Method forward

pointops2/functions/pointops.py:448–482

input: q: (N, h, hdim), index_q: (M), k: (N, h, hdim), index_k: (M), table_q: (L, h, hdim, 3), table_k: (L, h, hdim, 3), rel_idx: (M, 3) output: output: [M, h]

(ctx, q, index_q_offsets, n_max, k, index_k, table_q, table_k, rel_idx)

class DotProdWithIdx_v3(Function):
    @staticmethod
    def forward(ctx, q, index_q_offsets, n_max, k, index_k, table_q, table_k, rel_idx):
        """
        input: q: (N, h, hdim), index_q: (M), k: (N, h, hdim), index_k: (M), table_q: (L, h, hdim, 3), table_k: (L, h, hdim, 3), rel_idx: (M, 3)
        output: output: [M, h]
        """
        assert q.is_contiguous() and index_q_offsets.is_contiguous() and k.is_contiguous() and index_k.is_contiguous() and table_q.is_contiguous() and table_k.is_contiguous() and rel_idx.is_contiguous()

        N, h, hdim = q.shape
        M = index_k.shape[0]
        L = table_q.shape[0]
        assert table_k.shape[0] == L

        # # obtain the mapping from block_idx to m_idx
        # rel_idx_merge = rel_idx[:, 0] + rel_idx[:, 1] * L + rel_idx[:, 2] * (L ** 2) #[M, ]
        # sorted_values, sort_indices = torch.sort(rel_idx_merge)
        # _, counts = torch.unique_consecutive(sorted_values, return_counts=True)
        # rel_idx_offsets = torch.cumsum(counts, dim=-1) #[T,]
        # rel_idx_offsets = torch.cat([torch.zeros(1, dtype=torch.long).cuda(), rel_idx_offsets], 0) #[T+1,]
        # n_max = counts.max()
        # T = counts.shape[0]

        # print("M: {}, L: {}, n_max: {}, T: {}".format(M, L, n_max, T))
        # print("rel_idx_merge.shape: {}, sorted_values.shape: {}".format(rel_idx_merge.shape, sorted_values.shape))
        # print("counts.shape: {}".format(counts.shape))

        # print("M: {}, L: {}, n_max: {}".format(M, L, n_max))

        output = torch.cuda.FloatTensor(M, h).zero_()
        # pointops_cuda.dot_prod_with_idx_forward_cuda(N, M, h, hdim, q, index, table, rel_idx, output)
        pointops_cuda.dot_prod_with_idx_forward_cuda_v3(N, M, h, hdim, n_max, q, index_q_offsets, k, index_k, table_q, table_k, rel_idx, output)
        
        ctx.n_max = n_max
        # ctx.T = T
        ctx.save_for_backward(q, index_q_offsets, k, index_k, table_q, table_k, rel_idx)
        return output

    @staticmethod
    def backward(ctx, grad_output):