CUDA: fix get_rows_back for tables with more than 65535 rows (grid-y clamp + stride) (#25103)
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@@ -78,26 +78,29 @@ static __global__ void k_get_rows_float(
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template<typename grad_t, typename dst_t>
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static __global__ void k_get_rows_back_float(
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const grad_t * __restrict__ grad, const int32_t * __restrict__ rows, dst_t * __restrict__ dst, const int64_t ncols, const int64_t nrows_grad) {
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const grad_t * __restrict__ grad, const int32_t * __restrict__ rows, dst_t * __restrict__ dst,
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const int64_t ncols, const int64_t nrows_grad, const int64_t nrows_dst) {
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const int col = blockIdx.x*blockDim.x + threadIdx.x;
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if (col >= ncols) {
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return;
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}
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const int dst_row = blockIdx.y*blockDim.y + threadIdx.y;
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float sum = 0.0f;
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ggml_cuda_pdl_sync();
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for (int64_t i = 0; i < nrows_grad; ++i) {
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if (rows[i] != dst_row) {
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continue;
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}
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sum += grad[i*ncols + col];
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}
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dst[dst_row*ncols + col] = sum;
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// grid.y is clamped to the CUDA grid limit, so stride over the destination rows
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for (int64_t dst_row = blockIdx.y; dst_row < nrows_dst; dst_row += gridDim.y) {
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float sum = 0.0f;
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for (int64_t i = 0; i < nrows_grad; ++i) {
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if (rows[i] != dst_row) {
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continue;
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}
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sum += grad[i*ncols + col];
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}
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dst[dst_row*ncols + col] = sum;
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}
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}
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template<int qk, int qr, dequantize_kernel_t dq, typename dst_t>
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@@ -302,7 +305,7 @@ void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * d
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const dim3 block_dims(CUDA_GET_ROWS_BACK_BLOCK_SIZE, 1, 1);
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const int block_num_x = (ne00 + CUDA_GET_ROWS_BACK_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BACK_BLOCK_SIZE;
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const dim3 block_nums(block_num_x, ne1, 1);
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const dim3 block_nums(block_num_x, MIN(ne1, (int64_t)UINT16_MAX), 1);
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k_get_rows_back_float<<<block_nums, block_dims, 0, stream>>>(src0_d, src1_d, dst_d, ne00, ne10);
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k_get_rows_back_float<<<block_nums, block_dims, 0, stream>>>(src0_d, src1_d, dst_d, ne00, ne10, ne1);
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}
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