// Use grid strided loops, described here:
// https://devblogs.nvidia.com/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/
// This pattern ensures that all of the loop values are visited once, no matter
// what grid parameters are used for the function.

// We cannot include CUDA header for mathematical constants, since it requires
// that the development headers of the CUDA toolkit are installed.

template <typename T>
struct Constants {};

template <>
struct Constants<double> {
    static constexpr double INV_SQRT_2 = 0.7071067811865475;
    static constexpr double INV_SQRT_2PI = 0.3989422804014327;
};

template <>
struct Constants<float> {
    static constexpr float INV_SQRT_2 = 0.70710677;
    static constexpr float INV_SQRT_2PI = 0.3989423;
};


template <typename U>
__global__ void gather_add(U* out_bo, const U* table_to, const int* indices_bk,
        int T, int O, int B, int K)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int b = _loop_start; b < B; b += _loop_stride) {
        for (int k = 0; k < K; ++k) {
            int idx = indices_bk[b * K + k];
            const U* table = table_to + idx * O;
            U* out = out_bo + b * O;
            for (int o = 0; o < O; ++o) {
                out[o] += table[o];
            }
        }
    }
}


template <typename T>
__global__ void seq2col(T* output, const T* X, const int* lengths,
        int nW, int B, int I, int nL)
{
    // Let's say nW is 1 (it usually is). Then we want to take:

    // 1a 1b 1c
    // 2a 2b 2c
    // 3a 3b 3c

    // And make

    // __ __ __ 1a 1b 1c 2a 2b 2c
    // 1a 1b 1c 2a 2b 2c 3a 3b 3c
    // 2a 2b 2c 3a 3b 3c __ __ __

    // Where __ is padding.

    // Now let's say nW is 2. Then we want to take:

    // 1a 1b 1c
    // 2a 2b 2c
    // 3a 3b 3c

    // And make

    // __ __ __ __ __ __ 1a 1b 1c 2a 2b 2c 3a 3b 3c
    // __ __ __ 1a 1b 1c 2a 2b 2c 3a 3b 3c __ __ __
    // 1a 1b 1c 2a 2b 2c 3a 3b 3c __ __ __ __ __ __

    // * x_start=-6, x_end=9 : (0-2) * 3, (0+2+1) * 3
    // * x_start=-3, x_end=13 : (1-2) * 3, (1+2+1) * 3
    // * x_start=0, x_end=16 : (2-2) * 3, (2+2+1) * 3
    //
    // If lengths > 1, then the sequence lengths dictate
    // the boundaries/padding rather than the begin/end
    // of X.
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    int nF = nW * 2 + 1;

    int seq = 0;
    int seq_start = 0;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Find sequence sequence in which b lies.
        for (; seq < nL; ++seq) {
            if (b < seq_start + lengths[seq]) {
                break;
            }
            seq_start += lengths[seq];
        }

        // Calculate the bounds of the sequence wherein b lies.
        int seq_end = seq_start + lengths[seq];

        // Find the unconstrained window around b, which
        // may be out of the sequence bounds.
        int window_start = b - nW;
        int window_end = b + nW + 1;

        // Find the sequence-constrained window around b.
        int x_start = max(seq_start, window_start);
        int x_end = min(seq_end, window_end);
        int n_elems = x_end - x_start;

        // If the left window is cut short, we want to start by
        // the same amount in the output.
        int out_offset = x_start - window_start;

        for (int i = 0; i < n_elems * I; i++) {
            output[(b * I * nF) + (out_offset * I) + i] =
                X[(x_start * I) + i];
        }
    }
}


template <typename T>
__global__ void pad(T* out, T const **seqs, int const *lengths, int stride, int N, int L)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < L * stride; i += _loop_stride) {
        for (int j = 0; j < N; ++j) {
            T const *seq = seqs[j];
            if (i < lengths[j] * stride) {
                out[j * L * stride + i] = seq[i];
            } else {
                out[j * L * stride + i] = T();
            }
        }
    }
}


template <typename T>
__global__ void maxout(T* best, int* which, const T* cands, int B, int O, int P)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int bo = _loop_start; bo < B * O; bo += _loop_stride)
    {
        // Go to the candidates at the output we're working on
        const T* cands_bo = &cands[bo * P];

        int best_idx = 0;
        T best_val = cands_bo[0];
        for (int p = 1; p < P; ++p)
        {
            if (cands_bo[p] > best_val) {
                best_idx = p;
                best_val = cands_bo[p];
            }
        }

        which[bo] = best_idx;
        best[bo] = best_val;
    }
}


template <typename T>
__global__ void clipped_linear(T* Y, const T* X, double slope, double offset, double min_val, double max_val, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T y = X[i] * slope + offset;
        Y[i] = min(max(y, min_val), max_val);
    }
}


template <typename T>
__global__ void dish(T* Y, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        Y[i] = 0.5 * x * (x / sqrt(1 + x * x) + 1);
    }
}


template <typename T>
__global__ void gelu(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        if (x >= threshold) {
            Y[i] = x;
        } else if (x <= -threshold) {
            Y[i] = 0.0;
        } else {
            T cdf = 0.5 * (1.0 + erf(Constants<T>::INV_SQRT_2 * x));
            Y[i] = x * cdf;
        }
    }
}


template <typename T>
__global__ void mish(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    T one = 1.;
    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] >= threshold)
            Y[i] = X[i];
        else
            Y[i] = X[i] * tanh(log(one + exp(X[i])));
    }
}


template <typename T>
__global__ void swish(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] >= threshold) {
            Y[i] = X[i];
        } else if (X[i] <= -threshold) {
            Y[i] = 0.0;
        } else {
            T logistic_cdf = 1.0 / (1.0 + exp(-X[i]));
            Y[i] = X[i] * logistic_cdf;
        }
    }
}


template <typename U>
__global__ void reduce_sum(U* output, const U* X,
    const int* lengths, int B, int T, int O)
{
    // Compute sums of a batch of concatenated sequences
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Go to the regions we're working on
	    U* output_b = &output[b*O];
        // Find the sequence item we're working on
	    int t = 0;
        for (int i=0; i < b; ++i) {
	        t += lengths[i];
        }
        int length = lengths[b];
        // Each invocation of the kernel sums one batch.
        for (int i=0; i < length; ++i) // Iterate over rows
        {
	        const U* X_t = &X[(t+i)*O];
            for (int j=0; j < O; ++j)
            {
              output_b[j] += X_t[j];
            }
        }
    }
}


template <typename U>
__global__ void reduce_max(U* maxes, int* which,
    const U* X, const int* lengths, int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {

        // Go to the regions we're working on
        U* maxes_b = &maxes[b*O];
        int* which_b = &which[b*O];
        // Find the sequence item we're working on
        const U* X_t = X;
        for (int i=0; i < b; ++i) {
	        X_t += lengths[i] * O;
        }
        // Each invocation of the kernel maxes one sequence.
        // Start by assuming maxes are the first element.
        for (int i=0; i < O; ++i) {
            maxes_b[i] = X_t[i];
            which_b[i] = 0;
        }
        int length = lengths[b];
        for (int i=1; i < length; ++i) // Iterate over rows
        {
            X_t += O;
            for (int j=0; j < O; ++j)
            {
                if (X_t[j] > maxes_b[j])
                {
                    maxes_b[j] = X_t[j];
                    which_b[j] = i;
                }
            }
        }

    }
}


template <typename T>
__global__ void backprop_seq2col(T* d_seqs, const T* d_cols, const int* lengths,
        int nW, int B, int I, int nL)
{
    // Here's what we're doing, if we had 2d indexing.
    //for i in range(B):
    //    d_seq[i] += d_cols[i-2, 4]
    //    d_seq[i] += d_cols[i-1, 3]
    //    d_seq[i] += d_cols[i, 2]
    //    d_seq[i] += d_cols[i+1, 1]
    //    d_seq[i] += d_cols[i+2, 0]

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    int nF = nW * 2 + 1;
    int seq = 0;
    int seq_start = 0;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Find sequence offset in which b lies.
        // Fixme: do not restart offset search for every b.
        for (; seq < nL; ++seq) {
            if (b < seq_start + lengths[seq]) {
                break;
            }
            seq_start += lengths[seq];
        }

        // Calculate the bounds of the sequence wherein b lies.
        int seq_end = seq_start + lengths[seq];

        // Find the unconstrained window around b, which
        // may be out of the sequence bounds.
        int window_start = b - nW;
        int window_end = b + nW + 1;

        // Find the sequence-constrained window around b.
        int d_seqs_start = max(seq_start, window_start);
        int d_seqs_end = min(seq_end, window_end);


        // The here update proceeds differently than the other seq2col
        // implementations. We have to do all the updates for the b in this loop
        // iteration, otherwise we get data races due to parallelism in CUDA.
        //
        // A batch item b occurs, given nw=1, in:
        //
        // position 0 in b - 1 (if present) <- window_start
        // position 1 in b
        // position 2 in b + 1 (if present) <- window_end
        //
        // The following loop sums the gradients for those occurrences.
        // b_w loops over [b - 1, b, b + 1] and computes the position
        // of b within the column gradients of [b - 1 ... b + 1].
        for (int b_w = d_seqs_start; b_w < d_seqs_end; ++b_w) {
            int position = (2 * nW) - (b_w - window_start);
            int start = (b_w * I * nF) + (position * I);
            for (int i = 0; i < I; ++i) {
                d_seqs[(b*I + i)] += d_cols[start + i];
            }
        }
    }
}


template <typename T>
__global__ void backprop_clipped_linear(T* dX, const T* dY, const T* X, double slope, double offset, double min_val, double max_val, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    T low = (min_val - offset) / slope;
    T high = (max_val - offset) / slope;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];

        if (low < x && x < high) {
            dX[i] = dY[i] * slope;
        } else {
            dX[i] = 0;
        }
    }
}


template <typename T>
__global__ void backprop_hard_swish(T* dX, const T* dY, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] > 2.5) {
            dX[i] = dY[i];
        } else if (X[i] < -2.5) {
            dX[i] = 0;
        } else {
            dX[i] = dY[i] * (X[i] * 0.4 + 0.5);
        }
    }
}


template <typename T>
__global__ void backprop_hard_swish_mobilenet(T* dX, const T* dY, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] > 3.0) {
            dX[i] = dY[i];
        } else if (X[i] < -3.0) {
            dX[i] = 0;
        } else {
            dX[i] = dY[i] * ((X[i] * 2.0 + 3.0) / 6.0);
        }
    }
}


template <typename T>
__global__ void backprop_dish(T* dX, const T* dY, const T* X, int N)
{

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        T x_sq = x * x;
        T x_sq_plus_one = x_sq + 1.0;
        dX[i] = dY[i] * (x/sqrt(x_sq_plus_one) - (0.5 * x * x_sq)
            / pow(x_sq_plus_one, static_cast<T>(1.5)) + 0.5);
    }
}


template <typename T>
__global__ void backprop_gelu(T* dX, const T* dY, const T* X,
    double threshold, int N)
{

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];

        if (x >= threshold) {
            dX[i] = dY[i];
        } else if (x <= -threshold) {
            dX[i] = 0.0;
        } else {
            T cdf = 0.5 * (1.0 + erf(Constants<T>::INV_SQRT_2 * x));
            T pdf = Constants<T>::INV_SQRT_2PI * exp(-0.5 * x * x);
            dX[i] = dY[i] * (cdf + x * pdf);
        }
    }
}


template <typename T>
__global__ void backprop_maxout(T* dX,
    const T* dY, const int* which, int B, int O, int P)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Go to the regions we're working on
        T* dX_b = &dX[b*O*P];
        const T* dY_b = &dY[b*O];
        const int* which_b = &which[b*O];
        for (int i=0; i < O; ++i)
            dX_b[(i*P)+which_b[i]] = dY_b[i];
    }
}


template <typename T>
__global__ void backprop_mish(T* dX,
    const T* dY, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        if (x >= threshold)
        {
            dX[i] = dY[i];
        } else
        {
            T exp_x = exp(x);
            T exp_2x = exp(2*x);
            T exp_3x = exp(3*x);

            T omega = (4. * (x+1)) + (4 * exp_2x) + exp_3x + exp_x * (4.*x+6);
            T delta = 2 * exp_x + exp_2x + 2;
            dX[i] = dY[i] * ((exp_x * omega) / (delta * delta));
        }
    }
}


template <typename T>
__global__ void backprop_swish(T* dX, const T* dY, const T* X,
    const T* Y, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        T y = Y[i];

        if (x >= threshold) {
            dX[i] = dY[i];
        } else if (x <= -threshold) {
            dX[i] = 0.0;
        } else {
            T cdf = 1.0 / (1 + exp(-x));
            T d = y + cdf * (1 - y);
            dX[i] = dY[i] * d;
        }
    }
}


template <typename U>
__global__ void backprop_reduce_sum(U* dX, const U* d_sum, const int* lengths,
    int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // Find the sequence item we're working on
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        for (int i=0; i < O; ++i)
        {
            dX[t * O + i] = d_sum[b * O + i];
        }
    }
}


template <typename U>
__global__ void backprop_reduce_mean(U* dX, const U* d_mean, const int* lengths,
    int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // Find the sequence item we're working on
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        U* dX_t = &dX[t * O];
        const U* d_mean_b = &d_mean[b * O];
        int lengths_b = lengths[b];
        for (int i=0; i < O; ++i)
        {
            dX_t[i] = d_mean_b[i] / lengths_b;
        }
    }
}


template <typename U>
__global__ void backprop_reduce_max(U* dX, const U* d_maxes,
    const int* which, const int* lengths, int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // We're calculating the gradient of the unpooled sequences, from
        // the gradient of the maxes. In this loop, we're getting the gradient
        // of a single sequence item, t. We need to know the sequence index,
        // b.
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        // The "which" array tells us which rows were selected as the max.
        // So we need to find the index of our t in the sequence.
        int index_of_t = t-seq_start;
        // Get the rows we're dealing with, to avoid cluttering the loop
        // with the index math.
        U* dX_t = &dX[t*O];
        const U* d_maxes_b = &d_maxes[b*O];
        const int* which_b = &which[b*O];
        // Now loop over our row.
        for (int i=0; i < O; ++i)
        {
            // If we used the value for this cell,
            // pass the gradient
            if (which_b[i] == index_of_t)
               dX_t[i] = d_maxes_b[i];
        }
    }
}

	.file	"tmpxft_00362e42_00000000-6__custom_kernels.cudafe1.cpp"
	.text
#APP

#NO_APP
	.type	_ZL26__cudaUnregisterBinaryUtilv, @function
_ZL26__cudaUnregisterBinaryUtilv:
.LFB2056:
	.cfi_startproc
	endbr64
	movq	_ZL20__cudaFatCubinHandle(%rip), %rdi
	jmp	__cudaUnregisterFatBinary@PLT
	.cfi_endproc
.LFE2056:
	.size	_ZL26__cudaUnregisterBinaryUtilv, .-_ZL26__cudaUnregisterBinaryUtilv
	.section	.text.startup,"ax",@progbits
	.type	_ZL24__sti____cudaRegisterAllv, @function
_ZL24__sti____cudaRegisterAllv:
.LFB2079:
	.cfi_startproc
	endbr64
	pushq	%rax
	.cfi_def_cfa_offset 16
	leaq	_ZL15__fatDeviceText(%rip), %rdi
	call	__cudaRegisterFatBinary@PLT
	movq	%rax, %rdi
	movq	%rax, _ZL20__cudaFatCubinHandle(%rip)
	call	__cudaRegisterFatBinaryEnd@PLT
	leaq	_ZL26__cudaUnregisterBinaryUtilv(%rip), %rdi
	popq	%rdx
	.cfi_def_cfa_offset 8
	jmp	atexit@PLT
	.cfi_endproc
.LFE2079:
	.size	_ZL24__sti____cudaRegisterAllv, .-_ZL24__sti____cudaRegisterAllv
	.section	.init_array,"aw"
	.align 8
	.quad	_ZL24__sti____cudaRegisterAllv
	.section	.nvFatBinSegment,"aw"
	.align 8
	.type	_ZL15__fatDeviceText, @object
	.size	_ZL15__fatDeviceText, 24
_ZL15__fatDeviceText:
	.long	1180844977
	.long	1
	.quad	fatbinData
	.quad	0
	.local	_ZL20__cudaFatCubinHandle
	.comm	_ZL20__cudaFatCubinHandle,8,8
	.ident	"GCC: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0"
	.section	.note.GNU-stack,"",@progbits
	.section	.note.gnu.property,"a"
	.align 8
	.long	1f - 0f
	.long	4f - 1f
	.long	5
0:
	.string	"GNU"
1:
	.align 8
	.long	0xc0000002
	.long	3f - 2f
2:
	.long	0x3
3:
	.align 8
4:


#include <hip/hip_runtime.h>
// Use grid strided loops, described here:
// https://devblogs.nvidia.com/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/
// This pattern ensures that all of the loop values are visited once, no matter
// what grid parameters are used for the function.

// We cannot include CUDA header for mathematical constants, since it requires
// that the development headers of the CUDA toolkit are installed.

template <typename T>
struct Constants {};

template <>
struct Constants<double> {
    static constexpr double INV_SQRT_2 = 0.7071067811865475;
    static constexpr double INV_SQRT_2PI = 0.3989422804014327;
};

template <>
struct Constants<float> {
    static constexpr float INV_SQRT_2 = 0.70710677;
    static constexpr float INV_SQRT_2PI = 0.3989423;
};


template <typename U>
__global__ void gather_add(U* out_bo, const U* table_to, const int* indices_bk,
        int T, int O, int B, int K)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int b = _loop_start; b < B; b += _loop_stride) {
        for (int k = 0; k < K; ++k) {
            int idx = indices_bk[b * K + k];
            const U* table = table_to + idx * O;
            U* out = out_bo + b * O;
            for (int o = 0; o < O; ++o) {
                out[o] += table[o];
            }
        }
    }
}


template <typename T>
__global__ void seq2col(T* output, const T* X, const int* lengths,
        int nW, int B, int I, int nL)
{
    // Let's say nW is 1 (it usually is). Then we want to take:

    // 1a 1b 1c
    // 2a 2b 2c
    // 3a 3b 3c

    // And make

    // __ __ __ 1a 1b 1c 2a 2b 2c
    // 1a 1b 1c 2a 2b 2c 3a 3b 3c
    // 2a 2b 2c 3a 3b 3c __ __ __

    // Where __ is padding.

    // Now let's say nW is 2. Then we want to take:

    // 1a 1b 1c
    // 2a 2b 2c
    // 3a 3b 3c

    // And make

    // __ __ __ __ __ __ 1a 1b 1c 2a 2b 2c 3a 3b 3c
    // __ __ __ 1a 1b 1c 2a 2b 2c 3a 3b 3c __ __ __
    // 1a 1b 1c 2a 2b 2c 3a 3b 3c __ __ __ __ __ __

    // * x_start=-6, x_end=9 : (0-2) * 3, (0+2+1) * 3
    // * x_start=-3, x_end=13 : (1-2) * 3, (1+2+1) * 3
    // * x_start=0, x_end=16 : (2-2) * 3, (2+2+1) * 3
    //
    // If lengths > 1, then the sequence lengths dictate
    // the boundaries/padding rather than the begin/end
    // of X.
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    int nF = nW * 2 + 1;

    int seq = 0;
    int seq_start = 0;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Find sequence sequence in which b lies.
        for (; seq < nL; ++seq) {
            if (b < seq_start + lengths[seq]) {
                break;
            }
            seq_start += lengths[seq];
        }

        // Calculate the bounds of the sequence wherein b lies.
        int seq_end = seq_start + lengths[seq];

        // Find the unconstrained window around b, which
        // may be out of the sequence bounds.
        int window_start = b - nW;
        int window_end = b + nW + 1;

        // Find the sequence-constrained window around b.
        int x_start = max(seq_start, window_start);
        int x_end = min(seq_end, window_end);
        int n_elems = x_end - x_start;

        // If the left window is cut short, we want to start by
        // the same amount in the output.
        int out_offset = x_start - window_start;

        for (int i = 0; i < n_elems * I; i++) {
            output[(b * I * nF) + (out_offset * I) + i] =
                X[(x_start * I) + i];
        }
    }
}


template <typename T>
__global__ void pad(T* out, T const **seqs, int const *lengths, int stride, int N, int L)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < L * stride; i += _loop_stride) {
        for (int j = 0; j < N; ++j) {
            T const *seq = seqs[j];
            if (i < lengths[j] * stride) {
                out[j * L * stride + i] = seq[i];
            } else {
                out[j * L * stride + i] = T();
            }
        }
    }
}


template <typename T>
__global__ void maxout(T* best, int* which, const T* cands, int B, int O, int P)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int bo = _loop_start; bo < B * O; bo += _loop_stride)
    {
        // Go to the candidates at the output we're working on
        const T* cands_bo = &cands[bo * P];

        int best_idx = 0;
        T best_val = cands_bo[0];
        for (int p = 1; p < P; ++p)
        {
            if (cands_bo[p] > best_val) {
                best_idx = p;
                best_val = cands_bo[p];
            }
        }

        which[bo] = best_idx;
        best[bo] = best_val;
    }
}


template <typename T>
__global__ void clipped_linear(T* Y, const T* X, double slope, double offset, double min_val, double max_val, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T y = X[i] * slope + offset;
        Y[i] = min(max(y, min_val), max_val);
    }
}


template <typename T>
__global__ void dish(T* Y, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        Y[i] = 0.5 * x * (x / sqrt(1 + x * x) + 1);
    }
}


template <typename T>
__global__ void gelu(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        if (x >= threshold) {
            Y[i] = x;
        } else if (x <= -threshold) {
            Y[i] = 0.0;
        } else {
            T cdf = 0.5 * (1.0 + erf(Constants<T>::INV_SQRT_2 * x));
            Y[i] = x * cdf;
        }
    }
}


template <typename T>
__global__ void mish(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    T one = 1.;
    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] >= threshold)
            Y[i] = X[i];
        else
            Y[i] = X[i] * tanh(log(one + exp(X[i])));
    }
}


template <typename T>
__global__ void swish(T* Y, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] >= threshold) {
            Y[i] = X[i];
        } else if (X[i] <= -threshold) {
            Y[i] = 0.0;
        } else {
            T logistic_cdf = 1.0 / (1.0 + exp(-X[i]));
            Y[i] = X[i] * logistic_cdf;
        }
    }
}


template <typename U>
__global__ void reduce_sum(U* output, const U* X,
    const int* lengths, int B, int T, int O)
{
    // Compute sums of a batch of concatenated sequences
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Go to the regions we're working on
	    U* output_b = &output[b*O];
        // Find the sequence item we're working on
	    int t = 0;
        for (int i=0; i < b; ++i) {
	        t += lengths[i];
        }
        int length = lengths[b];
        // Each invocation of the kernel sums one batch.
        for (int i=0; i < length; ++i) // Iterate over rows
        {
	        const U* X_t = &X[(t+i)*O];
            for (int j=0; j < O; ++j)
            {
              output_b[j] += X_t[j];
            }
        }
    }
}


template <typename U>
__global__ void reduce_max(U* maxes, int* which,
    const U* X, const int* lengths, int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {

        // Go to the regions we're working on
        U* maxes_b = &maxes[b*O];
        int* which_b = &which[b*O];
        // Find the sequence item we're working on
        const U* X_t = X;
        for (int i=0; i < b; ++i) {
	        X_t += lengths[i] * O;
        }
        // Each invocation of the kernel maxes one sequence.
        // Start by assuming maxes are the first element.
        for (int i=0; i < O; ++i) {
            maxes_b[i] = X_t[i];
            which_b[i] = 0;
        }
        int length = lengths[b];
        for (int i=1; i < length; ++i) // Iterate over rows
        {
            X_t += O;
            for (int j=0; j < O; ++j)
            {
                if (X_t[j] > maxes_b[j])
                {
                    maxes_b[j] = X_t[j];
                    which_b[j] = i;
                }
            }
        }

    }
}


template <typename T>
__global__ void backprop_seq2col(T* d_seqs, const T* d_cols, const int* lengths,
        int nW, int B, int I, int nL)
{
    // Here's what we're doing, if we had 2d indexing.
    //for i in range(B):
    //    d_seq[i] += d_cols[i-2, 4]
    //    d_seq[i] += d_cols[i-1, 3]
    //    d_seq[i] += d_cols[i, 2]
    //    d_seq[i] += d_cols[i+1, 1]
    //    d_seq[i] += d_cols[i+2, 0]

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    int nF = nW * 2 + 1;
    int seq = 0;
    int seq_start = 0;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Find sequence offset in which b lies.
        // Fixme: do not restart offset search for every b.
        for (; seq < nL; ++seq) {
            if (b < seq_start + lengths[seq]) {
                break;
            }
            seq_start += lengths[seq];
        }

        // Calculate the bounds of the sequence wherein b lies.
        int seq_end = seq_start + lengths[seq];

        // Find the unconstrained window around b, which
        // may be out of the sequence bounds.
        int window_start = b - nW;
        int window_end = b + nW + 1;

        // Find the sequence-constrained window around b.
        int d_seqs_start = max(seq_start, window_start);
        int d_seqs_end = min(seq_end, window_end);


        // The here update proceeds differently than the other seq2col
        // implementations. We have to do all the updates for the b in this loop
        // iteration, otherwise we get data races due to parallelism in CUDA.
        //
        // A batch item b occurs, given nw=1, in:
        //
        // position 0 in b - 1 (if present) <- window_start
        // position 1 in b
        // position 2 in b + 1 (if present) <- window_end
        //
        // The following loop sums the gradients for those occurrences.
        // b_w loops over [b - 1, b, b + 1] and computes the position
        // of b within the column gradients of [b - 1 ... b + 1].
        for (int b_w = d_seqs_start; b_w < d_seqs_end; ++b_w) {
            int position = (2 * nW) - (b_w - window_start);
            int start = (b_w * I * nF) + (position * I);
            for (int i = 0; i < I; ++i) {
                d_seqs[(b*I + i)] += d_cols[start + i];
            }
        }
    }
}


template <typename T>
__global__ void backprop_clipped_linear(T* dX, const T* dY, const T* X, double slope, double offset, double min_val, double max_val, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    T low = (min_val - offset) / slope;
    T high = (max_val - offset) / slope;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];

        if (low < x && x < high) {
            dX[i] = dY[i] * slope;
        } else {
            dX[i] = 0;
        }
    }
}


template <typename T>
__global__ void backprop_hard_swish(T* dX, const T* dY, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] > 2.5) {
            dX[i] = dY[i];
        } else if (X[i] < -2.5) {
            dX[i] = 0;
        } else {
            dX[i] = dY[i] * (X[i] * 0.4 + 0.5);
        }
    }
}


template <typename T>
__global__ void backprop_hard_swish_mobilenet(T* dX, const T* dY, const T* X, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        if (X[i] > 3.0) {
            dX[i] = dY[i];
        } else if (X[i] < -3.0) {
            dX[i] = 0;
        } else {
            dX[i] = dY[i] * ((X[i] * 2.0 + 3.0) / 6.0);
        }
    }
}


template <typename T>
__global__ void backprop_dish(T* dX, const T* dY, const T* X, int N)
{

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        T x_sq = x * x;
        T x_sq_plus_one = x_sq + 1.0;
        dX[i] = dY[i] * (x/sqrt(x_sq_plus_one) - (0.5 * x * x_sq)
            / pow(x_sq_plus_one, static_cast<T>(1.5)) + 0.5);
    }
}


template <typename T>
__global__ void backprop_gelu(T* dX, const T* dY, const T* X,
    double threshold, int N)
{

    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];

        if (x >= threshold) {
            dX[i] = dY[i];
        } else if (x <= -threshold) {
            dX[i] = 0.0;
        } else {
            T cdf = 0.5 * (1.0 + erf(Constants<T>::INV_SQRT_2 * x));
            T pdf = Constants<T>::INV_SQRT_2PI * exp(-0.5 * x * x);
            dX[i] = dY[i] * (cdf + x * pdf);
        }
    }
}


template <typename T>
__global__ void backprop_maxout(T* dX,
    const T* dY, const int* which, int B, int O, int P)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int b = _loop_start; b < B; b += _loop_stride)
    {
        // Go to the regions we're working on
        T* dX_b = &dX[b*O*P];
        const T* dY_b = &dY[b*O];
        const int* which_b = &which[b*O];
        for (int i=0; i < O; ++i)
            dX_b[(i*P)+which_b[i]] = dY_b[i];
    }
}


template <typename T>
__global__ void backprop_mish(T* dX,
    const T* dY, const T* X, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        if (x >= threshold)
        {
            dX[i] = dY[i];
        } else
        {
            T exp_x = exp(x);
            T exp_2x = exp(2*x);
            T exp_3x = exp(3*x);

            T omega = (4. * (x+1)) + (4 * exp_2x) + exp_3x + exp_x * (4.*x+6);
            T delta = 2 * exp_x + exp_2x + 2;
            dX[i] = dY[i] * ((exp_x * omega) / (delta * delta));
        }
    }
}


template <typename T>
__global__ void backprop_swish(T* dX, const T* dY, const T* X,
    const T* Y, double threshold, int N)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;

    for (int i = _loop_start; i < N; i += _loop_stride)
    {
        T x = X[i];
        T y = Y[i];

        if (x >= threshold) {
            dX[i] = dY[i];
        } else if (x <= -threshold) {
            dX[i] = 0.0;
        } else {
            T cdf = 1.0 / (1 + exp(-x));
            T d = y + cdf * (1 - y);
            dX[i] = dY[i] * d;
        }
    }
}


template <typename U>
__global__ void backprop_reduce_sum(U* dX, const U* d_sum, const int* lengths,
    int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // Find the sequence item we're working on
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        for (int i=0; i < O; ++i)
        {
            dX[t * O + i] = d_sum[b * O + i];
        }
    }
}


template <typename U>
__global__ void backprop_reduce_mean(U* dX, const U* d_mean, const int* lengths,
    int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // Find the sequence item we're working on
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        U* dX_t = &dX[t * O];
        const U* d_mean_b = &d_mean[b * O];
        int lengths_b = lengths[b];
        for (int i=0; i < O; ++i)
        {
            dX_t[i] = d_mean_b[i] / lengths_b;
        }
    }
}


template <typename U>
__global__ void backprop_reduce_max(U* dX, const U* d_maxes,
    const int* which, const int* lengths, int B, int T, int O)
{
    int _loop_start = blockIdx.x * blockDim.x + threadIdx.x;
    int _loop_stride = blockDim.x * gridDim.x;
    int seq_start = 0;
    int b = 0;
    for (int t = _loop_start; t < T; t += _loop_stride)
    {
        // We're calculating the gradient of the unpooled sequences, from
        // the gradient of the maxes. In this loop, we're getting the gradient
        // of a single sequence item, t. We need to know the sequence index,
        // b.
        while ((b < B) && (seq_start+lengths[b]) <= t)
        {
           seq_start += lengths[b];
           b += 1;
        }
        if (lengths[b] == 0)
            continue;

        // The "which" array tells us which rows were selected as the max.
        // So we need to find the index of our t in the sequence.
        int index_of_t = t-seq_start;
        // Get the rows we're dealing with, to avoid cluttering the loop
        // with the index math.
        U* dX_t = &dX[t*O];
        const U* d_maxes_b = &d_maxes[b*O];
        const int* which_b = &which[b*O];
        // Now loop over our row.
        for (int i=0; i < O; ++i)
        {
            // If we used the value for this cell,
            // pass the gradient
            if (which_b[i] == index_of_t)
               dX_t[i] = d_maxes_b[i];
        }
    }
}

	.text
	.file	"_custom_kernels.hip"
	.type	__hip_cuid_,@object             # @__hip_cuid_
	.bss
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               # 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_

	.text
	.p2alignl 7, 3214868480
	.fill 96, 4, 3214868480
	.type	__hip_cuid_,@object             ; @__hip_cuid_
	.section	.bss,"aw",@nobits
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               ; 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_
	.amdgcn_target "amdgcn-amd-amdhsa--gfx1100"
	.amdgpu_metadata
---
amdhsa.kernels:  []
amdhsa.target:   amdgcn-amd-amdhsa--gfx1100
amdhsa.version:
  - 1
  - 2
...

	.end_amdgpu_metadata
