dtw_loss_functions.soft_dtw_implementations.soft_dtw_cuda_ron.cuda.kernels — dtw_loss_functions documentation

# Source code for dtw_loss_functions.soft_dtw_implementations.soft_dtw_cuda_ron.cuda.kernels

    from __future__ import annotations

    import math
    from numba import cuda

    @cuda.jit
    def softdtw_forward_diag_sqeuclid_cuda(X, Y, R, gamma, bandwidth, N, M, D, p):
        b = cuda.blockIdx.y
        t = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

        i_min = max(0, p - (M - 1))
        i_max = min(N - 1, p)
        diag_len = i_max - i_min + 1
        if t >= diag_len:
            return

        i = i_min + t
        j = p - i

        ip = i + 1
        jp = j + 1

        if bandwidth > 0 and abs(i - j) > bandwidth:
            return

        # cost = ||X[b,i,:] - Y[b,j,:]||^2
        cost = 0.0
        for k in range(D):
            diff = X[b, i, k] - Y[b, j, k]
            cost += diff * diff

        inv_gamma = 1.0 / gamma

        r0 = -R[b, ip - 1, jp - 1] * inv_gamma
        r1 = -R[b, ip - 1, jp]     * inv_gamma
        r2 = -R[b, ip,     jp - 1] * inv_gamma

        rmax = r0
        if r1 > rmax: rmax = r1
        if r2 > rmax: rmax = r2

        rsum = math.exp(r0 - rmax) + math.exp(r1 - rmax) + math.exp(r2 - rmax)
        softmin = -gamma * (math.log(rsum) + rmax)

        R[b, ip, jp] = cost + softmin

    @cuda.jit
    def softdtw_backward_log_diag_sqeuclid_cuda(X, Y, R, logE, inv_gamma, bandwidth, N, M, D, p):
        b = cuda.blockIdx.y
        t = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

        i_min = max(0, p - (M - 1))
        i_max = min(N - 1, p)
        diag_len = i_max - i_min + 1
        if t >= diag_len:
            return

        i = i_min + t
        j = p - i

        ip = i + 1
        jp = j + 1

        if bandwidth > 0 and abs(i - j) > bandwidth:
            return

        Rij = R[b, ip, jp]
        if math.isinf(Rij):
            Rij = -math.inf

        # costs for transitions:
        # D_pad[ip+1, jp]   corresponds to ||X[i+1] - Y[j]||^2
        # D_pad[ip, jp+1]   corresponds to ||X[i]   - Y[j+1]||^2
        # D_pad[ip+1, jp+1] corresponds to ||X[i+1] - Y[j+1]||^2

        # cost_down: (i+1, j)
        cost_down = 0.0
        if i + 1 < N:
            for k in range(D):
                diff = X[b, i + 1, k] - Y[b, j, k]
                cost_down += diff * diff
        else:
            # this state will be invalid anyway due to R boundary = -inf
            cost_down = 0.0

        # cost_right: (i, j+1)
        cost_right = 0.0
        if j + 1 < M:
            for k in range(D):
                diff = X[b, i, k] - Y[b, j + 1, k]
                cost_right += diff * diff
        else:
            cost_right = 0.0

        # cost_diag: (i+1, j+1)
        cost_diag = 0.0
        if (i + 1 < N) and (j + 1 < M):
            for k in range(D):
                diff = X[b, i + 1, k] - Y[b, j + 1, k]
                cost_diag += diff * diff
        else:
            cost_diag = 0.0

        la = (R[b, ip + 1, jp]     - Rij - cost_down)  * inv_gamma
        lb = (R[b, ip,     jp + 1] - Rij - cost_right) * inv_gamma
        lc = (R[b, ip + 1, jp + 1] - Rij - cost_diag)  * inv_gamma

        t1 = logE[b, ip + 1, jp]     + la
        t2 = logE[b, ip,     jp + 1] + lb
        t3 = logE[b, ip + 1, jp + 1] + lc

        # reuse your helper if you want (recommended):
        m = t1
        if t2 > m: m = t2
        if t3 > m: m = t3

        if m == -math.inf:
            logE[b, ip, jp] = -math.inf
        else:
            logE[b, ip, jp] = m + math.log(math.exp(t1 - m) + math.exp(t2 - m) + math.exp(t3 - m))

    @cuda.jit
    def softdtw_forward_kernel(D, gamma, bandwidth, max_i, max_j, n_passes, R):
        b = cuda.blockIdx.x
        tid = cuda.threadIdx.x

        I = tid
        inv_gamma = 1.0 / gamma

        for p in range(n_passes):
            J = max(0, min(p - tid, max_j - 1))

            i = I + 1
            j = J + 1

            if I + J == p and (I < max_i and J < max_j):
                if not (abs(i - j) > bandwidth > 0):
                    r0 = -R[b, i - 1, j - 1] * inv_gamma
                    r1 = -R[b, i - 1, j] * inv_gamma
                    r2 = -R[b, i, j - 1] * inv_gamma
                    rmax = max(max(r0, r1), r2)
                    rsum = math.exp(r0 - rmax) + math.exp(r1 - rmax) + math.exp(r2 - rmax)
                    softmin = -gamma * (math.log(rsum) + rmax)
                    R[b, i, j] = D[b, i - 1, j - 1] + softmin
            cuda.syncthreads()

    @cuda.jit
    def softdtw_forward_diag_cuda(D, R, gamma, bandwidth, N, M, p):
        b = cuda.blockIdx.y  # batch in Y
        t = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

        # diagonal bounds in unpadded coordinates
        i_min = max(0, p - (M - 1))
        i_max = min(N - 1, p)

        diag_len = i_max - i_min + 1
        if t >= diag_len:
            return

        i = i_min + t
        j = p - i

        ip = i + 1
        jp = j + 1

        # bandwidth pruning (in padded coords uses ip/jp, but difference same)
        if bandwidth > 0 and abs(ip - jp) > bandwidth:
            return

        inv_gamma = 1.0 / gamma

        r0 = -R[b, ip - 1, jp - 1] * inv_gamma
        r1 = -R[b, ip - 1, jp]     * inv_gamma
        r2 = -R[b, ip,     jp - 1] * inv_gamma

        rmax = r0
        if r1 > rmax: rmax = r1
        if r2 > rmax: rmax = r2

        rsum = math.exp(r0 - rmax) + math.exp(r1 - rmax) + math.exp(r2 - rmax)
        softmin = -gamma * (math.log(rsum) + rmax)

        R[b, ip, jp] = D[b, i, j] + softmin

    @cuda.jit
    def softdtw_backward_kernel_legacy(D_pad, R, inv_gamma, bandwidth, max_i, max_j, n_passes, E):
        b = cuda.blockIdx.x
        tid = cuda.threadIdx.x
        I = tid

        for p in range(n_passes):
            rev_p = n_passes - p - 1
            J = max(0, min(rev_p - tid, max_j - 1))

            i = I + 1
            j = J + 1

            if I + J == rev_p and (I < max_i and J < max_j):
                if math.isinf(R[b, i, j]):
                    R[b, i, j] = -math.inf

                if not (abs(i - j) > bandwidth > 0):
                    # NOTE: this is the baseline (numerically unsafe). We'll replace with stabilized/log-space soon.
                    a = math.exp((R[b, i + 1, j] - R[b, i, j] - D_pad[b, i + 1, j]) * inv_gamma)
                    bb = math.exp((R[b, i, j + 1] - R[b, i, j] - D_pad[b, i, j + 1]) * inv_gamma)
                    c = math.exp((R[b, i + 1, j + 1] - R[b, i, j] - D_pad[b, i + 1, j + 1]) * inv_gamma)
                    E[b, i, j] = E[b, i + 1, j] * a + E[b, i, j + 1] * bb + E[b, i + 1, j + 1] * c
            cuda.syncthreads()

    @cuda.jit(device=True, inline=True)
    def _logsumexp3(a, b, c):
        m = a
        if b > m: m = b
        if c > m: m = c
        if m == -math.inf:
            return -math.inf
        return m + math.log(math.exp(a - m) + math.exp(b - m) + math.exp(c - m))

    @cuda.jit
    def softdtw_backward_log_cuda(D, R, inv_gamma, bandwidth, max_i, max_j, n_passes, logE):
        """
        D: (B, N+2, M+2) padded
        R: (B, N+2, M+2) padded (with boundary conditions already set)
        logE: (B, N+2, M+2) padded, initialized to -inf with logE[:,-1,-1]=0
        """
        k = cuda.blockIdx.x
        tid = cuda.threadIdx.x

        I = tid

        for p in range(n_passes):
            rev_p = n_passes - p - 1
            J = max(0, min(rev_p - tid, max_j - 1))

            i = I + 1
            j = J + 1

            if I + J == rev_p and (I < max_i and J < max_j):

                # pruning
                if not (abs(i - j) > bandwidth > 0):

                    Rij = R[k, i, j]
                    if math.isinf(Rij):
                        Rij = -math.inf

                    # log transition weights (no exp here!)
                    la = (R[k, i + 1, j]     - Rij - D[k, i + 1, j])     * inv_gamma
                    lb = (R[k, i, j + 1]     - Rij - D[k, i, j + 1])     * inv_gamma
                    lc = (R[k, i + 1, j + 1] - Rij - D[k, i + 1, j + 1]) * inv_gamma

                    t1 = logE[k, i + 1, j]     + la
                    t2 = logE[k, i, j + 1]     + lb
                    t3 = logE[k, i + 1, j + 1] + lc

                    logE[k, i, j] = _logsumexp3(t1, t2, t3)

            cuda.syncthreads()

    @cuda.jit
    def softdtw_backward_log_diag_cuda(Dp, R, logE, inv_gamma, bandwidth, N, M, p):
        b = cuda.blockIdx.y
        t = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

        i_min = max(0, p - (M - 1))
        i_max = min(N - 1, p)
        diag_len = i_max - i_min + 1
        if t >= diag_len:
            return

        i = i_min + t
        j = p - i

        ip = i + 1
        jp = j + 1

        # pruning
        if bandwidth > 0 and abs(i - j) > bandwidth:
            return

        Rij = R[b, ip, jp]
        if math.isinf(Rij):
            Rij = -math.inf

        la = (R[b, ip + 1, jp]     - Rij - Dp[b, ip + 1, jp])     * inv_gamma
        lb = (R[b, ip, jp + 1]     - Rij - Dp[b, ip, jp + 1])     * inv_gamma
        lc = (R[b, ip + 1, jp + 1] - Rij - Dp[b, ip + 1, jp + 1]) * inv_gamma

        t1 = logE[b, ip + 1, jp]     + la
        t2 = logE[b, ip, jp + 1]     + lb
        t3 = logE[b, ip + 1, jp + 1] + lc

        m = t1
        if t2 > m: m = t2
        if t3 > m: m = t3

        if m == -math.inf:
            logE[b, ip, jp] = -math.inf
        else:
            logE[b, ip, jp] = _logsumexp3(t1, t2, t3)