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changeset 997:20c376dffe84 refactor/lazy_tensors

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Move tuple functions to their own file
author Jonatan Werpers <jonatan@werpers.com>
date Fri, 18 Mar 2022 21:26:02 +0100
parents aa72f067e771
children 390dfc3db4b1
files src/LazyTensors/LazyTensors.jl src/LazyTensors/lazy_tensor_operations.jl src/LazyTensors/tuple_manipulation.jl test/LazyTensors/lazy_tensor_operations_test.jl test/LazyTensors/tuple_manipulation_test.jl
diffstat 5 files changed, 136 insertions(+), 138 deletions(-) [+]
line wrap: on
line diff
--- a/src/LazyTensors/LazyTensors.jl	Fri Mar 18 21:17:01 2022 +0100
+++ b/src/LazyTensors/LazyTensors.jl	Fri Mar 18 21:26:02 2022 +0100
@@ -14,5 +14,6 @@
 include("lazy_tensor.jl")
 include("lazy_array.jl")
 include("lazy_tensor_operations.jl")
+include("tuple_manipulation.jl")
 
 end # module
--- a/src/LazyTensors/lazy_tensor_operations.jl	Fri Mar 18 21:17:01 2022 +0100
+++ b/src/LazyTensors/lazy_tensor_operations.jl	Fri Mar 18 21:26:02 2022 +0100
@@ -303,83 +303,6 @@
 end
 
 
-"""
-    split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...)
-
-Splits the multi-index `I` into two parts. One part which is expected to be
-used as a view, and one which is expected to be used as an index.
-Eg.
-```
-split_index(Val(1),Val(3),Val(2),Val(1),(1,2,3,4)) -> (1,:,:,:,4), (2,3)
-```
-
-`dim_view` controls how many colons are in the view, and `dim_index` controls
-how many elements are extracted from the middle.
-`dim_before` and `dim_after` decides the length of the index parts before and after the colons in the view index.
-
-Arguments should satisfy `length(I) == dim_before+B_domain+dim_after`.
-
-The returned values satisfy
- * `length(view_index) == dim_before + dim_view + dim_after`
- * `length(I_middle) == dim_index`
-"""
-function split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...) where {dim_before,dim_view, dim_index,dim_after}
-    I_before, I_middle, I_after = split_tuple(I, Val(dim_before), Val(dim_index))
-
-    view_index = (I_before..., ntuple((i)->:, dim_view)..., I_after...)
-
-    return view_index, I_middle
-end
-
-# TODO: Can this be replaced by something more elegant while still being type stable? 2020-10-21
-# See:
-# https://github.com/JuliaLang/julia/issues/34884
-# https://github.com/JuliaLang/julia/issues/30386
-"""
-    slice_tuple(t, Val(l), Val(u))
-
-Get a slice of a tuple in a type stable way.
-Equivalent to `t[l:u]` but type stable.
-"""
-function slice_tuple(t,::Val{L},::Val{U}) where {L,U}
-    return ntuple(i->t[i+L-1], U-L+1)
-end
-
-"""
-    split_tuple(t::Tuple{...}, ::Val{M}) where {N,M}
-
-Split the tuple `t` into two parts. the first part is `M` long.
-E.g
-```julia
-split_tuple((1,2,3,4),Val(3)) -> (1,2,3), (4,)
-```
-"""
-function split_tuple(t::NTuple{N,Any},::Val{M}) where {N,M}
-    return slice_tuple(t,Val(1), Val(M)), slice_tuple(t,Val(M+1), Val(N))
-end
-
-"""
-    split_tuple(t::Tuple{...},::Val{M},::Val{K}) where {N,M,K}
-
-Same as `split_tuple(t::NTuple{N},::Val{M})` but splits the tuple in three parts. With the first
-two parts having lenght `M` and `K`.
-"""
-function split_tuple(t::NTuple{N,Any},::Val{M},::Val{K}) where {N,M,K}
-    p1, tail = split_tuple(t, Val(M))
-    p2, p3 = split_tuple(tail, Val(K))
-    return p1,p2,p3
-end
-
-
-"""
-    flatten_tuple(t)
-
-Takes a nested tuple and flattens the whole structure
-"""
-flatten_tuple(t::NTuple{N, Number} where N) = t
-flatten_tuple(t::Tuple) = ((flatten_tuple.(t)...)...,) # simplify?
-flatten_tuple(ts::Vararg) = flatten_tuple(ts)
-
 @doc raw"""
     LazyOuterProduct(tms...)
 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/LazyTensors/tuple_manipulation.jl	Fri Mar 18 21:26:02 2022 +0100
@@ -0,0 +1,76 @@
+"""
+    split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...)
+
+Splits the multi-index `I` into two parts. One part which is expected to be
+used as a view, and one which is expected to be used as an index.
+Eg.
+```
+split_index(Val(1),Val(3),Val(2),Val(1),(1,2,3,4)) -> (1,:,:,:,4), (2,3)
+```
+
+`dim_view` controls how many colons are in the view, and `dim_index` controls
+how many elements are extracted from the middle.
+`dim_before` and `dim_after` decides the length of the index parts before and after the colons in the view index.
+
+Arguments should satisfy `length(I) == dim_before+B_domain+dim_after`.
+
+The returned values satisfy
+ * `length(view_index) == dim_before + dim_view + dim_after`
+ * `length(I_middle) == dim_index`
+"""
+function split_index(::Val{dim_before}, ::Val{dim_view}, ::Val{dim_index}, ::Val{dim_after}, I...) where {dim_before,dim_view, dim_index,dim_after}
+    I_before, I_middle, I_after = split_tuple(I, Val(dim_before), Val(dim_index))
+
+    view_index = (I_before..., ntuple((i)->:, dim_view)..., I_after...)
+
+    return view_index, I_middle
+end
+
+# TODO: Can this be replaced by something more elegant while still being type stable? 2020-10-21
+# See:
+# https://github.com/JuliaLang/julia/issues/34884
+# https://github.com/JuliaLang/julia/issues/30386
+"""
+    slice_tuple(t, Val(l), Val(u))
+
+Get a slice of a tuple in a type stable way.
+Equivalent to `t[l:u]` but type stable.
+"""
+function slice_tuple(t,::Val{L},::Val{U}) where {L,U}
+    return ntuple(i->t[i+L-1], U-L+1)
+end
+
+"""
+    split_tuple(t::Tuple{...}, ::Val{M}) where {N,M}
+
+Split the tuple `t` into two parts. the first part is `M` long.
+E.g
+```julia
+split_tuple((1,2,3,4),Val(3)) -> (1,2,3), (4,)
+```
+"""
+function split_tuple(t::NTuple{N,Any},::Val{M}) where {N,M}
+    return slice_tuple(t,Val(1), Val(M)), slice_tuple(t,Val(M+1), Val(N))
+end
+
+"""
+    split_tuple(t::Tuple{...},::Val{M},::Val{K}) where {N,M,K}
+
+Same as `split_tuple(t::NTuple{N},::Val{M})` but splits the tuple in three parts. With the first
+two parts having lenght `M` and `K`.
+"""
+function split_tuple(t::NTuple{N,Any},::Val{M},::Val{K}) where {N,M,K}
+    p1, tail = split_tuple(t, Val(M))
+    p2, p3 = split_tuple(tail, Val(K))
+    return p1,p2,p3
+end
+
+
+"""
+    flatten_tuple(t)
+
+Takes a nested tuple and flattens the whole structure
+"""
+flatten_tuple(t::NTuple{N, Number} where N) = t
+flatten_tuple(t::Tuple) = ((flatten_tuple.(t)...)...,) # simplify?
+flatten_tuple(ts::Vararg) = flatten_tuple(ts)
--- a/test/LazyTensors/lazy_tensor_operations_test.jl	Fri Mar 18 21:17:01 2022 +0100
+++ b/test/LazyTensors/lazy_tensor_operations_test.jl	Fri Mar 18 21:26:02 2022 +0100
@@ -394,67 +394,6 @@
     end
 end
 
-@testset "split_index" begin
-    @test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4))
-    @test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4))
-    @test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,))
-    @test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,))
-    @test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3))
-    @test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3))
-
-    @test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3))
-    @test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6))
-
-    @inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4)
-end
-
-@testset "slice_tuple" begin
-    @test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3)
-    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5)
-    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3)
-    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6)
-end
-
-@testset "split_tuple" begin
-    @testset "2 parts" begin
-        @test LazyTensors.split_tuple((),Val(0)) == ((),())
-        @test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,))
-        @test LazyTensors.split_tuple((1,),Val(1)) == ((1,),())
-
-        @test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4))
-        @test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4))
-        @test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4))
-        @test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,))
-        @test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),())
-
-        @test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,))
-
-        @inferred LazyTensors.split_tuple((1,2,3,4),Val(3))
-        @inferred LazyTensors.split_tuple((1,2,true,4),Val(3))
-    end
-
-    @testset "3 parts" begin
-        @test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),())
-        @test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,))
-        @test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,))
-
-        @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6))
-        @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,))
-
-        @inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2))
-        @inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1))
-    end
-end
-
-@testset "flatten_tuple" begin
-    @test LazyTensors.flatten_tuple((1,)) == (1,)
-    @test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6)
-    @test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6)
-    @test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6)
-    @test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6)
-end
-
-
 @testset "LazyOuterProduct" begin
 
     A = ScalingTensor(2.0, (5,))
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test/LazyTensors/tuple_manipulation_test.jl	Fri Mar 18 21:26:02 2022 +0100
@@ -0,0 +1,59 @@
+@testset "split_index" begin
+    @test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4))
+    @test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4))
+    @test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,))
+    @test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,))
+    @test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3))
+    @test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3))
+
+    @test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3))
+    @test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6))
+
+    @inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4)
+end
+
+@testset "slice_tuple" begin
+    @test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3)
+    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5)
+    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3)
+    @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6)
+end
+
+@testset "split_tuple" begin
+    @testset "2 parts" begin
+        @test LazyTensors.split_tuple((),Val(0)) == ((),())
+        @test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,))
+        @test LazyTensors.split_tuple((1,),Val(1)) == ((1,),())
+
+        @test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4))
+        @test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4))
+        @test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4))
+        @test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,))
+        @test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),())
+
+        @test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,))
+
+        @inferred LazyTensors.split_tuple((1,2,3,4),Val(3))
+        @inferred LazyTensors.split_tuple((1,2,true,4),Val(3))
+    end
+
+    @testset "3 parts" begin
+        @test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),())
+        @test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,))
+        @test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,))
+
+        @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6))
+        @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,))
+
+        @inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2))
+        @inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1))
+    end
+end
+
+@testset "flatten_tuple" begin
+    @test LazyTensors.flatten_tuple((1,)) == (1,)
+    @test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6)
+    @test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6)
+    @test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6)
+    @test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6)
+end