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Support pt1.8 by removing OpMathType dependencies #212

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b56c347
Remove OpMathType dependencies
Johnson-Wang Jul 21, 2023
bd9e49b
Fix typos
Johnson-Wang Jul 21, 2023
19b0514
Add test for half
Johnson-Wang Jul 21, 2023
da2cba2
fix dcnv3_core_pytorch in fp16
Johnson-Wang Jul 25, 2023
11641c6
fix forced float conversion
Johnson-Wang Jul 25, 2023
57a3c0c
remove redundant conversion
Johnson-Wang Jul 25, 2023
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2 changes: 1 addition & 1 deletion ..._prediction/projects/mmdet3d_plugin/bevformer/backbones/ops_dcnv3/functions/dcnv3_func.py
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Expand Up @@ -174,7 +174,7 @@ def dcnv3_core_pytorch(
reshape(N_*group, group_channels, H_in, W_in)
# N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
flatten(0, 1)
flatten(0, 1).to(input_.dtype)
# N_*group, group_channels, H_out*W_out, P_
sampling_input_ = F.grid_sample(
input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)
Expand Down
7 changes: 2 additions & 5 deletions ...y_prediction/projects/mmdet3d_plugin/bevformer/backbones/ops_dcnv3/src/cuda/dcnv3_cuda.cu
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Expand Up @@ -68,7 +68,7 @@ at::Tensor dcnv3_cuda_forward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_forward_cuda", ([&] {
dcnv3_im2col_cuda(
dcnv3_im2col_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
offset.data<scalar_t>() +
Expand Down Expand Up @@ -124,9 +124,6 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
channels, group * group_channels);

auto dtype = input.dtype();
if (dtype == at::kHalf) {
dtype = at::kFloat;
}

auto grad_input = at::zeros_like(input, dtype);
auto grad_offset = at::zeros_like(offset, dtype);
Expand All @@ -146,7 +143,7 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_backward_cuda", ([&] {
dcnv3_col2im_cuda(
dcnv3_col2im_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
Expand Down
3 changes: 1 addition & 2 deletions ...tion/projects/mmdet3d_plugin/bevformer/backbones/ops_dcnv3/src/cuda/dcnv3_im2col_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
#include <cstring>

#include <ATen/ATen.h>
#include <ATen/OpMathType.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THCAtomics.cuh>

Expand All @@ -27,7 +26,7 @@ inline int GET_BLOCKS(const int N, const int num_threads) {
return (N + num_threads - 1) / num_threads;
}

#define opmath_t at::opmath_type<scalar_t>
#define opmath_t scalar_t

template <typename scalar_t>
__device__ opmath_t dcnv3_im2col_bilinear(const scalar_t *&bottom_data,
Expand Down
2 changes: 1 addition & 1 deletion ...ng/openlane-v2/plugin/mmdet3d/baseline/models/backbones/ops_dcnv3/functions/dcnv3_func.py
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Expand Up @@ -174,7 +174,7 @@ def dcnv3_core_pytorch(
reshape(N_*group, group_channels, H_in, W_in)
# N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
flatten(0, 1)
flatten(0, 1).to(input_.dtype)
# N_*group, group_channels, H_out*W_out, P_
sampling_input_ = F.grid_sample(
input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)
Expand Down
7 changes: 2 additions & 5 deletions ...ing/openlane-v2/plugin/mmdet3d/baseline/models/backbones/ops_dcnv3/src/cuda/dcnv3_cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ at::Tensor dcnv3_cuda_forward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_forward_cuda", ([&] {
dcnv3_im2col_cuda(
dcnv3_im2col_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
offset.data<scalar_t>() +
Expand Down Expand Up @@ -124,9 +124,6 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
channels, group * group_channels);

auto dtype = input.dtype();
if (dtype == at::kHalf) {
dtype = at::kFloat;
}

auto grad_input = at::zeros_like(input, dtype);
auto grad_offset = at::zeros_like(offset, dtype);
Expand All @@ -146,7 +143,7 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_backward_cuda", ([&] {
dcnv3_col2im_cuda(
dcnv3_col2im_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
Expand Down
3 changes: 1 addition & 2 deletions ...lane-v2/plugin/mmdet3d/baseline/models/backbones/ops_dcnv3/src/cuda/dcnv3_im2col_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
#include <cstring>

#include <ATen/ATen.h>
#include <ATen/OpMathType.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THCAtomics.cuh>

Expand All @@ -27,7 +26,7 @@ inline int GET_BLOCKS(const int N, const int num_threads) {
return (N + num_threads - 1) / num_threads;
}

#define opmath_t at::opmath_type<scalar_t>
#define opmath_t scalar_t

template <typename scalar_t>
__device__ opmath_t dcnv3_im2col_bilinear(const scalar_t *&bottom_data,
Expand Down
2 changes: 1 addition & 1 deletion classification/ops_dcnv3/functions/dcnv3_func.py
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Expand Up @@ -206,7 +206,7 @@ def dcnv3_core_pytorch(
reshape(N_*group, group_channels, H_in, W_in)
# N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
flatten(0, 1)
flatten(0, 1).to(input_.dtype)
# N_*group, group_channels, H_out*W_out, P_
sampling_input_ = F.grid_sample(
input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)
Expand Down
7 changes: 2 additions & 5 deletions classification/ops_dcnv3/src/cuda/dcnv3_cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ at::Tensor dcnv3_cuda_forward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_forward_cuda", ([&] {
dcnv3_im2col_cuda(
dcnv3_im2col_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
offset.data<scalar_t>() +
Expand Down Expand Up @@ -124,9 +124,6 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
channels, group * group_channels);

auto dtype = input.dtype();
if (dtype == at::kHalf) {
dtype = at::kFloat;
}

auto grad_input = at::zeros_like(input, dtype);
auto grad_offset = at::zeros_like(offset, dtype);
Expand All @@ -146,7 +143,7 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_backward_cuda", ([&] {
dcnv3_col2im_cuda(
dcnv3_col2im_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
Expand Down
3 changes: 1 addition & 2 deletions classification/ops_dcnv3/src/cuda/dcnv3_im2col_cuda.cuh
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Expand Up @@ -14,7 +14,6 @@
#include <cstring>

#include <ATen/ATen.h>
#include <ATen/OpMathType.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THCAtomics.cuh>

Expand All @@ -27,7 +26,7 @@ inline int GET_BLOCKS(const int N, const int num_threads) {
return (N + num_threads - 1) / num_threads;
}

#define opmath_t at::opmath_type<scalar_t>
#define opmath_t scalar_t

template <typename scalar_t>
__device__ opmath_t dcnv3_im2col_bilinear(const scalar_t *&bottom_data,
Expand Down
95 changes: 95 additions & 0 deletions classification/ops_dcnv3/test.py
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Expand Up @@ -61,6 +61,36 @@ def check_forward_equal_with_pytorch_double():
print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')


@torch.no_grad()
def check_forward_equal_with_pytorch_half():
input = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask /= mask.sum(-1, keepdim=True)
mask = mask.reshape(N, H_out, W_out, M*P)

output_pytorch = dcnv3_core_pytorch(
input.half(),
offset.half(),
mask.half(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale, remove_center).detach().cpu()

im2col_step = 2
output_cuda = DCNv3Function.apply(
input.half(),
offset.half(),
mask.half(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step, remove_center).detach().cpu()

fwdok = torch.allclose(output_cuda, output_pytorch)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() /
output_pytorch.abs()).max()
print('>>> forward half')
print(f'* {fwdok} check_forward_equal_with_pytorch_half: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')


@torch.no_grad()
def check_forward_equal_with_pytorch_float():
input = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
Expand Down Expand Up @@ -154,6 +184,68 @@ def check_backward_equal_with_pytorch_double(channels=4, grad_input=True, grad_o
f'* {bwdok} mask_grad check_backward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')


def check_backward_equal_with_pytorch_half(channels=4, grad_input=True, grad_offset=True, grad_mask=True):
# H_in, W_in = 4, 4
N = 2
M = 2
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1

D = channels
input0 = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset0 = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask0 = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask0 /= mask0.sum(-1, keepdim=True)
mask0 = mask0.reshape(N, H_out, W_out, M*P)
input0.requires_grad = grad_input
offset0.requires_grad = grad_offset
mask0.requires_grad = grad_mask

output_pytorch = dcnv3_core_pytorch(
input0.half(),
offset0.half(),
mask0.half(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale, remove_center)
output_pytorch.sum().backward()

input1 = input0.detach()
offset1 = offset0.detach()
mask1 = mask0.detach()
input1.requires_grad = grad_input
offset1.requires_grad = grad_offset
mask1.requires_grad = grad_mask

im2col_step = 2
output_cuda = DCNv3Function.apply(
input1.half(),
offset1.half(),
mask1.half(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step, remove_center)
output_cuda.sum().backward()

print(f'>>> backward half: channels {D}')
bwdok = torch.allclose(input0.grad, input1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (input0.grad - input1.grad).abs().max()
max_rel_err = ((input0.grad - input1.grad).abs() /
input0.grad.abs()).max()
print(
f'* {bwdok} input_grad check_backward_equal_with_pytorch_half: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')

bwdok = torch.allclose(offset0.grad, offset1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (offset0.grad - offset1.grad).abs().max()
max_rel_err = ((offset0.grad - offset1.grad).abs() /
offset0.grad.abs()).max()
print(
f'* {bwdok} offset_grad check_backward_equal_with_pytorch_half: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')

bwdok = torch.allclose(mask0.grad, mask1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (mask0.grad - mask1.grad).abs().max()
max_rel_err = ((mask0.grad - mask1.grad).abs() /
mask0.grad.abs()).max()
print(
f'* {bwdok} mask_grad check_backward_equal_with_pytorch_half: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')

def check_backward_equal_with_pytorch_float(channels=4, grad_input=True, grad_offset=True, grad_mask=True):
# H_in, W_in = 4, 4
N = 2
Expand Down Expand Up @@ -254,9 +346,12 @@ def check_time_cost(im2col_step=128):

if __name__ == '__main__':
check_forward_equal_with_pytorch_double()
check_forward_equal_with_pytorch_half()
check_forward_equal_with_pytorch_float()
for channels in [1, 16, 30, 32, 64, 71, 1025]:
check_backward_equal_with_pytorch_double(channels, True, True, True)
for channels in [1, 16, 30, 32, 64, 71, 1025]:
check_backward_equal_with_pytorch_half(channels, True, True, True)
for channels in [1, 16, 30, 32, 64, 71, 1025]:
check_backward_equal_with_pytorch_float(channels, True, True, True)
for i in range(3):
Expand Down
2 changes: 1 addition & 1 deletion detection/ops_dcnv3/functions/dcnv3_func.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,7 +174,7 @@ def dcnv3_core_pytorch(
reshape(N_*group, group_channels, H_in, W_in)
# N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
flatten(0, 1)
flatten(0, 1).to(input_.dtype)
# N_*group, group_channels, H_out*W_out, P_
sampling_input_ = F.grid_sample(
input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)
Expand Down
7 changes: 2 additions & 5 deletions detection/ops_dcnv3/src/cuda/dcnv3_cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ at::Tensor dcnv3_cuda_forward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_forward_cuda", ([&] {
dcnv3_im2col_cuda(
dcnv3_im2col_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
offset.data<scalar_t>() +
Expand Down Expand Up @@ -124,9 +124,6 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
channels, group * group_channels);

auto dtype = input.dtype();
if (dtype == at::kHalf) {
dtype = at::kFloat;
}

auto grad_input = at::zeros_like(input, dtype);
auto grad_offset = at::zeros_like(offset, dtype);
Expand All @@ -146,7 +143,7 @@ dcnv3_cuda_backward(const at::Tensor &input, const at::Tensor &offset,
// AT_DISPATCH_FLOATING_TYPES(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
input.type(), "ms_deform_attn_backward_cuda", ([&] {
dcnv3_col2im_cuda(
dcnv3_col2im_cuda<opmath_t>(
at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
input.data<scalar_t>() + n * im2col_step_ * per_input_size,
Expand Down
3 changes: 1 addition & 2 deletions detection/ops_dcnv3/src/cuda/dcnv3_im2col_cuda.cuh
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
#include <cstring>

#include <ATen/ATen.h>
#include <ATen/OpMathType.h>
#include <ATen/cuda/CUDAContext.h>
#include <THC/THCAtomics.cuh>

Expand All @@ -27,7 +26,7 @@ inline int GET_BLOCKS(const int N, const int num_threads) {
return (N + num_threads - 1) / num_threads;
}

#define opmath_t at::opmath_type<scalar_t>
#define opmath_t scalar_t

template <typename scalar_t>
__device__ opmath_t dcnv3_im2col_bilinear(const scalar_t *&bottom_data,
Expand Down
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