-
-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
broadcast.jl
955 lines (843 loc) · 32.8 KB
/
broadcast.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
# This file is a part of Julia. License is MIT: https://julialang.org/license
using Test, Random
module TestBroadcastInternals
using Base.Broadcast: check_broadcast_axes, check_broadcast_shape, newindex, _bcs
using Base: OneTo
using Test, Random
@test @inferred(_bcs((3,5), (3,5))) == (3,5)
@test @inferred(_bcs((3,1), (3,5))) == (3,5)
@test @inferred(_bcs((3,), (3,5))) == (3,5)
@test @inferred(_bcs((3,5), (3,))) == (3,5)
@test_throws DimensionMismatch _bcs((3,5), (4,5))
@test_throws DimensionMismatch _bcs((3,5), (3,4))
@test @inferred(_bcs((-1:1, 2:5), (-1:1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((-1:1, 2:5), (1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((-1:1, 1), (1, 2:5))) == (-1:1, 2:5)
@test @inferred(_bcs((-1:1,), (-1:1, 2:5))) == (-1:1, 2:5)
@test_throws DimensionMismatch _bcs((-1:1, 2:6), (-1:1, 2:5))
@test_throws DimensionMismatch _bcs((-1:1, 2:5), (2, 2:5))
@test @inferred(Broadcast.combine_axes(zeros(3,4), zeros(3,4))) == (OneTo(3),OneTo(4))
@test @inferred(Broadcast.combine_axes(zeros(3,4), zeros(3))) == (OneTo(3),OneTo(4))
@test @inferred(Broadcast.combine_axes(zeros(3), zeros(3,4))) == (OneTo(3),OneTo(4))
@test @inferred(Broadcast.combine_axes(zeros(3), zeros(1,4), zeros(1))) == (OneTo(3),OneTo(4))
check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,5))
check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,1))
check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3))
check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,5), zeros(3))
check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,5), 1)
check_broadcast_axes((OneTo(3),OneTo(5)), 5, 2)
@test_throws DimensionMismatch check_broadcast_axes((OneTo(3),OneTo(5)), zeros(2,5))
@test_throws DimensionMismatch check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,4))
@test_throws DimensionMismatch check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,4,2))
@test_throws DimensionMismatch check_broadcast_axes((OneTo(3),OneTo(5)), zeros(3,5), zeros(2))
check_broadcast_axes((-1:1, 6:9), 1)
check_broadcast_shape((-1:1, 6:9), (-1:1, 6:9))
check_broadcast_shape((-1:1, 6:9), (-1:1, 1))
check_broadcast_shape((-1:1, 6:9), (1, 6:9))
@test_throws DimensionMismatch check_broadcast_shape((-1:1, 6:9), (-1, 6:9))
@test_throws DimensionMismatch check_broadcast_shape((-1:1, 6:9), (-1:1, 6))
ci(x) = CartesianIndex(x)
@test @inferred(newindex(ci((2,2)), (true, true), (-1,-1))) == ci((2,2))
@test @inferred(newindex(ci((2,2)), (true, false), (-1,-1))) == ci((2,-1))
@test @inferred(newindex(ci((2,2)), (false, true), (-1,-1))) == ci((-1,2))
@test @inferred(newindex(ci((2,2)), (false, false), (-1,-1))) == ci((-1,-1))
@test @inferred(newindex(ci((2,2)), (true,), (-1,-1))) == ci((2,))
@test @inferred(newindex(ci((2,2)), (true,), (-1,))) == ci((2,))
@test @inferred(newindex(ci((2,2)), (false,), (-1,))) == ci((-1,))
@test @inferred(newindex(ci((2,2)), (), ())) == ci(())
end
function as_sub(x::AbstractVector)
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:length(y))
y[:] = x[:]
y
end
function as_sub(x::AbstractMatrix)
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:size(y,1), 2:2:size(y,2))
for j=1:size(x,2)
for i=1:size(x,1)
y[i,j] = x[i,j]
end
end
y
end
function as_sub(x::AbstractArray{T,3}) where T
y = similar(x, eltype(x), tuple(([size(x)...]*2)...))
y = view(y, 2:2:size(y,1), 2:2:size(y,2), 2:2:size(y,3))
for k=1:size(x,3)
for j=1:size(x,2)
for i=1:size(x,1)
y[i,j,k] = x[i,j,k]
end
end
end
y
end
bittest(f::Function, a...) = (@test f.(a...) == BitArray(broadcast(f, a...)))
n1 = 21
n2 = 32
n3 = 17
rb = 1:5
for arr in (identity, as_sub)
@test broadcast(+, arr([1 0; 0 1]), arr([1, 4])) == [2 1; 4 5]
@test broadcast(+, arr([1 0; 0 1]), arr([1 4])) == [2 4; 1 5]
@test broadcast(+, arr([1 0]), arr([1, 4])) == [2 1; 5 4]
@test broadcast(+, arr([1, 0]), arr([1 4])) == [2 5; 1 4]
@test broadcast(+, arr([1, 0]), arr([1, 4])) == [2, 4]
@test broadcast(+, arr([1, 0]), 2) == [3, 2]
@test @inferred(broadcast(+, arr([1 0; 0 1]), arr([1, 4]))) == arr([2 1; 4 5])
@test arr([1 0; 0 1]) .+ arr([1 4]) == arr([2 4; 1 5])
@test arr([1 0]) .+ arr([1, 4]) == arr([2 1; 5 4])
@test arr([1, 0]) .+ arr([1 4]) == arr([2 5; 1 4])
@test arr([1, 0]) .+ arr([1, 4]) == arr([2, 4])
@test arr([1]) .+ arr([]) == arr([])
A = arr([1 0; 0 1]); @test broadcast!(+, A, A, arr([1, 4])) == arr([2 1; 4 5])
A = arr([1 0; 0 1]); @test broadcast!(+, A, A, arr([1 4])) == arr([2 4; 1 5])
A = arr([1 0]); @test_throws DimensionMismatch broadcast!(+, A, A, arr([1, 4]))
A = arr([1 0]); @test broadcast!(+, A, A, arr([1 4])) == arr([2 4])
A = arr([1 0]); @test broadcast!(+, A, A, 2) == arr([3 2])
@test arr([ 1 2]) .* arr([3, 4]) == [ 3 6; 4 8]
@test arr([24.0 12.0]) ./ arr([2.0, 3.0]) == [12 6; 8 4]
@test arr([1 2]) ./ arr([3, 4]) == [1/3 2/3; 1/4 2/4]
@test arr([1 2]) .\ arr([3, 4]) == [3 1.5; 4 2]
@test arr([3 4]) .^ arr([1, 2]) == [3 4; 9 16]
@test arr(BitArray([true false])) .* arr(BitArray([true, true])) == [true false; true false]
@test arr(BitArray([true false])) .^ arr(BitArray([false, true])) == [true true; true false]
@test arr(BitArray([true false])) .^ arr([0, 3]) == [true true; true false]
M = arr([11 12; 21 22])
@test getindex.((M,), [2 1; 1 2], arr([1, 2])) == [21 11; 12 22]
@test_throws BoundsError getindex.((M,), [2 1; 1 2], arr([1, -1]))
@test_throws BoundsError getindex.((M,), [2 1; 1 2], arr([1, 2]), [2])
@test getindex.((M,), [2 1; 1 2],arr([2, 1]), [1]) == [22 12; 11 21]
A = arr(zeros(2,2))
setindex!.((A,), arr([21 11; 12 22]), [2 1; 1 2], arr([1, 2]))
@test A == M
setindex!.((A,), 5, [1,2], [2 2])
@test A == [11 5; 21 5]
setindex!.((A,), 7, [1,2], [1 2])
@test A == fill(7, 2, 2)
A = arr(zeros(3,3))
setindex!.((A,), 10:12, 1:3, 1:3)
@test A == [10 0 0; 0 11 0; 0 0 12]
@test_throws BoundsError setindex!.((A,), 7, [1,-1], [1 2])
for f in ((==), (<) , (!=), (<=))
bittest(f, arr([1 0; 0 1]), arr([1, 4]))
bittest(f, arr([1 0; 0 1]), arr([1 4]))
bittest(f, arr([0, 1]), arr([1 4]))
bittest(f, arr([0 1]), arr([1, 4]))
bittest(f, arr([1, 0]), arr([1, 4]))
bittest(f, arr(rand(rb, n1, n2, n3)), arr(rand(rb, n1, n2, n3)))
bittest(f, arr(rand(rb, 1, n2, n3)), arr(rand(rb, n1, 1, n3)))
bittest(f, arr(rand(rb, 1, n2, 1)), arr(rand(rb, n1, 1, n3)))
bittest(f, arr(bitrand(n1, n2, n3)), arr(bitrand(n1, n2, n3)))
end
end
r1 = 1:1
r2 = 1:5
ratio = [1,1/2,1/3,1/4,1/5]
@test r1.*r2 == [1:5;]
@test r1./r2 == ratio
m = [1:2;]'
@test m.*r2 == [1:5 2:2:10]
@test m./r2 ≈ [ratio 2ratio]
@test m./[r2;] ≈ [ratio 2ratio]
@test @inferred(broadcast(+,[0,1.2],reshape([0,-2],1,1,2))) == reshape([0 -2; 1.2 -0.8],2,1,2)
rt = Base.return_types(broadcast, Tuple{typeof(+), Array{Float64, 3}, Array{Int, 1}})
@test length(rt) == 1 && rt[1] == Array{Float64, 3}
rt = Base.return_types(broadcast!, Tuple{Function, Array{Float64, 3}, Array{Float64, 3}, Array{Int, 1}})
@test length(rt) == 1 && rt[1] == Array{Float64, 3}
# f.(args...) syntax (#15032)
let x = [1, 3.2, 4.7],
y = [3.5, pi, 1e-4],
α = 0.2342
@test sin.(x) == broadcast(sin, x)
@test sin.(α) == broadcast(sin, α)
@test sin.(3.2) == broadcast(sin, 3.2) == sin(3.2)
@test factorial.(3) == broadcast(factorial, 3)
@test atan.(x, y) == broadcast(atan, x, y)
@test atan.(x, y') == broadcast(atan, x, y')
@test atan.(x, α) == broadcast(atan, x, α)
@test atan.(α, y') == broadcast(atan, α, y')
end
# issue 14725
let a = Number[2, 2.0, 4//2, 2+0im] / 2
@test eltype(a) == Number
end
let a = Real[2, 2.0, 4//2] / 2
@test eltype(a) == Real
end
let a = Real[2, 2.0, 4//2] / 2.0
@test eltype(a) == Float64
end
# issue 16164
let a = broadcast(Float32, [3, 4, 5])
@test eltype(a) == Float32
end
# broadcasting scalars:
@test sin.(1) === broadcast(sin, 1) === sin(1)
@test (()->1234).() === broadcast(()->1234) === 1234
# issue #4883
@test isa(broadcast(tuple, [1 2 3], ["a", "b", "c"]), Matrix{Tuple{Int,String}})
@test isa(broadcast((x,y)->(x==1 ? 1.0 : x, y), [1 2 3], ["a", "b", "c"]), Matrix{Tuple{Real,String}})
let a = length.(["foo", "bar"])
@test isa(a, Vector{Int})
@test a == [3, 3]
end
let a = sin.([1, 2])
@test isa(a, Vector{Float64})
@test a ≈ [0.8414709848078965, 0.9092974268256817]
end
# PR #17300: loop fusion
@test (x->x+1).((x->x+2).((x->x+3).(1:10))) == 7:16
let A = [sqrt(i)+j for i = 1:3, j=1:4]
@test atan.(log.(A), sum(A, dims=1)) == broadcast(atan, broadcast(log, A), sum(A, dims=1))
end
let x = sin.(1:10)
@test atan.((x->x+1).(x), (x->x+2).(x)) == broadcast(atan, x.+1, x.+2)
@test sin.(atan.([x.+1,x.+2]...)) == sin.(atan.(x.+1 ,x.+2)) == @. sin(atan(x+1,x+2))
@test sin.(atan.(x, 3.7)) == broadcast(x -> sin(atan(x,3.7)), x)
@test atan.(x, 3.7) == broadcast(x -> atan(x,3.7), x) == broadcast(atan, x, 3.7)
end
# Use side effects to check for loop fusion.
let g = Int[]
f17300(x) = begin; push!(g, x); x+2; end
f17300.(f17300.(f17300.(1:3)))
@test g == [1,3,5, 2,4,6, 3,5,7]
empty!(g)
@. f17300(f17300(f17300(1:3)))
@test g == [1,3,5, 2,4,6, 3,5,7]
end
# fusion with splatted args:
let x = sin.(1:10), a = [x]
@test cos.(x) == cos.(a...)
@test atan.(x,x) == atan.(a..., a...) == atan.([x, x]...)
@test atan.(x, cos.(x)) == atan.(a..., cos.(x)) == broadcast(atan, x, cos.(a...)) == broadcast(atan, a..., cos.(a...))
@test ((args...)->cos(args[1])).(x) == cos.(x) == ((y,args...)->cos(y)).(x)
end
@test atan.(3, 4) == atan(3, 4) == (() -> atan(3, 4)).()
# fusion with keyword args:
let x = [1:4;]
f17300kw(x; y=0) = x + y
@test f17300kw.(x) == x
@test f17300kw.(x, y=1) == f17300kw.(x; y=1) == f17300kw.(x; [(:y,1)]...) == x .+ 1 == [2, 3, 4, 5]
@test f17300kw.(sin.(x), y=1) == f17300kw.(sin.(x); y=1) == sin.(x) .+ 1
@test sin.(f17300kw.(x, y=1)) == sin.(f17300kw.(x; y=1)) == sin.(x .+ 1)
end
function lazybc end
struct LazyBC{T}
value::T
end
Broadcast.broadcasted(::typeof(lazybc), x) = LazyBC(x)
Broadcast.materialize(x::LazyBC) = x.value
@testset "FixKwargs" begin
function f end
bc = lazybc.(f.(1, 2, a = 3, b = 4))
@test bc.f isa Base.Broadcast.FixKwargs
@test bc.f.f === f
@test (; bc.f.kwargs...) === (a = 3, b = 4)
@test bc.args == (1, 2)
end
struct TypeWithKwargs end
TypeWithKwargs(a, args...; kwargs...) = TypeWithKwargs()
@testset "type inference with a type with kwargs" begin
f() = last.(tuple.([1], TypeWithKwargs.(1, 2; a = 3, b = 4)))[1]
@test @inferred(f()) === TypeWithKwargs()
end
# issue #23236
let X = [[true,false],[false,true]]
@test [.!x for x in X] == [[false,true],[true,false]]
end
# splice escaping of @.
let x = [4, -9, 1, -16]
@test [2, 3, 4, 5] == @.(1 + sqrt($sort(abs(x))))
end
# interaction of @. with let
@test [1,4,9] == @. let x = [1,2,3]; x^2; end
# interaction of @. with for loops
let x = [1,2,3], y = x
@. for i = 1:3
y = y^2 # should convert to y .= y.^2
end
@test x == [1,256,6561]
end
# interaction of @. with function definitions
let x = [1,2,3]
@. f(x) = x^2
@test f(x) == [1,4,9]
end
# Issue #23622: @. with chained comparisons
let x = [1,2,3]
@test (1 .< x .< 3) == @.(1 < x < 3) == (@. 1 .< x .< 3) == [false, true, false]
@test (x .=== 1:3 .=== [1,2,3]) == @.(x === 1:3 === [1,2,3]) == [true, true, true]
end
# PR #17510: Fused in-place assignment
let x = [1:4;], y = x
y .= 2:5
@test y === x == [2:5;]
y .= factorial.(x)
@test y === x == [2,6,24,120]
y .= 7
@test y === x == [7,7,7,7]
y .= factorial.(3)
@test y === x == [6,6,6,6]
f17510() = 9
y .= f17510.()
@test y === x == [9,9,9,9]
y .-= 1
@test y === x == [8,8,8,8]
@. y -= 1:4 # @. should convert to .-=
@test y === x == [7,6,5,4]
x[1:2] .= 1
@test y === x == [1,1,5,4]
@. x[1:2] .+= [2,3] # use .+= to make sure @. works with dotted assignment
@test y === x == [3,4,5,4]
@. x[:] .= 0 # use .= to make sure @. works with dotted assignment
@test y === x == [0,0,0,0]
@. x[2:end] = 1:3 # @. should convert to .=
@test y === x == [0,1,2,3]
end
let a = [[4, 5], [6, 7]]
a[1] .= 3
@test a == [[3, 3], [6, 7]]
end
let d = Dict(:foo => [1,3,7], (3,4) => [5,9])
d[:foo] .+= 2
@test d[:foo] == [3,5,9]
d[3,4] .-= 1
@test d[3,4] == [4,8]
end
let identity = error, x = [1,2,3]
x .= 1 # make sure it goes to broadcast!(Base.identity, ...), not identity
@test x == [1,1,1]
end
# make sure scalars are inlined, which causes f.(x,scalar) to lower to a "thunk"
import Base.Meta: isexpr
@test isexpr(Meta.lower(Main, :(f.(x,1))), :thunk)
@test isexpr(Meta.lower(Main, :(f.(x,1.0))), :thunk)
@test isexpr(Meta.lower(Main, :(f.(x,$π))), :thunk)
@test isexpr(Meta.lower(Main, :(f.(x,"hello"))), :thunk)
@test isexpr(Meta.lower(Main, :(f.(x,$("hello")))), :thunk)
# PR #17623: Fused binary operators
@test [true] .* [true] == [true]
@test [1,2,3] .|> (x->x+1) == [2,3,4]
let g = Int[], ⊕ = (a,b) -> let c=a+2b; push!(g, c); c; end
@test [1,2,3] .⊕ [10,11,12] .⊕ [100,200,300] == [221,424,627]
@test g == [21,221,24,424,27,627] # test for loop fusion
end
# Fused unary operators
@test .√[3,4,5] == sqrt.([3,4,5])
@test .![true, true, false] == [false, false, true]
@test .-[1,2,3] == -[1,2,3] == .+[-1,-2,-3] == [-1,-2,-3]
# PR 16988
@test Base.promote_op(+, Bool) === Int
@test isa(broadcast(+, [true]), Array{Int,1})
# issue #17304
let foo = [[1,2,3],[4,5,6],[7,8,9]]
@test max.(foo...) == broadcast(max, foo...) == [7,8,9]
end
# Issue 17314
@test broadcast(x->log(log(log(x))), [1000]) == [log(log(log(1000)))]
let f17314 = x -> x < 0 ? false : x
@test eltype(broadcast(f17314, 1:3)) === Int
@test eltype(broadcast(f17314, -1:1)) === Integer
@test eltype(broadcast(f17314, Int[])) == Union{Bool,Int}
end
let io = IOBuffer()
broadcast(x->print(io,x), 1:5) # broadcast with side effects
@test take!(io) == [0x31,0x32,0x33,0x34,0x35]
end
# Issue 18176
let f18176(a, b, c) = a + b + c
@test f18176.(1.0:2, 3, 4) == f18176.(3.0, 1.0:2, 4.0) == broadcast(f18176, 3, 4, 1.0:2)
end
# Issue #17984
let A17984 = []
@test isa(abs.(A17984), Array{Any,1})
end
# Issue #16966
@test parse.(Int, "1") == 1
@test parse.(Int, ["1", "2"]) == [1, 2]
@test trunc.((Int,), [1.2, 3.4]) == [1, 3]
@test abs.((1, -2)) == (1, 2)
@test broadcast(+, 1.0, (0, -2.0)) == (1.0,-1.0)
@test broadcast(+, 1.0, (0, -2.0), [1]) == [2.0, 0.0]
@test broadcast(*, ["Hello"], ", ", ["World"], "!") == ["Hello, World!"]
let s = "foo"
@test s .* ["bar", "baz"] == ["foobar", "foobaz"] == "foo" .* ["bar", "baz"]
end
# Ensure that even strange constructors that break `T(x)::T` work with broadcast
struct StrangeType18623 end
StrangeType18623(x) = x
StrangeType18623(x,y) = (x,y)
@test @inferred(broadcast(StrangeType18623, 1:3)) == [1,2,3]
@test @inferred(broadcast(StrangeType18623, 1:3, 4:6)) == [(1,4),(2,5),(3,6)]
@test typeof(Int.(Number[1, 2, 3])) === typeof((x->Int(x)).(Number[1, 2, 3]))
@test @inferred(broadcast(CartesianIndex, 1:2)) == [CartesianIndex(1), CartesianIndex(2)]
@test @inferred(broadcast(CartesianIndex, 1:2, 3:4)) == [CartesianIndex(1,3), CartesianIndex(2,4)]
# Issue 18622
@test @inferred(broadcast(muladd, [1.0], [2.0], [3.0])) == [5.0]
@test @inferred(broadcast(tuple, 1:3, 4:6, 7:9)) == [(1,4,7), (2,5,8), (3,6,9)]
# 19419
@test @inferred(broadcast(round, Int, [1])) == [1]
# https://discourse.julialang.org/t/towards-broadcast-over-combinations-of-sparse-matrices-and-scalars/910
let
f(A, n) = broadcast(x -> +(x, n), A)
@test @inferred(f([1.0], 1)) == [2.0]
g() = (a = 1; Broadcast.combine_eltypes(x -> x + a, (1.0,)))
@test @inferred(g()) === Float64
end
# Ref as 0-dimensional array for broadcast
@test (-).(C_NULL, C_NULL)::UInt == 0
@test (+).(1, Ref(2)) == 3
@test (+).(Ref(1), Ref(2)) == 3
@test (+).([[0,2], [1,3]], Ref{Vector{Int}}([1,-1])) == [[1,1], [2,2]]
# Check that broadcast!(f, A) populates A via independent calls to f (#12277, #19722),
# and similarly for broadcast!(f, A, numbers...) (#19799).
@test let z = 1; A = broadcast!(() -> z += 1, zeros(2)); A[1] != A[2]; end
@test let z = 1; A = broadcast!(x -> z += x, zeros(2), 1); A[1] != A[2]; end
## broadcasting for custom AbstractArray
abstract type ArrayData{T,N} <: AbstractArray{T,N} end
Base.getindex(A::ArrayData, i::Integer...) = A.data[i...]
Base.setindex!(A::ArrayData, v::Any, i::Integer...) = setindex!(A.data, v, i...)
Base.size(A::ArrayData) = size(A.data)
Base.similar(bc::Broadcast.Broadcasted{Broadcast.ArrayStyle{A}}, ::Type{T}) where {A,T} =
A(Array{T}(undef, size(bc)))
struct Array19745{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:Array19745} = Broadcast.ArrayStyle{Array19745}()
# Two specialized broadcast rules with no declared precedence
struct AD1{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD1} = Broadcast.ArrayStyle{AD1}()
struct AD2{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD2} = Broadcast.ArrayStyle{AD2}()
# Two specialized broadcast rules with explicit precedence
struct AD1P{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD1P} = Broadcast.ArrayStyle{AD1P}()
struct AD2P{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD2P} = Broadcast.ArrayStyle{AD2P}()
Base.BroadcastStyle(a1::Broadcast.ArrayStyle{AD1P}, ::Broadcast.ArrayStyle{AD2P}) = a1
# Two specialized broadcast rules where users unnecessarily
# define `BroadcastStyle` for both argument orders (but do so consistently)
struct AD1B{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD1B} = Broadcast.ArrayStyle{AD1B}()
struct AD2B{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD2B} = Broadcast.ArrayStyle{AD2B}()
Base.BroadcastStyle(a1::Broadcast.ArrayStyle{AD1B}, a2::Broadcast.ArrayStyle{AD2B}) = a1
Base.BroadcastStyle(a2::Broadcast.ArrayStyle{AD2B}, a1::Broadcast.ArrayStyle{AD1B}) = a1
# Two specialized broadcast rules with conflicting precedence
struct AD1C{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD1C} = Broadcast.ArrayStyle{AD1C}()
struct AD2C{T,N} <: ArrayData{T,N}
data::Array{T,N}
end
Base.BroadcastStyle(::Type{T}) where {T<:AD2C} = Broadcast.ArrayStyle{AD2C}()
Base.BroadcastStyle(a1::Broadcast.ArrayStyle{AD1C}, a2::Broadcast.ArrayStyle{AD2C}) = a1
Base.BroadcastStyle(a2::Broadcast.ArrayStyle{AD2C}, a1::Broadcast.ArrayStyle{AD1C}) = a2
# A Custom type with specific dimensionality
struct AD2Dim{T} <: ArrayData{T,2}
data::Array{T,2}
end
struct AD2DimStyle <: Broadcast.AbstractArrayStyle{2}; end
AD2DimStyle(::Val{2}) = AD2DimStyle()
AD2DimStyle(::Val{N}) where {N} = Broadcast.DefaultArrayStyle{N}()
Base.similar(bc::Broadcast.Broadcasted{AD2DimStyle}, ::Type{T}) where {T} =
AD2Dim(Array{T}(undef, size(bc)))
Base.BroadcastStyle(::Type{T}) where {T<:AD2Dim} = AD2DimStyle()
@testset "broadcasting for custom AbstractArray" begin
a = randn(10)
aa = Array19745(a)
fadd(aa) = aa .+ 1
fadd2(aa) = aa .+ 1 .* 2
fprod(aa) = aa .* aa'
@test a .+ 1 == @inferred(fadd(aa))
@test a .+ 1 .* 2 == @inferred(fadd2(aa))
@test a .* a' == @inferred(fprod(aa))
@test isa(aa .+ 1, Array19745)
@test isa(aa .+ 1 .* 2, Array19745)
@test isa(aa .* aa', Array19745)
a1 = AD1(rand(2,3))
a2 = AD2(rand(2))
@test a1 .+ 1 isa AD1
@test a2 .+ 1 isa AD2
@test a1 .+ 1 .* 2 isa AD1
@test a2 .+ 1 .* 2 isa AD2
@test a1 .+ a2 isa Array
@test a2 .+ a1 isa Array
@test a1 .+ a2 .+ a1 isa Array
@test a1 .+ a2 .+ a2 isa Array
a1 = AD1P(rand(2,3))
a2 = AD2P(rand(2))
@test a1 .+ 1 isa AD1P
@test a2 .+ 1 isa AD2P
@test a1 .+ 1 .* 2 isa AD1P
@test a2 .+ 1 .* 2 isa AD2P
@test a1 .+ a2 isa AD1P
@test a2 .+ a1 isa AD1P
@test a1 .+ a2 .+ a1 isa AD1P
@test a1 .+ a2 .+ a2 isa AD1P
a1 = AD1B(rand(2,3))
a2 = AD2B(rand(2))
@test a1 .+ 1 isa AD1B
@test a2 .+ 1 isa AD2B
@test a1 .+ 1 .* 2 isa AD1B
@test a2 .+ 1 .* 2 isa AD2B
@test a1 .+ a2 isa AD1B
@test a2 .+ a1 isa AD1B
@test a1 .+ a2 .+ a1 isa AD1B
@test a1 .+ a2 .+ a2 isa AD1B
a1 = AD1C(rand(2,3))
a2 = AD2C(rand(2))
@test a1 .+ 1 isa AD1C
@test a2 .+ 1 isa AD2C
@test a1 .+ 1 .* 2 isa AD1C
@test a2 .+ 1 .* 2 isa AD2C
@test_throws ErrorException a1 .+ a2
a2d = AD2Dim(rand(2, 3))
a2 = AD2(rand(2))
@test a2d .+ 1 isa AD2Dim
@test a2d .+ a2 isa Matrix
@test a2d .+ (1:2) isa AD2Dim
@test a2d .+ ones(2, 3) isa AD2Dim
@test a2d .+ ones(2, 3, 4) isa Array{Float64, 3}
end
# broadcast should only "peel off" one container layer
@test getindex.([Ref(1), Ref(2)]) == [1, 2]
let io = IOBuffer()
broadcast(x -> print(io, x), [Ref(1.0)])
@test String(take!(io)) == "Base.RefValue{Float64}(1.0)"
end
# Test that broadcast's promotion mechanism handles closures accepting more than one argument.
# (See issue #19641 and referenced issues and pull requests.)
let f() = (a = 1; Broadcast.combine_eltypes((x, y) -> x + y + a, (1.0, 1.0)))
@test @inferred(f()) == Float64
end
@testset "broadcast resulting in BitArray" begin
let f(x) = x ? true : "false"
ba = f.([true])
@test ba isa BitArray
@test ba == [true]
a = f.([false])
@test a isa Array{String}
@test a == ["false"]
@test f.([true, false]) == [true, "false"]
end
end
# Test that broadcast treats type arguments as scalars, i.e. containertype yields Any,
# even for subtypes of abstract array. (https://github.com/JuliaStats/DataArrays.jl/issues/229)
@testset "treat type arguments as scalars, DataArrays issue 229" begin
@test Broadcast.combine_styles(Broadcast.broadcastable(AbstractArray)) == Base.Broadcast.DefaultArrayStyle{0}()
@test broadcast(==, [1], AbstractArray) == BitArray([false])
@test broadcast(==, 1, AbstractArray) == false
end
@testset "broadcasting falls back to iteration (issues #26421, #19577, #23746)" begin
@test_throws ArgumentError broadcast(identity, Dict(1=>2))
@test_throws ArgumentError broadcast(identity, (a=1, b=2))
@test_throws ArgumentError length.(Dict(1 => BitSet(1:2), 2 => BitSet(1:3)))
@test_throws MethodError broadcast(identity, Base)
@test broadcast(identity, Iterators.filter(iseven, 1:10)) == 2:2:10
d = Dict([1,2] => 1.1, [3,2] => 0.1)
@test length.(keys(d)) == [2,2]
@test Set(exp.(Set([1,2,3]))) == Set(exp.([1,2,3]))
end
# Test that broadcasting identity where the input and output Array shapes do not match
# yields the correct result, not merely a partial copy. See pull request #19895 for discussion.
let N = 5
@test iszero(fill(1, N, N) .= zeros(N, N))
@test iszero(fill(1, N, N) .= zeros(N, 1))
@test iszero(fill(1, N, N) .= zeros(1, N))
@test iszero(fill(1, N, N) .= zeros(1, 1))
end
@testset "test broadcast for matrix of matrices" begin
A = fill([0 0; 0 0], 4, 4)
A[1:3,1:3] .= [[1 1; 1 1]]
@test all(A[1:3,1:3] .== [[1 1; 1 1]])
end
# Test that broadcast does not confuse eltypes. See also
# https://github.com/JuliaLang/julia/issues/21325
@testset "eltype confusion (#21325)" begin
foo(x::Char, y::Int) = 0
foo(x::String, y::Int) = "hello"
@test broadcast(foo, "x", [1, 2, 3]) == ["hello", "hello", "hello"]
@test isequal(
[Set([1]), Set([2])] .∪ Ref(Set([3])),
[Set([1, 3]), Set([2, 3])])
end
# A bare bones custom type that supports broadcast
struct Foo26601{T}
data::T
end
Base.axes(f::Foo26601) = axes(f.data)
Base.getindex(f::Foo26601, i...) = getindex(f.data, i...)
Base.ndims(::Type{Foo26601{T}}) where {T} = ndims(T)
Base.Broadcast.broadcastable(f::Foo26601) = f
@testset "barebones custom object broadcasting" begin
for d in (rand(Float64, ()), rand(5), rand(5,5), rand(5,5,5))
f = Foo26601(d)
@test f .* 2 == d .* 2
@test f .* (1:5) == d .* (1:5)
@test f .* reshape(1:25,5,5) == d .* reshape(1:25,5,5)
@test sqrt.(f) == sqrt.(d)
@test f .* (1,2,3,4,5) == d .* (1,2,3,4,5)
end
end
@testset "broadcast resulting in tuples" begin
# Issue #21291
let t = (0, 1, 2)
o = 1
@test @inferred(broadcast(+, t, o)) == (1, 2, 3)
end
# Issue #23647
@test (1, 2, 3) .+ (1,) == (1,) .+ (1, 2, 3) == (2, 3, 4)
@test (1,) .+ () == () .+ (1,) == () .+ () == ()
@test (1, 2) .+ (1, 2) == (2, 4)
@test_throws DimensionMismatch (1, 2) .+ (1, 2, 3)
end
@testset "broadcasted assignment from tuples and tuple styles (#33020)" begin
a = zeros(3)
@test_throws DimensionMismatch a .= (1,2)
@test_throws DimensionMismatch a .= sqrt.((1,2))
a .= (1,)
@test all(==(1), a)
a .= sqrt.((2,))
@test all(==(√2), a)
a = zeros(3, 2)
@test_throws DimensionMismatch a .= (1,2)
@test_throws DimensionMismatch a .= sqrt.((1,2))
a .= (1,)
@test all(==(1), a)
a .= sqrt.((2,))
@test all(==(√2), a)
a .= (1,2,3)
@test a == [1 1; 2 2; 3 3]
end
@testset "scalar .=" begin
A = [[1,2,3],4:5,6]
A[1] .= 0
@test A[1] == [0,0,0]
@test_throws ErrorException A[2] .= 0
@test_throws MethodError A[3] .= 0
A = [[1,2,3],4:5]
A[1] .= 0
@test A[1] == [0,0,0]
@test_throws ErrorException A[2] .= 0
end
# Issue #22180
@test convert.(Any, [1, 2]) == [1, 2]
# Issue #24944
let n = 1
@test ceil.(Int, n ./ (1,)) == (1,)
@test ceil.(Int, 1 ./ (1,)) == (1,)
end
# Issue #29266
@testset "deprecated scalar-fill .=" begin
a = fill(1, 10)
@test_throws ArgumentError a[1:5] = 0
x = randn(10)
@test_throws ArgumentError x[x .> 0.0] = 0.0
end
# lots of splatting!
let x = [[1, 4], [2, 5], [3, 6]]
y = .+(x..., .*(x..., x...)..., x[1]..., x[2]..., x[3]...)
@test y == [14463, 14472]
z = zeros(2)
z .= .+(x..., .*(x..., x...)..., x[1]..., x[2]..., x[3]...)
@test z == Float64[14463, 14472]
end
# Issue #21094
@generated function foo21094(out, x)
quote
out .= x .+ x
out
end
end
@test foo21094([0.0], [1.0]) == [2.0]
# Issue #22053
struct T22053
t
end
Broadcast.BroadcastStyle(::Type{T22053}) = Broadcast.Style{T22053}()
Broadcast.axes(::T22053) = ()
Broadcast.broadcastable(t::T22053) = t
function Base.copy(bc::Broadcast.Broadcasted{Broadcast.Style{T22053}})
all(x->isa(x, T22053), bc.args) && return 1
return 0
end
Base.:*(::T22053, ::T22053) = 2
let x = T22053(1)
@test x*x == 2
@test x.*x == 1
end
# Issue https://github.com/JuliaLang/julia/pull/25377#discussion_r159956996
let X = Any[1,2]
X .= nothing
@test X[1] == X[2] == nothing
end
# Ensure that broadcast styles with custom indexing work
let X = zeros(2, 3)
X .= (1, 2)
@test X == [1 1 1; 2 2 2]
end
# issue #27988: inference of Broadcast.flatten
using .Broadcast: Broadcasted
let
bc = Broadcasted(+, (Broadcasted(*, (1, 2)), Broadcasted(*, (Broadcasted(*, (3, 4)), 5))))
@test @inferred(Broadcast.cat_nested(bc)) == (1,2,3,4,5)
@test @inferred(Broadcast.materialize(Broadcast.flatten(bc))) == @inferred(Broadcast.materialize(bc)) == 62
bc = Broadcasted(+, (Broadcasted(*, (1, Broadcasted(/, (2.0, 2.5)))), Broadcasted(*, (Broadcasted(*, (3, 4)), 5))))
@test @inferred(Broadcast.cat_nested(bc)) == (1,2.0,2.5,3,4,5)
@test @inferred(Broadcast.materialize(Broadcast.flatten(bc))) == @inferred(Broadcast.materialize(bc)) == 60.8
end
let
bc = Broadcasted(+, (Broadcasted(*, ([1, 2, 3], 4)), 5))
@test isbits(Broadcast.flatten(bc).f)
end
# Issue #26127: multiple splats in a fused dot-expression
let f(args...) = *(args...)
x, y, z = (1,2), 3, (4, 5)
@test f.(x..., y, z...) == broadcast(f, x..., y, z...) == 120
@test f.(x..., f.(x..., y, z...), y, z...) == broadcast(f, x..., broadcast(f, x..., y, z...), y, z...) == 120*120
end
@testset "Issue #27911: Broadcasting over collections with big indices" begin
@test iszero.(Int128(0):Int128(2)) == [true, false, false]
@test iszero.((Int128(0):Int128(2)) .- 1) == [false, true, false]
@test iszero.(big(0):big(2)) == [true, false, false]
@test iszero.((big(0):big(2)) .- 1) == [false, true, false]
end
@testset "Issue #27775: Broadcast!ing over nested scalar operations" begin
a = zeros(2)
a .= 1 ./ (1 + 2)
@test a == [1/3, 1/3]
a .= 1 ./ (1 .+ 3)
@test a == [1/4, 1/4]
a .= sqrt.(1 ./ 2)
@test a == [sqrt(1/2), sqrt(1/2)]
rng = MersenneTwister(1234)
a .= rand.((rng,))
rng = MersenneTwister(1234)
@test a == [rand(rng), rand(rng)]
@test a[1] != a[2]
rng = MersenneTwister(1234)
broadcast!(rand, a, (rng,))
rng = MersenneTwister(1234)
@test a == [rand(rng), rand(rng)]
@test a[1] != a[2]
end
# Issue #27446: Broadcasting pair operator
let
c = ["foo", "bar"]
d = [1,2]
@test Dict(c .=> d) == Dict("foo" => 1, "bar" => 2)
end
# Broadcasted iterable/indexable APIs
let
bc = Broadcast.instantiate(Broadcast.broadcasted(+, zeros(5), 5))
@test IndexStyle(bc) == IndexLinear()
@test eachindex(bc) === Base.OneTo(5)
@test length(bc) === 5
@test ndims(bc) === 1
@test ndims(typeof(bc)) === 1
@test bc[1] === bc[CartesianIndex((1,))] === 5.0
@test copy(bc) == [v for v in bc] == collect(bc)
@test eltype(copy(bc)) == eltype([v for v in bc]) == eltype(collect(bc))
@test ndims(copy(bc)) == ndims([v for v in bc]) == ndims(collect(bc)) == ndims(bc)
bc = Broadcast.instantiate(Broadcast.broadcasted(+, zeros(5), 5*ones(1, 4)))
@test IndexStyle(bc) == IndexCartesian()
@test eachindex(bc) === CartesianIndices((Base.OneTo(5), Base.OneTo(4)))
@test length(bc) === 20
@test ndims(bc) === 2
@test ndims(typeof(bc)) === 2
@test bc[1,1] == bc[CartesianIndex((1,1))] === 5.0
@test copy(bc) == [v for v in bc] == collect(bc)
@test eltype(copy(bc)) == eltype([v for v in bc]) == eltype(collect(bc))
@test ndims(copy(bc)) == ndims([v for v in bc]) == ndims(collect(bc)) == ndims(bc)
end
# issue #31295
let a = rand(5), b = rand(5), c = copy(a)
view(identity(a), 1:3) .+= view(b, 1:3)
@test a == [(c+b)[1:3]; c[4:5]]
x = [1]
x[[1,1]] .+= 1
@test x == [2]
end
@testset "broadcasted mapreduce" begin
xs = 1:10
ys = 1:2:20
bc = Broadcast.instantiate(Broadcast.broadcasted(*, xs, ys))
@test IndexStyle(bc) == IndexLinear()
@test sum(bc) == mapreduce(Base.splat(*), +, zip(xs, ys))
xs2 = reshape(xs, 1, :)
ys2 = reshape(ys, 1, :)
bc = Broadcast.instantiate(Broadcast.broadcasted(*, xs2, ys2))
@test IndexStyle(bc) == IndexCartesian()
@test sum(bc) == mapreduce(Base.splat(*), +, zip(xs, ys))
xs = 1:5:3*5
ys = 1:4:3*4
bc = Broadcast.instantiate(
Broadcast.broadcasted(iseven, Broadcast.broadcasted(-, xs, ys)))
@test count(bc) == count(iseven, map(-, xs, ys))
xs = reshape(1:6, (2, 3))
ys = 1:2
bc = Broadcast.instantiate(Broadcast.broadcasted(*, xs, ys))
@test reduce(+, bc; dims=1, init=0) == [5 11 17]
# Let's test that `Broadcasted` actually hits the efficient
# `mapreduce` method as intended. We are going to invoke `reduce`
# with this *NON-ASSOCIATIVE* binary operator to see what
# associativity is chosen by the implementation:
paren = (x, y) -> "($x,$y)"
# Next, we construct data `xs` such that `length(xs)` is greater
# than short array cutoff of `_mapreduce`:
alphabets = 'a':'z'
blksize = Base.pairwise_blocksize(identity, paren) ÷ length(alphabets)
xs = repeat(alphabets, 2 * blksize)
@test length(xs) > blksize
# So far we constructed the data `xs` and reducing function
# `paren` such that `reduce` and `foldl` results are different.
# That is to say, this `reduce` does not hit the fall-back `foldl`
# branch:
@test foldl(paren, xs) != reduce(paren, xs)
# Now let's try it with `Broadcasted`:
bcraw = Broadcast.broadcasted(identity, xs)
bc = Broadcast.instantiate(bcraw)
# If `Broadcasted` has `IndexLinear` style, it should hit the
# `reduce` branch:
@test IndexStyle(bc) == IndexLinear()
@test reduce(paren, bc) == reduce(paren, xs)
# If `Broadcasted` does not have `IndexLinear` style, it should
# hit the `foldl` branch:
@test IndexStyle(bcraw) == IndexCartesian()
@test reduce(paren, bcraw) == foldl(paren, xs)
end
# treat Pair as scalar:
@test replace.(split("The quick brown fox jumps over the lazy dog"), r"[aeiou]"i => "_") ==
["Th_", "q__ck", "br_wn", "f_x", "j_mps", "_v_r", "th_", "l_zy", "d_g"]
# 28680
@test 1 .+ 1 .+ (1, 2) == (3, 4)
# PR #35260 no allocations in simple broadcasts
u = rand(100)
k1 = similar(u)
k2 = similar(u)
k3 = similar(u)
k4 = similar(u)
f(a,b,c,d,e) = @. a = a + 1*(b+c+d+e)
@allocated f(u,k1,k2,k3,k4)
@test (@allocated f(u,k1,k2,k3,k4)) == 0
ret = @macroexpand @.([Int, Number] <: Real)
@test ret == :([Int, Number] .<: Real)
ret = @macroexpand @.([Int, Number] >: Real)
@test ret == :([Int, Number] .>: Real)