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add pta for group_points
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@wujiadi1
wujiadi1 committed May 28, 2024
1 parent 528e466 commit 990d9d7
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Showing 2 changed files with 110 additions and 14 deletions.
65 changes: 65 additions & 0 deletions mmcv/ops/csrc/pytorch/npu/group_points_npu.cpp
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Original file line number Diff line number Diff line change
@@ -0,0 +1,65 @@
#include "pytorch_npu_helper.hpp"

using namespace NPU_NAME_SPACE;
using namespace std;

void group_points_forward_npu(int b, int c, int n, int npoints, int nsample,
const Tensor points, const Tensor idx,
Tensor out) {
// b, c, n, and npoints do not need to be passed into gatherv2,
// b, c, n, and npoints are calculated inside the operator
// gatherv2 operator in ascend needs to set axis to 0, batch_dims is 0
c10::SmallVector<int64_t, N> axis = {0};
int64_t batch_dims = 0;

auto index = at::arange(0, b);
index = index.to(points.device());
index = index.view({-1, 1, 1});
index = at::mul(index, n);
at::Tensor indices = at::add(index, idx);
indices = indices.view({-1});

at::Tensor trans_features = points.transpose(1, 2);
at::Tensor features = trans_features.contiguous();
features = features.view({b * n, c});

OpCommand cmd;
cmd.Name("GatherV2")
.Input(features)
.Input(indices)
.Input(axis)
.Output(out)
.Attr("batch_dims", batch_dims)
.Run();

at::Tensor output =
out.view({b, npoints, nsample, c}).transpose(1, 3).transpose(2, 3);
at::Tensor res = output.contiguous();
out.copy_(res);
}

void group_points_backward_npu(int b, int c, int n, int npoints, int nsample,
const Tensor grad_out, const Tensor idx, Tensor grad_features)
{
at::Tensor trans_idx = idx.view({b * npoints * nsample});
at::Tensor trans_grad_out = grad_out.permute({0, 2, 3, 1});
at::Tensor grad_out_tensor = trans_grad_out.contiguous();
grad_out_tensor = grad_out_tensor.view({b * npoints * nsample, c});
at::Tensor out = at::zeros({b, n, c}, grad_out.options());

EXEC_NPU_CMD(aclnnGroupPointsGrad, grad_out_tensor, trans_idx, b, c, n, npoints, nsample, out);

at::Tensor grad_points = out.transpose(1, 2);

grad_features.copy_(grad_points);
}

void group_points_forward_impl(int b, int c, int n, int npoints, int nsample,
const Tensor points, const Tensor idx,
Tensor out);
void group_points_backward_impl(int b, int c, int n, int npoints, int nsample,
const Tensor points, const Tensor idx,
Tensor out);

REGISTER_NPU_IMPL(group_points_forward_impl, group_points_forward_npu);
REGISTER_NPU_IMPL(group_points_backward_impl, group_points_backward_npu);
59 changes: 45 additions & 14 deletions tests/test_ops/test_group_points.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,25 @@
import torch

from mmcv.ops import grouping_operation
from mmcv.utils import IS_CUDA_AVAILABLE, IS_NPU_AVAILABLE


@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
pytest.param(
'npu',
marks=pytest.mark.skipif(
not IS_NPU_AVAILABLE, reason='requires NPU support'))
])
@pytest.mark.parametrize('dtype', [torch.half, torch.float, torch.double])
def test_grouping_points(dtype):
def test_grouping_points(dtype, device):
idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0],
[0, 0, 0]]]).int().cuda()
[0, 0, 0]]]).int().to(device)
features = torch.tensor([[[
0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
0.9268, 0.8414
Expand All @@ -37,9 +46,12 @@ def test_grouping_points(dtype):
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]]],
dtype=dtype).cuda()
dtype=dtype).to(device)
features.requires_grad = True

output = grouping_operation(features, idx)
output.backward(output)
grad_features = features.grad
expected_output = torch.tensor(
[[[[0.5798, 0.5798, 0.5798], [-1.3311, -1.3311, -1.3311],
[0.9268, 0.9268, 0.9268], [0.5798, 0.5798, 0.5798],
Expand All @@ -59,17 +71,36 @@ def test_grouping_points(dtype):
[[-0.6646, -0.6646, -0.6646], [0.4990, 0.4990, 0.4990],
[0.0386, 0.0386, 0.0386], [-0.6646, -0.6646, -0.6646],
[-0.6646, -0.6646, -0.6646], [-0.6646, -0.6646, -0.6646]]]],
dtype=dtype).cuda()
dtype=dtype).to(device)
expected_grad_features = torch.tensor(
[[[6.9576, 0.0000, 0.0000, -3.9933, 0.0000, 0.0000, 0.0000, 0.0000, 2.7804, 0.0000],
[65.0964, 0.0000, 0.0000, 4.4220, 0.0000, 0.0000, 0.0000, 0.0000, 6.4743, 0.0000],
[-19.5192, 0.0000, 0.0000, -5.0793, 0.0000, 0.0000, 0.0000, 0.0000, -5.0358, 0.0000]],
[[-0.4560, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, -1.1079, 0.0000, 0.0000, -5.5581],
[14.1276, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 18.2595, 0.0000, 0.0000, 8.4687],
[-7.9752, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.4970, 0.0000, 0.0000, 0.1158]]],
dtype=dtype).to(device)
assert torch.allclose(output, expected_output)
assert torch.allclose(grad_features, expected_grad_features)


@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
pytest.param(
'npu',
marks=pytest.mark.skipif(
not IS_NPU_AVAILABLE, reason='requires NPU support'))
])
@pytest.mark.parametrize('dtype', [torch.half, torch.float, torch.double])
def test_stack_grouping_points(dtype):
def test_stack_grouping_points(dtype, device):
if device == 'npu' and dtype == torch.double:
return
idx = torch.tensor([[0, 0, 0], [3, 3, 3], [8, 8, 8], [1, 1, 1], [0, 0, 0],
[2, 2, 2], [0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0],
[1, 1, 1], [0, 0, 0]]).int().cuda()
[1, 1, 1], [0, 0, 0]]).int().to(device)
features = torch.tensor([[
0.5798, -0.7981, -0.9280, -1.3311, 1.3687, 0.9277, -0.4164, -1.8274,
0.9268, 0.8414
Expand All @@ -94,9 +125,9 @@ def test_stack_grouping_points(dtype):
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
]],
dtype=dtype).cuda()
features_batch_cnt = torch.tensor([3, 3]).int().cuda()
indices_batch_cnt = torch.tensor([6, 6]).int().cuda()
dtype=dtype).to(device)
features_batch_cnt = torch.tensor([3, 3]).int().to(device)
indices_batch_cnt = torch.tensor([6, 6]).int().to(device)
output = grouping_operation(features, idx, features_batch_cnt,
indices_batch_cnt)
expected_output = torch.tensor(
Expand Down Expand Up @@ -160,5 +191,5 @@ def test_stack_grouping_points(dtype):
[-0.3190, -0.3190, -0.3190], [0.7798, 0.7798, 0.7798],
[-0.3693, -0.3693, -0.3693], [-0.9457, -0.9457, -0.9457],
[-0.2942, -0.2942, -0.2942], [-1.8527, -1.8527, -1.8527]]],
dtype=dtype).cuda()
dtype=dtype).to(device)
assert torch.allclose(output, expected_output)

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