fix: fix the typos

.typos.toml

@@ -0,0 +1,5 @@
+[default.extend-words]
+numer = "numer"
+nd = "nd"
+Ba = "Ba"
+

ext/LuxLibCUDAExt/cublaslt.jl

@@ -125,7 +125,7 @@ function LuxLib._cublaslt_matmul_fused!(
     lthandle = Ref{CUBLAS.cublasLtHandle_t}()
     CUBLAS.cublasLtCreate(lthandle)
 
-    # Seach for the best algorithm
+    # Search for the best algorithm
     heuristic = Ref{CUBLAS.cublasLtMatmulHeuristicResult_t}()
     returnedResults = Ref{Cint}(0)
     CUBLAS.cublasLtMatmulAlgoGetHeuristic(

ext/LuxLibForwardDiffExt.jl

@@ -12,7 +12,7 @@ LuxLib.__has_dual(::AbstractArray{<:ForwardDiff.Dual}) = true
     return ForwardDiff.valtype(eltype(x))
 end
 
-# Convolutions: We might want to capture these furthur down in `conv!`
+# Convolutions: We might want to capture these further down in `conv!`
 # NOTE: In principle we can concatenate all of the partials along the batch dimension
 #       and cut down substantially on the time to compute jacobians.
 # Here we should be broadcasting with `Tag` for safety but that breaks GPU compilation.

src/utils.jl

@@ -27,7 +27,7 @@ EnzymeRules.inactive_noinl(::typeof(_copy_autodiff_barrier), ::Any...) = nothing
 __is_tracked(x) = x == :TrackedArray || x == :TrackedVector
 __is_tracked(args...) = any(__is_tracked, args)
 
-# Droping ForwardDiff Gradients
+# Dropping ForwardDiff Gradients
 function _drop_forwarddiff_partials end
 
 _drop_forwarddiff_partials(x::AbstractArray) = x