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Merge 947976cc17 into 856dde20ae

pull/409/merge
Andrew Choi 2025-04-16 15:31:33 -07:00 committed by GitHub
parent 856dde20ae 947976cc17
commit cf784db3aa
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GPG Key ID: B5690EEEBB952194
1 changed files with 31 additions and 18 deletions
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39
groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu
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@ -15,6 +15,19 @@
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <torch/extension.h>
#include <torch/version.h>
// Check PyTorch version and define appropriate macros
#if TORCH_VERSION_MAJOR >= 2 && TORCH_VERSION_MINOR >= 6
// PyTorch 2.x and above
#define GET_TENSOR_TYPE(x) x.scalar_type()
#define IS_CUDA_TENSOR(x) x.device().is_cuda()
#else
// PyTorch 1.x
#define GET_TENSOR_TYPE(x) x.type()
#define IS_CUDA_TENSOR(x) x.type().is_cuda()
#endif
namespace groundingdino {
@ -32,11 +45,11 @@ at::Tensor ms_deform_attn_cuda_forward(
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(value), "value must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(spatial_shapes), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(level_start_index), "level_start_index must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(sampling_loc), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(attn_weight), "attn_weight must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
@ -62,7 +75,7 @@ at::Tensor ms_deform_attn_cuda_forward(
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto columns = output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES(GET_TENSOR_TYPE(value), "ms_deform_attn_forward_cuda", ([&] {
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
@ -98,12 +111,12 @@ std::vector<at::Tensor> ms_deform_attn_cuda_backward(
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(value), "value must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(spatial_shapes), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(level_start_index), "level_start_index must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(sampling_loc), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(attn_weight), "attn_weight must be a CUDA tensor");
AT_ASSERTM(IS_CUDA_TENSOR(grad_output), "grad_output must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
@ -132,7 +145,7 @@ std::vector<at::Tensor> ms_deform_attn_cuda_backward(
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto grad_output_g = grad_output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
AT_DISPATCH_FLOATING_TYPES(GET_TENSOR_TYPE(value), "ms_deform_attn_backward_cuda", ([&] {
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,