Lazy loading of LazyArrays

Project.toml

@@ -5,21 +5,21 @@ version = "0.2.1"
 
 [deps]
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
-LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
 LazyStack = "1fad7336-0346-5a1a-a56f-a06ba010965b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
 [compat]
 Compat = "2.2, 3"
-LazyArrays = "0.12, 0.13, 0.14, 0.15, 0.16"
 LazyStack = "0.0.4, 0.0.5, 0.0.6, 0.0.7, 0.0.8"
 MacroTools = "0.5"
 OffsetArrays = "0.11, 1.0"
+Requires = "0.5, 1"
 StaticArrays = "0.10, 0.11, 0.12"
 ZygoteRules = "0.1, 0.2"
 julia = "1"
@@ -28,10 +28,11 @@ julia = "1"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 Einsum = "b7d42ee7-0b51-5a75-98ca-779d3107e4c0"
 JuliennedArrays = "5cadff95-7770-533d-a838-a1bf817ee6e0"
+LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
 LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Strided = "5e0ebb24-38b0-5f93-81fe-25c709ecae67"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Compat", "Einsum", "JuliennedArrays", "LoopVectorization", "Statistics", "Strided"]
+test = ["Test", "Compat", "Einsum", "JuliennedArrays", "LazyArrays", "LoopVectorization", "Statistics", "Strided"]

docs/src/options.md

@@ -79,11 +79,13 @@ In the following example, the product `V .* V' .* V3` contains about 1GB of data
 the writing of which is avoided by giving the option `lazy`: 
 
 ```julia
+using LazyArrays # you must now load this package
 V = rand(500); V3 = reshape(V,1,1,:);
 
 @time @reduce W[i] := sum(j,k) V[i]*V[j]*V[k];        # 0.6 seconds, 950 MB
 @time @reduce W[i] := sum(j,k) V[i]*V[j]*V[k]  lazy;  # 0.025 s, 5 KB
 ```
+However, right now this gives `3.7 s (250 M allocations, 9 GB)`, something is broken!
 
 The package [Strided.jl](https://github.com/Jutho/Strided.jl) can apply multi-threading to 
 broadcasting, and some other magic. You can enable it with the option `strided`, like this: 

src/TensorCast.jl

@@ -1,6 +1,12 @@
 
 module TensorCast
 
+# This speeds up loading a bit, on Julia 1.5, about 1s in my test.
+# https://github.com/JuliaPlots/Plots.jl/pull/2544/files
+if isdefined(Base, :Experimental) && isdefined(Base.Experimental, Symbol("@optlevel"))
+    @eval Base.Experimental.@optlevel 1
+end
+
 export @cast, @reduce, @matmul, @pretty
 
 using MacroTools, StaticArrays, Compat
@@ -10,11 +16,28 @@ include("macro.jl")
 include("pretty.jl")
 include("string.jl")
 
-include("slice.jl")     # slice, glue, etc
-include("view.jl")      # orient, Reverse{d} etc
-include("lazy.jl")      # LazyCast
-include("static.jl")    # StaticArrays
+module Fast # shield non-macro code from @optlevel 1
+    using LinearAlgebra, StaticArrays, Compat
+
+    include("slice.jl")     # slice, glue, etc
+    export sliceview, slicecopy, glue, glue!, red_glue, cat_glue, copy_glue, lazy_glue, iscodesorted, countcolons
+
+    include("view.jl")      # orient, Reverse{d} etc
+    export diagview, orient, rview, mul!, star, PermuteDims, Reverse, Shuffle
+
+    include("static.jl")    # StaticArrays
+    export static_slice, static_glue
+
+end
+using .Fast
+const mul! = Fast.mul!
+
+using Requires
+
+@init @require LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02" begin
+    include("lazy.jl")      # LazyCast # this costs about 3s in my test, 3.8s -> 7.7s
+end
 
-include("warm.jl")
+include("warm.jl") # worth 2s in my test
 
 end # module

src/lazy.jl

@@ -1,5 +1,5 @@
 
-import LazyArrays
+import .LazyArrays
 
 #=
 The macro option "lazy" always produces things like sum(@__dot__(lazy(x+y)))

src/macro.jl

@@ -234,6 +234,8 @@ This mostly aims to re-work the given expression into `some(steps(A))[i,j]`,
 but also pushes `A = f(x)` into `store.top`.
 """
 function standardise(ex, store::NamedTuple, call::CallInfo; LHS=false)
+    @nospecialize ex
+
     # This acts only on single indexing expressions:
     if @capture(ex, A_{ijk__})
         static=true
@@ -378,6 +380,7 @@ target dims not correctly handled yet -- what do I want? TODO
 Simple glue / stand. does not permutedims, but broadcasting may have to... avoid twice?
 """
 function standardglue(ex, target, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
 
     # The sole target here is indexing expressions:
     if @capture(ex, A_[inner__])
@@ -469,6 +472,7 @@ This beings the expression to have target indices,
 by permutedims and if necessary broadcasting, always using `readycast()`.
 """
 function targetcast(ex, target, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
 
     # If just one naked expression, then we won't broadcast:
     if @capture(ex, A_[ijk__])
@@ -503,6 +507,7 @@ end
 This is walked over the expression to prepare for `@__dot__` etc, by `targetcast()`.
 """
 function readycast(ex, target, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
 
     # Scalar functions can be protected entirely from broadcasting:
     # TODO this means A[i,j] + rand()/10 doesn't work, /(...,10) is a function!
@@ -578,6 +583,7 @@ If there are more than two factors, it recurses, and you get `(A*B) * C`,
 or perhaps tuple `(A*B, C)`.
 """
 function matmultarget(ex, target, parsed, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
 
     @capture(ex, A_ * B_ * C__ | *(A_, B_, C__) ) || throw(MacroError("can't @matmul that!", call))
 
@@ -631,6 +637,7 @@ pushing calculation steps into store.
 Also a convenient place to tidy all indices, including e.g. `fun(M[:,j],N[j]).same[i']`.
 """
 function recursemacro(ex, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
 
     # Actually look for recursion
     if @capture(ex, @reduce(subex__) )
@@ -675,6 +682,8 @@ This saves to `store` the sizes of all input tensors, and their sub-slices if an
   however it should not destroy this so that `sz_j` can be got later.
 """
 function rightsizes(ex, store::NamedTuple, call::CallInfo)
+    @nospecialize ex
+
     :recurse in call.flags && return nothing # outer version took care of this
 
     if @capture(ex, A_[outer__][inner__] | A_[outer__]{inner__} )
@@ -1115,8 +1124,7 @@ end
 
 tensorprimetidy(v::Vector) = Any[ tensorprimetidy(x) for x in v ]
 function tensorprimetidy(ex)
-    MacroTools.postwalk(ex) do x
-
+    MacroTools.postwalk(ex) do @nospecialize x
         @capture(x, ((ij__,) \ k_) ) && return :( ($(ij...),$k) )
         @capture(x, i_ \ j_ ) && return :( ($i,$j) )
 
@@ -1172,7 +1180,7 @@ containsindexing(s) = false
 function containsindexing(ex::Expr)
     flag = false
     # MacroTools.postwalk(x -> @capture(x, A_[ijk__]) && (flag=true), ex)
-    MacroTools.postwalk(ex) do x
+    MacroTools.postwalk(ex) do @nospecialize x
         # @capture(x, A_[ijk__]) && !(all(isconstant, ijk)) && (flag=true)
         if @capture(x, A_[ijk__])
             # @show x ijk # TODO this is a bit broken?  @pretty @cast Z[i,j] := W[i] * exp(X[1][i] - X[2][j])
@@ -1185,7 +1193,7 @@ end
 listindices(s::Symbol) = []
 function listindices(ex::Expr)
     list = []
-    MacroTools.postwalk(ex) do x
+    MacroTools.postwalk(ex) do @nospecialize x
         if @capture(x, A_[ijk__])
             flat, _ = indexparse(nothing, ijk)
             push!(list, flat)

test/runtests.jl

@@ -6,6 +6,7 @@ using StaticArrays
 using OffsetArrays
 using Einsum
 using Strided
+using LazyArrays
 using Compat
 if VERSION >= v"1.1"
     using LoopVectorization