# Inconsistent behaviour using Zygote.Buffer
**URL:** https://discourse.julialang.org/t/inconsistent-behaviour-using-zygote-buffer/99139
**Category:** General Usage
**Tags:** zygote
**Created:** [May 19, 2023, 8:57pm UTC](https://discourse.julialang.org/t/inconsistent-behaviour-using-zygote-buffer/99139 "2023-05-19T20:57:51Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@keithjlee](https://discourse.julialang.org/u/keithjlee)
#### Post date: [May 19, 2023, 8:57pm UTC](https://discourse.julialang.org/t/inconsistent-behaviour-using-zygote-buffer/99139/1 "2023-05-19T20:57:51Z")
I’m working on making a structural analysis code base work smoothly with Automatic Differentiation, specifically Zygote for its generally friendly interface. In short, a major performance bottleneck is the assembly of the global stiffness matrix K for an eventual linear solve to determine structural deformation.
To make use of constant-once-first-assembled sparsity pattern of the matrix, i’m trying to make each iteration simply generate a new non-zero value vector `K.nzval`, after which an updated sparse matrix can be more efficiently generated by reusing the original `K.colptr, K.rowval`, etc.
I’m making use of Zygote Buffers to make array mutation possible in the creation of the new `nzval` vector. However, I’m finding that initiating a new buffer has inconsistent values for `Buffer.data`, which affects the final outcome. Creating a buffer from a zero’d vector does not have all zeros for `Buffer.data`; generating a buffer from the original `nzval` vector also does not maintain the same values.
Am I misusing these buffers in some way? Or are there any suggestions to efficiently reassemble large sparse matrices for automatic differentiation?
Here’s a minimum working example with nonsense values, but the principle is the same:
```julia
using SparseArrays, Zygote
#initial sparse matrix
M = sprand(5000, 5000, .01)
#reuse the same sparsity pattern
m, n = size(M)
colp = M.colptr
rowv = M.rowval
nzv = M.nzval
#dummy indices that point to positions in S.nzval that need updating
n_updates = 1000
dim_update = 6
nzindices = [rand(1:length(nzv), dim_update) for _ in 1:n_updates]
updatevals = [rand(dim_update) for _ in 1:n_updates]
#without buffers (CANNOT BE AUTO-DIFFERENTIATED BUT CONSISTENT)
nznew = zero(nzv)
for (index, value) in zip(nzindices, updatevals)
nznew[index] .+= value
end
newS_nobuffer = SparseMatrixCSC(m, n, colp, rowv, nznew)
#with buffers (CAN BE AD'd, BUT INCONSISTENT)
nzbuff = Zygote.Buffer(zero(nzv))
for (index, value) in zip(nzindices, updatevals)
nzbuff[index] .+= value
end
newS_buffer = SparseMatrixCSC(m, n, colp, rowv, copy(nzbuff))
#compare the two
norm(newS_nobuffer .- newS_buffer)
# this will give a different value each time
for _ = 1:100
nzbuff = Zygote.Buffer(zero(nzv))
for (index, value) in zip(nzindices, updatevals)
nzbuff[index] .+= value
end
newS_buffer = SparseMatrixCSC(m, n, colp, rowv, copy(nzbuff))
norm(newS_nobuffer .- newS_buffer)
end
```
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### Author:  [@keithjlee](https://discourse.julialang.org/u/keithjlee)
#### Post date: [May 23, 2023, 10:11pm UTC](https://discourse.julialang.org/t/inconsistent-behaviour-using-zygote-buffer/99139/2 "2023-05-23T22:11:42Z")
FYI,
I decided to approach this the “right” way and just wrote proper adjoint rules to bypass the buffers. However, I’m still curious as to its inconsistent behaviour!
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### Author:  [@contradict](https://discourse.julialang.org/u/contradict)
#### Post date: [May 23, 2023, 11:45pm UTC](https://discourse.julialang.org/t/inconsistent-behaviour-using-zygote-buffer/99139/3 "2023-05-23T23:45:22Z")
`Zygote.Buffer` is like `similar`, it allocates a new uninitialized array with the same type and dimensions as the argument. Once you create the `Buffer`, all entries must be initialized explicitly, it does not copy data from the argument.