Repository: ggerganov/llama.cpp
File: ggml/src/ggml-cpu/repack.cpp (C++)

Code before:
            nchunk = nth;
        }

        // Ensure nchunk doesn't exceed the number of rows divided by minimum chunk size
        // This prevents creating too many tiny chunks that could overlap after alignment
        const int64_t max_nchunk = (nr + min_chunk_size - 1) / min_chunk_size;
        if (nchunk > max_nchunk) {
            nchunk = max_nchunk;
        }

        if (ith == 0) {
            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
            ggml_threadpool_chunk_set(params->threadpool, nth);
        }

        ggml_barrier(params->threadpool);

        // The first chunk comes from our thread_id, the rest will get auto-assigned.
        int current_chunk = ith;

        while (current_chunk < nchunk) {
            int64_t src0_start = (current_chunk * ne01) / nchunk;
            int64_t src0_end   = ((current_chunk + 1) * ne01) / nchunk;
            
            // Align boundaries to NB_COLS - round up to ensure all data is included
            // The chunk size limiting above ensures chunks are large enough to prevent overlaps
            src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
            src0_end   = (src0_end   % NB_COLS) ? src0_end   + NB_COLS - (src0_end   % NB_COLS) : src0_end;
            if (src0_end > ne01) {
                src0_end = ne01;
            }
            
            if (src0_start >= src0_end) {
                break;
            }

            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end);

            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
        }
    }

    void forward_mul_mat_id(ggml_compute_params * params, ggml_tensor * op) {
        const ggml_tensor * src0 = op->src[0];
        const ggml_tensor * src1 = op->src[1];
        const ggml_tensor * ids  = op->src[2];
        ggml_tensor *       dst  = op;

        GGML_TENSOR_BINARY_OP_LOCALS

        const int ith = params->ith;

Review comment (suggestion):
```suggestion

            // Align boundaries to NB_COLS - round up to ensure all data is included
            // The chunk size limiting above ensures chunks are large enough to prevent overlaps
```

Code after:
            nchunk = nth;
        }

        // Ensure nchunk doesn't exceed the number of rows divided by minimum chunk size
        // This prevents creating too many tiny chunks that could overlap after alignment
        const int64_t max_nchunk = (nr + min_chunk_size - 1) / min_chunk_size;
        if (nchunk > max_nchunk) {
            nchunk = max_nchunk;
        }

        if (ith == 0) {
            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
            ggml_threadpool_chunk_set(params->threadpool, nth);
        }

        ggml_barrier(params->threadpool);

        // The first chunk comes from our thread_id, the rest will get auto-assigned.
        int current_chunk = ith;

        while (current_chunk < nchunk) {
            int64_t src0_start = (current_chunk * ne01) / nchunk;
            int64_t src0_end   = ((current_chunk + 1) * ne01) / nchunk;

            // Align boundaries to NB_COLS - round up to ensure all data is included
            // The chunk size limiting above ensures chunks are large enough to prevent overlaps
            src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
            src0_end   = (src0_end   % NB_COLS) ? src0_end   + NB_COLS - (src0_end   % NB_COLS) : src0_end;
            if (src0_end > ne01) {
                src0_end = ne01;
            }

            if (src0_start >= src0_end) {
                break;
            }

            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end);

            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
        }
    }

    void forward_mul_mat_id(ggml_compute_params * params, ggml_tensor * op) {
        const ggml_tensor * src0 = op->src[0];
        const ggml_tensor * src1 = op->src[1];
        const ggml_tensor * ids  = op->src[2];
        ggml_tensor *       dst  = op;

        GGML_TENSOR_BINARY_OP_LOCALS

        const int ith = params->ith;