Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

third_party/vipe/csrc/grounding_dino_ext/ms_deform_attn_cuda.cu

@@ -0,0 +1,136 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+#include "ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                       const at::Tensor &level_start_index, const at::Tensor &sampling_loc,
+                                       const at::Tensor &attn_weight, const int im2col_step) {
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    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.is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+    const int batch_n = im2col_step_;
+    auto output_n = output.view({batch / im2col_step_, batch_n, num_query, num_heads, channels});
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    for (int n = 0; n < batch / im2col_step_; ++n) {
+        auto columns = output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(
+            value.scalar_type(), "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>(), level_start_index.data<int64_t>(),
+                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size, batch_n, spatial_size,
+                    num_heads, channels, num_levels, num_query, num_point, columns.data<scalar_t>());
+            }));
+    }
+
+    output = output.view({batch, num_query, num_heads * channels});
+
+    return output;
+}
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                                     const at::Tensor &level_start_index,
+                                                     const at::Tensor &sampling_loc, const at::Tensor &attn_weight,
+                                                     const at::Tensor &grad_output, const int im2col_step) {
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    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(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+    AT_ASSERTM(value.is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.is_cuda(), "attn_weight must be a CUDA tensor");
+    AT_ASSERTM(grad_output.is_cuda(), "grad_output must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto grad_value = at::zeros_like(value);
+    auto grad_sampling_loc = at::zeros_like(sampling_loc);
+    auto grad_attn_weight = at::zeros_like(attn_weight);
+
+    const int batch_n = im2col_step_;
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    auto grad_output_n = grad_output.view({batch / im2col_step_, batch_n, num_query, num_heads, channels});
+
+    for (int n = 0; n < batch / im2col_step_; ++n) {
+        auto grad_output_g = grad_output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.scalar_type(), "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,
+                                           spatial_shapes.data<int64_t>(), level_start_index.data<int64_t>(),
+                                           sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                           attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                                           batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                                           grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                                           grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                           grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+                                   }));
+    }
+
+    return {grad_value, grad_sampling_loc, grad_attn_weight};
+}
+
+}  // namespace groundingdino

third_party/vipe/csrc/grounding_dino_ext/ms_deform_attn_cuda.h

@@ -0,0 +1,25 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                       const at::Tensor &level_start_index, const at::Tensor &sampling_loc,
+                                       const at::Tensor &attn_weight, const int im2col_step);
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                                     const at::Tensor &level_start_index,
+                                                     const at::Tensor &sampling_loc, const at::Tensor &attn_weight,
+                                                     const at::Tensor &grad_output, const int im2col_step);
+
+}  // namespace groundingdino

third_party/vipe/csrc/grounding_dino_ext/ms_deform_im2col_cuda.cuh

@@ -0,0 +1,914 @@
+/*!
+**************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************
+* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
+* Copyright (c) 2018 Microsoft
+**************************************************************************
+*/
+
+#include <cstdio>
+#include <algorithm>
+#include <cstring>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THCAtomics.cuh>
+
+#define CUDA_KERNEL_LOOP(i, n) for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+inline int GET_BLOCKS(const int N, const int num_threads) { return (N + num_threads - 1) / num_threads; }
+
+template <typename scalar_t>
+__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t *&bottom_data, const int &height, const int &width,
+                                                   const int &nheads, const int &channels, const scalar_t &h,
+                                                   const scalar_t &w, const int &m, const int &c) {
+    const int h_low = floor(h);
+    const int w_low = floor(w);
+    const int h_high = h_low + 1;
+    const int w_high = w_low + 1;
+
+    const scalar_t lh = h - h_low;
+    const scalar_t lw = w - w_low;
+    const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+    const int w_stride = nheads * channels;
+    const int h_stride = width * w_stride;
+    const int h_low_ptr_offset = h_low * h_stride;
+    const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+    const int w_low_ptr_offset = w_low * w_stride;
+    const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+    const int base_ptr = m * channels + c;
+
+    scalar_t v1 = 0;
+    if (h_low >= 0 && w_low >= 0) {
+        const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+        v1 = bottom_data[ptr1];
+    }
+    scalar_t v2 = 0;
+    if (h_low >= 0 && w_high <= width - 1) {
+        const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+        v2 = bottom_data[ptr2];
+    }
+    scalar_t v3 = 0;
+    if (h_high <= height - 1 && w_low >= 0) {
+        const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+        v3 = bottom_data[ptr3];
+    }
+    scalar_t v4 = 0;
+    if (h_high <= height - 1 && w_high <= width - 1) {
+        const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+        v4 = bottom_data[ptr4];
+    }
+
+    const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+    const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+    return val;
+}
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear(const scalar_t *&bottom_data, const int &height, const int &width,
+                                               const int &nheads, const int &channels, const scalar_t &h,
+                                               const scalar_t &w, const int &m, const int &c, const scalar_t &top_grad,
+                                               const scalar_t &attn_weight, scalar_t *&grad_value,
+                                               scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) {
+    const int h_low = floor(h);
+    const int w_low = floor(w);
+    const int h_high = h_low + 1;
+    const int w_high = w_low + 1;
+
+    const scalar_t lh = h - h_low;
+    const scalar_t lw = w - w_low;
+    const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+    const int w_stride = nheads * channels;
+    const int h_stride = width * w_stride;
+    const int h_low_ptr_offset = h_low * h_stride;
+    const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+    const int w_low_ptr_offset = w_low * w_stride;
+    const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+    const int base_ptr = m * channels + c;
+
+    const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+    const scalar_t top_grad_value = top_grad * attn_weight;
+    scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+    scalar_t v1 = 0;
+    if (h_low >= 0 && w_low >= 0) {
+        const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+        v1 = bottom_data[ptr1];
+        grad_h_weight -= hw * v1;
+        grad_w_weight -= hh * v1;
+        atomicAdd(grad_value + ptr1, w1 * top_grad_value);
+    }
+    scalar_t v2 = 0;
+    if (h_low >= 0 && w_high <= width - 1) {
+        const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+        v2 = bottom_data[ptr2];
+        grad_h_weight -= lw * v2;
+        grad_w_weight += hh * v2;
+        atomicAdd(grad_value + ptr2, w2 * top_grad_value);
+    }
+    scalar_t v3 = 0;
+    if (h_high <= height - 1 && w_low >= 0) {
+        const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+        v3 = bottom_data[ptr3];
+        grad_h_weight += hw * v3;
+        grad_w_weight -= lh * v3;
+        atomicAdd(grad_value + ptr3, w3 * top_grad_value);
+    }
+    scalar_t v4 = 0;
+    if (h_high <= height - 1 && w_high <= width - 1) {
+        const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+        v4 = bottom_data[ptr4];
+        grad_h_weight += lw * v4;
+        grad_w_weight += lh * v4;
+        atomicAdd(grad_value + ptr4, w4 * top_grad_value);
+    }
+
+    const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+    *grad_attn_weight = top_grad * val;
+    *grad_sampling_loc = width * grad_w_weight * top_grad_value;
+    *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
+}
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t *&bottom_data, const int &height, const int &width,
+                                                  const int &nheads, const int &channels, const scalar_t &h,
+                                                  const scalar_t &w, const int &m, const int &c,
+                                                  const scalar_t &top_grad, const scalar_t &attn_weight,
+                                                  scalar_t *&grad_value, scalar_t *grad_sampling_loc,
+                                                  scalar_t *grad_attn_weight) {
+    const int h_low = floor(h);
+    const int w_low = floor(w);
+    const int h_high = h_low + 1;
+    const int w_high = w_low + 1;
+
+    const scalar_t lh = h - h_low;
+    const scalar_t lw = w - w_low;
+    const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+    const int w_stride = nheads * channels;
+    const int h_stride = width * w_stride;
+    const int h_low_ptr_offset = h_low * h_stride;
+    const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+    const int w_low_ptr_offset = w_low * w_stride;
+    const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+    const int base_ptr = m * channels + c;
+
+    const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+    const scalar_t top_grad_value = top_grad * attn_weight;
+    scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+    scalar_t v1 = 0;
+    if (h_low >= 0 && w_low >= 0) {
+        const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+        v1 = bottom_data[ptr1];
+        grad_h_weight -= hw * v1;
+        grad_w_weight -= hh * v1;
+        atomicAdd(grad_value + ptr1, w1 * top_grad_value);
+    }
+    scalar_t v2 = 0;
+    if (h_low >= 0 && w_high <= width - 1) {
+        const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+        v2 = bottom_data[ptr2];
+        grad_h_weight -= lw * v2;
+        grad_w_weight += hh * v2;
+        atomicAdd(grad_value + ptr2, w2 * top_grad_value);
+    }
+    scalar_t v3 = 0;
+    if (h_high <= height - 1 && w_low >= 0) {
+        const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+        v3 = bottom_data[ptr3];
+        grad_h_weight += hw * v3;
+        grad_w_weight -= lh * v3;
+        atomicAdd(grad_value + ptr3, w3 * top_grad_value);
+    }
+    scalar_t v4 = 0;
+    if (h_high <= height - 1 && w_high <= width - 1) {
+        const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+        v4 = bottom_data[ptr4];
+        grad_h_weight += lw * v4;
+        grad_w_weight += lh * v4;
+        atomicAdd(grad_value + ptr4, w4 * top_grad_value);
+    }
+
+    const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+    atomicAdd(grad_attn_weight, top_grad * val);
+    atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
+    atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_im2col_gpu_kernel(const int n, const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index,
+                                                const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+                                                const int batch_size, const int spatial_size, const int num_heads,
+                                                const int channels, const int num_levels, const int num_query,
+                                                const int num_point, scalar_t *data_col) {
+    CUDA_KERNEL_LOOP(index, n) {
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        scalar_t *data_col_ptr = data_col + index;
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+        scalar_t col = 0;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels,
+                                                          h_im, w_im, m_col, c_col) *
+                           weight;
+                }
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+            }
+        }
+        *data_col_ptr = col;
+    }
+}
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(
+    const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+    const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+    const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels,
+    const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc,
+    scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+        __shared__ scalar_t cache_grad_attn_weight[blockSize];
+        unsigned int tid = threadIdx.x;
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                *(cache_grad_sampling_loc + (threadIdx.x << 1)) = 0;
+                *(cache_grad_sampling_loc + ((threadIdx.x << 1) + 1)) = 0;
+                *(cache_grad_attn_weight + threadIdx.x) = 0;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                   w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                   cache_grad_sampling_loc + (threadIdx.x << 1),
+                                                   cache_grad_attn_weight + threadIdx.x);
+                }
+
+                __syncthreads();
+                if (tid == 0) {
+                    scalar_t _grad_w = cache_grad_sampling_loc[0], _grad_h = cache_grad_sampling_loc[1],
+                             _grad_a = cache_grad_attn_weight[0];
+                    int sid = 2;
+                    for (unsigned int tid = 1; tid < blockSize; ++tid) {
+                        _grad_w += cache_grad_sampling_loc[sid];
+                        _grad_h += cache_grad_sampling_loc[sid + 1];
+                        _grad_a += cache_grad_attn_weight[tid];
+                        sid += 2;
+                    }
+
+                    *grad_sampling_loc = _grad_w;
+                    *(grad_sampling_loc + 1) = _grad_h;
+                    *grad_attn_weight = _grad_a;
+                }
+                __syncthreads();
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(
+    const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+    const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+    const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels,
+    const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc,
+    scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+        __shared__ scalar_t cache_grad_attn_weight[blockSize];
+        unsigned int tid = threadIdx.x;
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                *(cache_grad_sampling_loc + (threadIdx.x << 1)) = 0;
+                *(cache_grad_sampling_loc + ((threadIdx.x << 1) + 1)) = 0;
+                *(cache_grad_attn_weight + threadIdx.x) = 0;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                   w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                   cache_grad_sampling_loc + (threadIdx.x << 1),
+                                                   cache_grad_attn_weight + threadIdx.x);
+                }
+
+                __syncthreads();
+
+                for (unsigned int s = blockSize / 2; s > 0; s >>= 1) {
+                    if (tid < s) {
+                        const unsigned int xid1 = tid << 1;
+                        const unsigned int xid2 = (tid + s) << 1;
+                        cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+                        cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+                        cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+                    }
+                    __syncthreads();
+                }
+
+                if (tid == 0) {
+                    *grad_sampling_loc = cache_grad_sampling_loc[0];
+                    *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+                    *grad_attn_weight = cache_grad_attn_weight[0];
+                }
+                __syncthreads();
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(
+    const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+    const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+    const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels,
+    const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc,
+    scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        extern __shared__ int _s[];
+        scalar_t *cache_grad_sampling_loc = (scalar_t *)_s;
+        scalar_t *cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+        unsigned int tid = threadIdx.x;
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                *(cache_grad_sampling_loc + (threadIdx.x << 1)) = 0;
+                *(cache_grad_sampling_loc + ((threadIdx.x << 1) + 1)) = 0;
+                *(cache_grad_attn_weight + threadIdx.x) = 0;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                   w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                   cache_grad_sampling_loc + (threadIdx.x << 1),
+                                                   cache_grad_attn_weight + threadIdx.x);
+                }
+
+                __syncthreads();
+                if (tid == 0) {
+                    scalar_t _grad_w = cache_grad_sampling_loc[0], _grad_h = cache_grad_sampling_loc[1],
+                             _grad_a = cache_grad_attn_weight[0];
+                    int sid = 2;
+                    for (unsigned int tid = 1; tid < blockDim.x; ++tid) {
+                        _grad_w += cache_grad_sampling_loc[sid];
+                        _grad_h += cache_grad_sampling_loc[sid + 1];
+                        _grad_a += cache_grad_attn_weight[tid];
+                        sid += 2;
+                    }
+
+                    *grad_sampling_loc = _grad_w;
+                    *(grad_sampling_loc + 1) = _grad_h;
+                    *grad_attn_weight = _grad_a;
+                }
+                __syncthreads();
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(
+    const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+    const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+    const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels,
+    const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc,
+    scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        extern __shared__ int _s[];
+        scalar_t *cache_grad_sampling_loc = (scalar_t *)_s;
+        scalar_t *cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+        unsigned int tid = threadIdx.x;
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                *(cache_grad_sampling_loc + (threadIdx.x << 1)) = 0;
+                *(cache_grad_sampling_loc + ((threadIdx.x << 1) + 1)) = 0;
+                *(cache_grad_attn_weight + threadIdx.x) = 0;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                   w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                   cache_grad_sampling_loc + (threadIdx.x << 1),
+                                                   cache_grad_attn_weight + threadIdx.x);
+                }
+
+                __syncthreads();
+
+                for (unsigned int s = blockDim.x / 2, spre = blockDim.x; s > 0; s >>= 1, spre >>= 1) {
+                    if (tid < s) {
+                        const unsigned int xid1 = tid << 1;
+                        const unsigned int xid2 = (tid + s) << 1;
+                        cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+                        cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+                        cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+                        if (tid + (s << 1) < spre) {
+                            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+                            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+                            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+                        }
+                    }
+                    __syncthreads();
+                }
+
+                if (tid == 0) {
+                    *grad_sampling_loc = cache_grad_sampling_loc[0];
+                    *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+                    *grad_attn_weight = cache_grad_attn_weight[0];
+                }
+                __syncthreads();
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(
+    const int n, const scalar_t *grad_col, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+    const int64_t *data_level_start_index, const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+    const int batch_size, const int spatial_size, const int num_heads, const int channels, const int num_levels,
+    const int num_query, const int num_point, scalar_t *grad_value, scalar_t *grad_sampling_loc,
+    scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        extern __shared__ int _s[];
+        scalar_t *cache_grad_sampling_loc = (scalar_t *)_s;
+        scalar_t *cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+        unsigned int tid = threadIdx.x;
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                *(cache_grad_sampling_loc + (threadIdx.x << 1)) = 0;
+                *(cache_grad_sampling_loc + ((threadIdx.x << 1) + 1)) = 0;
+                *(cache_grad_attn_weight + threadIdx.x) = 0;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                   w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                   cache_grad_sampling_loc + (threadIdx.x << 1),
+                                                   cache_grad_attn_weight + threadIdx.x);
+                }
+
+                __syncthreads();
+
+                for (unsigned int s = blockDim.x / 2, spre = blockDim.x; s > 0; s >>= 1, spre >>= 1) {
+                    if (tid < s) {
+                        const unsigned int xid1 = tid << 1;
+                        const unsigned int xid2 = (tid + s) << 1;
+                        cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+                        cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+                        cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+                        if (tid + (s << 1) < spre) {
+                            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+                            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+                            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+                        }
+                    }
+                    __syncthreads();
+                }
+
+                if (tid == 0) {
+                    atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
+                    atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
+                    atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
+                }
+                __syncthreads();
+
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, const scalar_t *grad_col, const scalar_t *data_value,
+                                                   const int64_t *data_spatial_shapes,
+                                                   const int64_t *data_level_start_index,
+                                                   const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+                                                   const int batch_size, const int spatial_size, const int num_heads,
+                                                   const int channels, const int num_levels, const int num_query,
+                                                   const int num_point, scalar_t *grad_value,
+                                                   scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) {
+    CUDA_KERNEL_LOOP(index, n) {
+        int _temp = index;
+        const int c_col = _temp % channels;
+        _temp /= channels;
+        const int sampling_index = _temp;
+        const int m_col = _temp % num_heads;
+        _temp /= num_heads;
+        const int q_col = _temp % num_query;
+        _temp /= num_query;
+        const int b_col = _temp;
+
+        const scalar_t top_grad = grad_col[index];
+
+        int data_weight_ptr = sampling_index * num_levels * num_point;
+        int data_loc_w_ptr = data_weight_ptr << 1;
+        const int grad_sampling_ptr = data_weight_ptr;
+        grad_sampling_loc += grad_sampling_ptr << 1;
+        grad_attn_weight += grad_sampling_ptr;
+        const int grad_weight_stride = 1;
+        const int grad_loc_stride = 2;
+        const int qid_stride = num_heads * channels;
+        const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+        for (int l_col = 0; l_col < num_levels; ++l_col) {
+            const int level_start_id = data_level_start_index[l_col];
+            const int spatial_h_ptr = l_col << 1;
+            const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+            const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+            const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+            const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+            scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+            for (int p_col = 0; p_col < num_point; ++p_col) {
+                const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+                const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+                const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+                const scalar_t h_im = loc_h * spatial_h - 0.5;
+                const scalar_t w_im = loc_w * spatial_w - 0.5;
+                if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) {
+                    ms_deform_attn_col2im_bilinear_gm(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im,
+                                                      w_im, m_col, c_col, top_grad, weight, grad_value_ptr,
+                                                      grad_sampling_loc, grad_attn_weight);
+                }
+                data_weight_ptr += 1;
+                data_loc_w_ptr += 2;
+                grad_attn_weight += grad_weight_stride;
+                grad_sampling_loc += grad_loc_stride;
+            }
+        }
+    }
+}
+
+template <typename scalar_t>
+void ms_deformable_im2col_cuda(cudaStream_t stream, const scalar_t *data_value, const int64_t *data_spatial_shapes,
+                               const int64_t *data_level_start_index, const scalar_t *data_sampling_loc,
+                               const scalar_t *data_attn_weight, const int batch_size, const int spatial_size,
+                               const int num_heads, const int channels, const int num_levels, const int num_query,
+                               const int num_point, scalar_t *data_col) {
+    const int num_kernels = batch_size * num_query * num_heads * channels;
+    const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+    const int num_threads = CUDA_NUM_THREADS;
+    ms_deformable_im2col_gpu_kernel<scalar_t><<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+        num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight,
+        batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
+
+    cudaError_t err = cudaGetLastError();
+    if (err != cudaSuccess) {
+        printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+    }
+}
+
+template <typename scalar_t>
+void ms_deformable_col2im_cuda(cudaStream_t stream, const scalar_t *grad_col, const scalar_t *data_value,
+                               const int64_t *data_spatial_shapes, const int64_t *data_level_start_index,
+                               const scalar_t *data_sampling_loc, const scalar_t *data_attn_weight,
+                               const int batch_size, const int spatial_size, const int num_heads, const int channels,
+                               const int num_levels, const int num_query, const int num_point, scalar_t *grad_value,
+                               scalar_t *grad_sampling_loc, scalar_t *grad_attn_weight) {
+    const int num_threads = (channels > CUDA_NUM_THREADS) ? CUDA_NUM_THREADS : channels;
+    const int num_kernels = batch_size * num_query * num_heads * channels;
+    const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+    if (channels > 1024) {
+        if ((channels & 1023) == 0) {
+            ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
+                <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads * 3 * sizeof(scalar_t),
+                   stream>>>(num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                             data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels,
+                             num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+        } else {
+            ms_deformable_col2im_gpu_kernel_gm<scalar_t>
+                <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                    num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc,
+                    data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                    grad_value, grad_sampling_loc, grad_attn_weight);
+        }
+    } else {
+        switch (channels) {
+            case 1:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 2:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 4:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 8:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 16:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 32:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 64:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 128:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 256:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 512:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            case 1024:
+                ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
+                    <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, 0, stream>>>(
+                        num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                        data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels, num_levels,
+                        num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                break;
+            default:
+                if (channels < 64) {
+                    ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
+                        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads * 3 * sizeof(scalar_t),
+                           stream>>>(num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                                     data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels,
+                                     num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                } else {
+                    ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
+                        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads, num_threads * 3 * sizeof(scalar_t),
+                           stream>>>(num_kernels, grad_col, data_value, data_spatial_shapes, data_level_start_index,
+                                     data_sampling_loc, data_attn_weight, batch_size, spatial_size, num_heads, channels,
+                                     num_levels, num_query, num_point, grad_value, grad_sampling_loc, grad_attn_weight);
+                }
+        }
+    }
+    cudaError_t err = cudaGetLastError();
+    if (err != cudaSuccess) {
+        printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+    }
+}

third_party/vipe/csrc/grounding_dino_ext/vision.cpp

@@ -0,0 +1,33 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+#include "ms_deform_attn_cuda.h"
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_forward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                  const at::Tensor &level_start_index, const at::Tensor &sampling_loc,
+                                  const at::Tensor &attn_weight, const int im2col_step) {
+    if (value.is_cuda()) {
+        return ms_deform_attn_cuda_forward(value, spatial_shapes, level_start_index, sampling_loc, attn_weight,
+                                           im2col_step);
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector<at::Tensor> ms_deform_attn_backward(const at::Tensor &value, const at::Tensor &spatial_shapes,
+                                                const at::Tensor &level_start_index, const at::Tensor &sampling_loc,
+                                                const at::Tensor &attn_weight, const at::Tensor &grad_output,
+                                                const int im2col_step) {
+    if (value.is_cuda()) {
+        return ms_deform_attn_cuda_backward(value, spatial_shapes, level_start_index, sampling_loc, attn_weight,
+                                            grad_output, im2col_step);
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+}  // namespace groundingdino
+
+void pybind_grounding_dino_ext(py::module &m) {
+    m.def("ms_deform_attn_forward", &groundingdino::ms_deform_attn_forward, "ms_deform_attn_forward");
+    m.def("ms_deform_attn_backward", &groundingdino::ms_deform_attn_backward, "ms_deform_attn_backward");
+}