phi::funcs::set_constant(dev_ctx, indices, 0);
return;
}

int len_before = phi::product(phi::slice_ddim(in_dims, 0, axis));
int len_after =
phi::product(phi::slice_ddim(in_dims, axis + 1, in_dims.size()));
Expand Down
80 changes: 80 additions & 0 deletions python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py
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Expand Up @@ -855,6 +855,50 @@ def test_reverse(self):
self.assertEqual(out.shape, [])
self.assertEqual(out.grad.shape, [])

def test_sort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
out1 = paddle.sort(x1, axis=-1)
out2 = paddle.sort(x2, axis=0)

out1.backward()
out2.backward()

self.assertEqual(out1.shape, [])
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前反向kernel计算逻辑没问题，可以在单测里面再加一下前反向值的检查，因为值比较固定。XPU也一样

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done

self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), x1.numpy())
self.assertEqual(out2.numpy(), x2.numpy())
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 1)
self.assertEqual(x2.grad.numpy(), 1)

def test_argsort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
out1 = paddle.argsort(x1, axis=-1)
out2 = paddle.argsort(x2, axis=0)

out1.backward()
out2.backward()

self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), 0)
self.assertEqual(out2.numpy(), 0)
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 0)
self.assertEqual(x2.grad.numpy(), 0)


class TestSundryAPIStatic(unittest.TestCase):
def setUp(self):
Expand Down Expand Up @@ -1182,6 +1226,42 @@ def test_reverse(self):
self.assertEqual(res1.shape, ())
self.assertEqual(res2.shape, ())

@prog_scope()
def test_sort(self):
x1 = paddle.rand([])
x1.stop_gradient = False
out1 = paddle.sort(x1, axis=-1)
paddle.static.append_backward(out1)

x2 = paddle.rand([])
x2.stop_gradient = False
out2 = paddle.sort(x2, axis=0)
paddle.static.append_backward(out2)

prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out1, out2])

self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())

@prog_scope()
def test_argsort(self):
x1 = paddle.rand([])
x1.stop_gradient = False
out1 = paddle.argsort(x1, axis=-1)
paddle.static.append_backward(out1)

x2 = paddle.rand([])
x2.stop_gradient = False
out2 = paddle.argsort(x2, axis=0)
paddle.static.append_backward(out2)

prog = paddle.static.default_main_program()
res = self.exe.run(prog, fetch_list=[out1, out2])

self.assertEqual(res[0].shape, ())
self.assertEqual(res[1].shape, ())


# Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
class TestNoBackwardAPI(unittest.TestCase):
Expand Down
44 changes: 44 additions & 0 deletions python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py
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Expand Up @@ -646,6 +646,50 @@ def test_reshape__tensor(self):
out = paddle.reshape_(x, new_shape)
self.assertEqual(out.shape, [1, 1])

def test_sort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
out1 = paddle.sort(x1, axis=-1)
out2 = paddle.sort(x2, axis=0)

out1.backward()
out2.backward()

self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), x1.numpy())
self.assertEqual(out2.numpy(), x2.numpy())
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 1)
self.assertEqual(x2.grad.numpy(), 1)

def test_argsort(self):
x1 = paddle.rand([])
x2 = paddle.rand([])
x1.stop_gradient = False
x2.stop_gradient = False
out1 = paddle.argsort(x1, axis=-1)
out2 = paddle.argsort(x2, axis=0)

out1.backward()
out2.backward()

self.assertEqual(out1.shape, [])
self.assertEqual(out2.shape, [])
self.assertEqual(out1.numpy(), 0)
self.assertEqual(out2.numpy(), 0)
self.assertEqual(out1.grad.shape, [])
self.assertEqual(out2.grad.shape, [])
self.assertEqual(x1.grad.shape, [])
self.assertEqual(x2.grad.shape, [])
self.assertEqual(x1.grad.numpy(), 0)
self.assertEqual(x2.grad.numpy(), 0)


# Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
class TestNoBackwardAPI(unittest.TestCase):
Expand Down