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add count_indices for cuarray add CUDA kernel for divide_by_counts! add NNlib.∇scatter_src for cuda gradient support scatter mean AD for CUDA support scatter *,/ AD for CUDA
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| function NNlib.count_indices(idx::AnyCuArray) | ||
| dst_counts = length.(NNlib.reverse_indices(idx)) | ||
| src_counts = NNlib.gather(cu(dst_counts), idx) | ||
| return src_counts | ||
| end | ||
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| function divide_kernel!(xs, ys, max_idx) | ||
| index = threadIdx().x + (blockIdx().x - 1) * blockDim().x | ||
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| @inbounds if index <= max_idx | ||
| xs[index] = xs[index] / ys[index] | ||
| end | ||
| return nothing | ||
| end | ||
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| function divide_kernel!(xs, counts, max_idx, max_dims_idx, dims_size) | ||
| index = threadIdx().x + (blockIdx().x - 1) * blockDim().x | ||
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| @inbounds if index <= max_idx | ||
| j, k = divrem(index-1, max_dims_idx) | ||
| dims_i = Tuple(CartesianIndices(dims_size)[k+1]) | ||
| @atomic xs[dims_i..., j+1] = xs[dims_i..., j+1] / counts[j+1] | ||
| end | ||
| return nothing | ||
| end | ||
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| function NNlib.divide_by_counts!(xs::AnyCuArray{T}, idx::AnyCuArray, dims) where {T} | ||
| counts = CuArray{T}(NNlib.count_indices(idx)) | ||
| args = if dims == 0 | ||
| max_idx = length(idx) | ||
| xs, counts, max_idx | ||
| else | ||
| dims_size = size(xs)[1:dims] | ||
| max_dims_idx = prod(dims_size) | ||
| max_idx = prod(size(xs)) | ||
| xs, counts, max_idx, max_dims_idx, dims_size | ||
| end | ||
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| kernel = @cuda launch=false divide_kernel!(args...) | ||
| config = launch_configuration(kernel.fun; max_threads=256) | ||
| threads = min(max_idx, config.threads) | ||
| blocks = cld(max_idx, threads) | ||
| kernel(args...; threads=threads, blocks=blocks) | ||
| return xs | ||
| end | ||
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| function NNlib.reverse_indices(idx::AnyCuArray{<:Any,N}) where N | ||
| max_dims = maximum_dims(idx) | ||
| T = CartesianIndex{N} | ||
| rev = Array{Vector{T}}(undef, max_dims...) | ||
| for i in eachindex(rev) | ||
| rev[i] = T[] | ||
| end | ||
| NNlib.reverse_indices!(rev, idx) | ||
| return map(cu, rev) | ||
| end |
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