John Benediktsson
parent
298bbddeb1
commit
d27c259928
extra/tensors
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@ -1,50 +1,28 @@
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! Copyright (C) 2019 HMC Clinic.
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! See http://factorcode.org/license.txt for BSD license.
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USING: arrays io kernel locals math prettyprint tensors tools.time ;
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USING: arrays kernel locals math math.functions math.statistics memory
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sequences tools.time ;
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IN: tensors.benchmark
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<PRIVATE
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! puts items from els (a quotation) on stack, runs ops (a quot w no stack effect) n times
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! returns an array with times (ns) for each trial
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:: benchmark-multiple ( els: ( -- .. ) op: ( .. -- .. ) n -- ..arr )
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! put els on stack
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els call
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! create array
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n 0 <array> :> arr
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! perform op n times
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n [ gc [ op benchmark ] dip arr set-nth ] each-integer
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arr ; inline
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:: add-tensors ( trials elems -- time )
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! Create the arrays to be added
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elems 1array naturals dup
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! Benchmark!
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[ trials [ 2dup t+ drop ] times ] benchmark
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! Normalize
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trials / >float
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nip nip ;
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:: matmul-tensors ( trials elems -- time )
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! Create the arrays to be multiplied
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elems elems 2array naturals dup
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! Benchmark!
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[ trials [ 2dup matmul drop ] times ] benchmark
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! Normalize
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trials / >float
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nip nip ;
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:: transpose-tensor ( trials elems -- time )
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! Create the array to be transposed
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elems elems 2array naturals
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! benchmark
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[ trials [ dup transpose drop ] times ] benchmark
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! Normalize
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trials / >float
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nip ;
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PRIVATE>
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: run-benchmarks ( -- )
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"Benchmarking the tensors vocabulary" print
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"Add two 100 element tensors" print
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1000000 100 add-tensors .
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"Add two 100,000 element tensors" print
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10000 100000 add-tensors .
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"Multiply two 10x10 matrices" print
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100000 10 matmul-tensors .
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"Multiply two 100x100 matrices" print
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1000 100 matmul-tensors .
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"Transpose a 10x10 matrix" print
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10000 10 transpose-tensor .
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"Transpose a 100x100 matrix" print
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10 100 transpose-tensor . ;
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! finds the confidence interval of seq with significance level 95
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:: confidence-interval ( seq -- {c1,c2} )
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seq mean :> m
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! HARDCODING ALERT: z value for alpha = 95 is 1.96
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seq sample-std 1.96 *
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! div by sqrt(n)
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seq length sqrt / :> modifier
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m modifier -
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m modifier +
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2array ;
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@ -0,0 +1,506 @@
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|
||||
13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17
|
||||
11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27
|
||||
14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05
|
||||
15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45
|
||||
13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02
|
||||
9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88
|
||||
22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11
|
||||
9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52
|
||||
5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59
|
||||
9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85
|
||||
12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79
|
||||
10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98
|
||||
6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79
|
||||
9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44
|
||||
9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13
|
||||
7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31
|
||||
6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44
|
||||
5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73
|
||||
5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27
|
||||
8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74
|
||||
9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71
|
||||
4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13
|
||||
4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01
|
||||
8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94
|
||||
7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23
|
||||
6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7
|
||||
4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42
|
||||
3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65
|
||||
6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99
|
||||
5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29
|
||||
7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22
|
||||
3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13
|
||||
3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15
|
||||
4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32
|
||||
15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13
|
||||
13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76
|
||||
4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29
|
||||
4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87
|
||||
3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36
|
||||
4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66
|
||||
8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14
|
||||
6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1
|
||||
4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68
|
||||
15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91
|
||||
10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03
|
||||
14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11
|
||||
5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74
|
||||
5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74
|
||||
5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01
|
||||
2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42
|
||||
2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34
|
||||
3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58
|
||||
5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98
|
||||
4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45
|
||||
0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06
|
||||
0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97
|
||||
0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68
|
||||
0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07
|
||||
0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35
|
||||
0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01
|
||||
0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59
|
||||
0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6
|
||||
0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14
|
||||
0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1
|
||||
0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92
|
||||
0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1
|
||||
0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33
|
||||
0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67
|
||||
0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08
|
||||
0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64
|
||||
0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48
|
||||
0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88
|
||||
|
|
|
@ -0,0 +1,73 @@
|
|||
! Copyright (C) 2019 HMC Clinic.
|
||||
! See http://factorcode.org/license.txt for BSD license.
|
||||
! Code based on https://towardsdatascience.com/linear-regression-from-scratch-with-numpy-implementation-finally-8e617d8e274c
|
||||
|
||||
USING: arrays accessors csv io io.encodings.utf8 kernel locals math math.parser
|
||||
math.ranges math.statistics prettyprint sequences tensors ;
|
||||
IN: tensors.demos
|
||||
|
||||
<PRIVATE
|
||||
|
||||
! Normalize across each of the features
|
||||
:: normalize ( X -- norm )
|
||||
! Compute the mean for each of the features and repeat it so that it can be
|
||||
! combined with X
|
||||
X transpose tensor>array :> X-T
|
||||
X-T [ mean ] map >tensor :> feat-means
|
||||
X shape>> first [0,b) [ drop feat-means ] map stack :> means
|
||||
! Compute the std for each of the features and repeat it so that it can be
|
||||
! combined with X
|
||||
X-T [ std ] map >tensor :> feat-stds
|
||||
X shape>> first [0,b) [ drop feat-stds ] map stack :> stds
|
||||
X means t- stds t/ ;
|
||||
|
||||
:: compute-cost ( X y params -- cost )
|
||||
! Compute (1/(2*n_samples))
|
||||
1 2 y shape>> first * /
|
||||
! Compute h
|
||||
X params matmul
|
||||
! Compute sum((h-y)**2)
|
||||
y t- dup t* sum
|
||||
! Multiply to get final cost
|
||||
* ;
|
||||
|
||||
:: gradient-descent ( X y params lr n-iters -- history params )
|
||||
lr y shape>> first / :> batch-lr
|
||||
{ n-iters } zeros :> history
|
||||
X transpose :> X-T
|
||||
params
|
||||
n-iters [
|
||||
! Update params with
|
||||
! params = params - (learning_rate/n_samples) * X.T @ (X @ params - y)
|
||||
swap dup :> old-params
|
||||
batch-lr X-T X old-params matmul y t- matmul t* t- :> new-params
|
||||
! Compute the cost and add it to the history
|
||||
X y new-params compute-cost swap history set-nth
|
||||
new-params
|
||||
] each-integer
|
||||
history swap ;
|
||||
|
||||
PRIVATE>
|
||||
|
||||
:: linear-regression ( X y lr n-iters -- )
|
||||
X normalize
|
||||
! Add the constant coefficient
|
||||
y shape>> first 1 2array ones swap 2array hstack :> X-norm
|
||||
! Create the array of parameters
|
||||
X-norm shape>> second 1 2array zeros :> params
|
||||
! Compute the initial cost
|
||||
X-norm y params compute-cost
|
||||
! Print!
|
||||
number>string "The initial cost is " swap append print
|
||||
! Perform gradient descent
|
||||
X-norm y params lr n-iters gradient-descent
|
||||
"The optimal parameters are " print .
|
||||
last number>string "The final cost was " swap append print
|
||||
;
|
||||
|
||||
! Load and return the boston house-prices dataset
|
||||
: load-boston-data ( -- X y )
|
||||
"vocab:tensors/demos/data.csv" utf8 file>csv
|
||||
[ [ string>number ] map ] map >tensor
|
||||
"vocab:tensors/demos/target.csv" utf8 file>csv
|
||||
[ [ string>number ] map ] map >tensor ;
|
||||
|
|
@ -0,0 +1,506 @@
|
|||
24
|
||||
21.6
|
||||
34.7
|
||||
33.4
|
||||
36.2
|
||||
28.7
|
||||
22.9
|
||||
27.1
|
||||
16.5
|
||||
18.9
|
||||
15
|
||||
18.9
|
||||
21.7
|
||||
20.4
|
||||
18.2
|
||||
19.9
|
||||
23.1
|
||||
17.5
|
||||
20.2
|
||||
18.2
|
||||
13.6
|
||||
19.6
|
||||
15.2
|
||||
14.5
|
||||
15.6
|
||||
13.9
|
||||
16.6
|
||||
14.8
|
||||
18.4
|
||||
21
|
||||
12.7
|
||||
14.5
|
||||
13.2
|
||||
13.1
|
||||
13.5
|
||||
18.9
|
||||
20
|
||||
21
|
||||
24.7
|
||||
30.8
|
||||
34.9
|
||||
26.6
|
||||
25.3
|
||||
24.7
|
||||
21.2
|
||||
19.3
|
||||
20
|
||||
16.6
|
||||
14.4
|
||||
19.4
|
||||
19.7
|
||||
20.5
|
||||
25
|
||||
23.4
|
||||
18.9
|
||||
35.4
|
||||
24.7
|
||||
31.6
|
||||
23.3
|
||||
19.6
|
||||
18.7
|
||||
16
|
||||
22.2
|
||||
25
|
||||
33
|
||||
23.5
|
||||
19.4
|
||||
22
|
||||
17.4
|
||||
20.9
|
||||
24.2
|
||||
21.7
|
||||
22.8
|
||||
23.4
|
||||
24.1
|
||||
21.4
|
||||
20
|
||||
20.8
|
||||
21.2
|
||||
20.3
|
||||
28
|
||||
23.9
|
||||
24.8
|
||||
22.9
|
||||
23.9
|
||||
26.6
|
||||
22.5
|
||||
22.2
|
||||
23.6
|
||||
28.7
|
||||
22.6
|
||||
22
|
||||
22.9
|
||||
25
|
||||
20.6
|
||||
28.4
|
||||
21.4
|
||||
38.7
|
||||
43.8
|
||||
33.2
|
||||
27.5
|
||||
26.5
|
||||
18.6
|
||||
19.3
|
||||
20.1
|
||||
19.5
|
||||
19.5
|
||||
20.4
|
||||
19.8
|
||||
19.4
|
||||
21.7
|
||||
22.8
|
||||
18.8
|
||||
18.7
|
||||
18.5
|
||||
18.3
|
||||
21.2
|
||||
19.2
|
||||
20.4
|
||||
19.3
|
||||
22
|
||||
20.3
|
||||
20.5
|
||||
17.3
|
||||
18.8
|
||||
21.4
|
||||
15.7
|
||||
16.2
|
||||
18
|
||||
14.3
|
||||
19.2
|
||||
19.6
|
||||
23
|
||||
18.4
|
||||
15.6
|
||||
18.1
|
||||
17.4
|
||||
17.1
|
||||
13.3
|
||||
17.8
|
||||
14
|
||||
14.4
|
||||
13.4
|
||||
15.6
|
||||
11.8
|
||||
13.8
|
||||
15.6
|
||||
14.6
|
||||
17.8
|
||||
15.4
|
||||
21.5
|
||||
19.6
|
||||
15.3
|
||||
19.4
|
||||
17
|
||||
15.6
|
||||
13.1
|
||||
41.3
|
||||
24.3
|
||||
23.3
|
||||
27
|
||||
50
|
||||
50
|
||||
50
|
||||
22.7
|
||||
25
|
||||
50
|
||||
23.8
|
||||
23.8
|
||||
22.3
|
||||
17.4
|
||||
19.1
|
||||
23.1
|
||||
23.6
|
||||
22.6
|
||||
29.4
|
||||
23.2
|
||||
24.6
|
||||
29.9
|
||||
37.2
|
||||
39.8
|
||||
36.2
|
||||
37.9
|
||||
32.5
|
||||
26.4
|
||||
29.6
|
||||
50
|
||||
32
|
||||
29.8
|
||||
34.9
|
||||
37
|
||||
30.5
|
||||
36.4
|
||||
31.1
|
||||
29.1
|
||||
50
|
||||
33.3
|
||||
30.3
|
||||
34.6
|
||||
34.9
|
||||
32.9
|
||||
24.1
|
||||
42.3
|
||||
48.5
|
||||
50
|
||||
22.6
|
||||
24.4
|
||||
22.5
|
||||
24.4
|
||||
20
|
||||
21.7
|
||||
19.3
|
||||
22.4
|
||||
28.1
|
||||
23.7
|
||||
25
|
||||
23.3
|
||||
28.7
|
||||
21.5
|
||||
23
|
||||
26.7
|
||||
21.7
|
||||
27.5
|
||||
30.1
|
||||
44.8
|
||||
50
|
||||
37.6
|
||||
31.6
|
||||
46.7
|
||||
31.5
|
||||
24.3
|
||||
31.7
|
||||
41.7
|
||||
48.3
|
||||
29
|
||||
24
|
||||
25.1
|
||||
31.5
|
||||
23.7
|
||||
23.3
|
||||
22
|
||||
20.1
|
||||
22.2
|
||||
23.7
|
||||
17.6
|
||||
18.5
|
||||
24.3
|
||||
20.5
|
||||
24.5
|
||||
26.2
|
||||
24.4
|
||||
24.8
|
||||
29.6
|
||||
42.8
|
||||
21.9
|
||||
20.9
|
||||
44
|
||||
50
|
||||
36
|
||||
30.1
|
||||
33.8
|
||||
43.1
|
||||
48.8
|
||||
31
|
||||
36.5
|
||||
22.8
|
||||
30.7
|
||||
50
|
||||
43.5
|
||||
20.7
|
||||
21.1
|
||||
25.2
|
||||
24.4
|
||||
35.2
|
||||
32.4
|
||||
32
|
||||
33.2
|
||||
33.1
|
||||
29.1
|
||||
35.1
|
||||
45.4
|
||||
35.4
|
||||
46
|
||||
50
|
||||
32.2
|
||||
22
|
||||
20.1
|
||||
23.2
|
||||
22.3
|
||||
24.8
|
||||
28.5
|
||||
37.3
|
||||
27.9
|
||||
23.9
|
||||
21.7
|
||||
28.6
|
||||
27.1
|
||||
20.3
|
||||
22.5
|
||||
29
|
||||
24.8
|
||||
22
|
||||
26.4
|
||||
33.1
|
||||
36.1
|
||||
28.4
|
||||
33.4
|
||||
28.2
|
||||
22.8
|
||||
20.3
|
||||
16.1
|
||||
22.1
|
||||
19.4
|
||||
21.6
|
||||
23.8
|
||||
16.2
|
||||
17.8
|
||||
19.8
|
||||
23.1
|
||||
21
|
||||
23.8
|
||||
23.1
|
||||
20.4
|
||||
18.5
|
||||
25
|
||||
24.6
|
||||
23
|
||||
22.2
|
||||
19.3
|
||||
22.6
|
||||
19.8
|
||||
17.1
|
||||
19.4
|
||||
22.2
|
||||
20.7
|
||||
21.1
|
||||
19.5
|
||||
18.5
|
||||
20.6
|
||||
19
|
||||
18.7
|
||||
32.7
|
||||
16.5
|
||||
23.9
|
||||
31.2
|
||||
17.5
|
||||
17.2
|
||||
23.1
|
||||
24.5
|
||||
26.6
|
||||
22.9
|
||||
24.1
|
||||
18.6
|
||||
30.1
|
||||
18.2
|
||||
20.6
|
||||
17.8
|
||||
21.7
|
||||
22.7
|
||||
22.6
|
||||
25
|
||||
19.9
|
||||
20.8
|
||||
16.8
|
||||
21.9
|
||||
27.5
|
||||
21.9
|
||||
23.1
|
||||
50
|
||||
50
|
||||
50
|
||||
50
|
||||
50
|
||||
13.8
|
||||
13.8
|
||||
15
|
||||
13.9
|
||||
13.3
|
||||
13.1
|
||||
10.2
|
||||
10.4
|
||||
10.9
|
||||
11.3
|
||||
12.3
|
||||
8.8
|
||||
7.2
|
||||
10.5
|
||||
7.4
|
||||
10.2
|
||||
11.5
|
||||
15.1
|
||||
23.2
|
||||
9.7
|
||||
13.8
|
||||
12.7
|
||||
13.1
|
||||
12.5
|
||||
8.5
|
||||
5
|
||||
6.3
|
||||
5.6
|
||||
7.2
|
||||
12.1
|
||||
8.3
|
||||
8.5
|
||||
5
|
||||
11.9
|
||||
27.9
|
||||
17.2
|
||||
27.5
|
||||
15
|
||||
17.2
|
||||
17.9
|
||||
16.3
|
||||
7
|
||||
7.2
|
||||
7.5
|
||||
10.4
|
||||
8.8
|
||||
8.4
|
||||
16.7
|
||||
14.2
|
||||
20.8
|
||||
13.4
|
||||
11.7
|
||||
8.3
|
||||
10.2
|
||||
10.9
|
||||
11
|
||||
9.5
|
||||
14.5
|
||||
14.1
|
||||
16.1
|
||||
14.3
|
||||
11.7
|
||||
13.4
|
||||
9.6
|
||||
8.7
|
||||
8.4
|
||||
12.8
|
||||
10.5
|
||||
17.1
|
||||
18.4
|
||||
15.4
|
||||
10.8
|
||||
11.8
|
||||
14.9
|
||||
12.6
|
||||
14.1
|
||||
13
|
||||
13.4
|
||||
15.2
|
||||
16.1
|
||||
17.8
|
||||
14.9
|
||||
14.1
|
||||
12.7
|
||||
13.5
|
||||
14.9
|
||||
20
|
||||
16.4
|
||||
17.7
|
||||
19.5
|
||||
20.2
|
||||
21.4
|
||||
19.9
|
||||
19
|
||||
19.1
|
||||
19.1
|
||||
20.1
|
||||
19.9
|
||||
19.6
|
||||
23.2
|
||||
29.8
|
||||
13.8
|
||||
13.3
|
||||
16.7
|
||||
12
|
||||
14.6
|
||||
21.4
|
||||
23
|
||||
23.7
|
||||
25
|
||||
21.8
|
||||
20.6
|
||||
21.2
|
||||
19.1
|
||||
20.6
|
||||
15.2
|
||||
7
|
||||
8.1
|
||||
13.6
|
||||
20.1
|
||||
21.8
|
||||
24.5
|
||||
23.1
|
||||
19.7
|
||||
18.3
|
||||
21.2
|
||||
17.5
|
||||
16.8
|
||||
22.4
|
||||
20.6
|
||||
23.9
|
||||
22
|
||||
11.9
|
||||
|
|
|
@ -1,27 +1,46 @@
|
|||
! Copyright (C) 2019 HMC Clinic.
|
||||
! See http://factorcode.org/license.txt for BSD license.
|
||||
USING: arrays help.markup help.syntax math sequences ;
|
||||
USING: arrays help.markup help.syntax lexer math sequences ;
|
||||
IN: tensors
|
||||
|
||||
ARTICLE: "tensors" "Tensors" "A " { $snippet "tensor" } " is a sequence "
|
||||
"of floating point numbers "
|
||||
ARTICLE: "tensors" "Tensors"
|
||||
"A " { $snippet "tensor" } " is a sequence of floating point numbers "
|
||||
"shaped into an n-dimensional matrix. It supports fast, scalable matrix "
|
||||
"operations such as matrix multiplication and transposition as well as a "
|
||||
"number of element-wise operations. Words for working with tensors are found "
|
||||
"in the " { $vocab-link "tensors" } " vocabulary." $nl $nl
|
||||
"Tensors can be created "
|
||||
"by calling one of four constructors:"
|
||||
{ $subsections zeros ones naturals arange }
|
||||
"They can be converted to the corresponding N-dimensional array with"
|
||||
{ $subsections tensor>array }
|
||||
"in the " { $vocab-link "tensors" } " vocabulary." $nl
|
||||
"More information about tensors can be found here:"
|
||||
{ $subsections "creation" "manipulation" } ;
|
||||
|
||||
ARTICLE: "creation" "Creating Tensors"
|
||||
"Tensors can be created by calling one of following constructors:"
|
||||
{ $subsections zeros ones naturals arange (tensor) }
|
||||
"They can be converted to/from the corresponding N-dimensional array with"
|
||||
{ $subsections tensor>array >tensor }
|
||||
"There is also a tensor parsing word"
|
||||
{ $subsections POSTPONE: t{ } ;
|
||||
|
||||
ARTICLE: "manipulation" "Manipulating Tensors"
|
||||
"The number of dimensions can be extracted with:"
|
||||
{ $subsections dims }
|
||||
"Additionally, tensors can be reshaped with:"
|
||||
"Tensors can be reshaped with:"
|
||||
{ $subsections reshape flatten }
|
||||
"Tensors can be combined element-wise with other tensors as well as numbers with:"
|
||||
{ $subsections t+ t- t* t/ t% }
|
||||
"Finally, tensors support the following matrix operations:"
|
||||
{ $subsections matmul transpose } ;
|
||||
"Tensors support the following matrix operations:"
|
||||
{ $subsections matmul transpose }
|
||||
"Tensors also support the following concatenation operations:"
|
||||
{ $subsections stack hstack vstack t-concat }
|
||||
"Tensors implement all " { $vocab-link "sequences" } " operations." $nl
|
||||
"Tensors can be indexed into using either numbers or arrays, for example:"
|
||||
{ $example
|
||||
"USING: prettyprint sequences tensors ;"
|
||||
"t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }"
|
||||
"[ { 1 1 } swap nth ] [ 4 swap nth ] bi = ."
|
||||
"t"
|
||||
}
|
||||
"If the array being used to index into the tensor has the wrong number "
|
||||
"of dimensions, a " { $link dimension-mismatch-error } " will be thrown." ;
|
||||
|
||||
ARTICLE: "tensor-operators" "Tensor Operators" "Info here" ;
|
||||
|
||||
|
|
@ -46,6 +65,34 @@ HELP: non-positive-shape-error
|
|||
", which allow users to directly set the shape of a " { $link tensor }
|
||||
", when the shape has zero or negative values." } ;
|
||||
|
||||
HELP: non-uniform-seq-error
|
||||
{ $values { "seq" sequence } }
|
||||
{ $description "Throws a " { $link non-uniform-seq-error } "." }
|
||||
{ $error-description "Thrown by operations such as " { $link >tensor }
|
||||
", which allow users to directly input the values of a " { $link tensor }
|
||||
" as a nested sequence, when the subsequences have varying lengths." } ;
|
||||
|
||||
HELP: dimension-mismatch-error
|
||||
{ $values { "tensor-dim" number } { "index-dim" number } }
|
||||
{ $description "Throws a " { $link dimension-mismatch-error } "." }
|
||||
{ $error-description "Thrown by indexing operations such as " { $link nth }
|
||||
" and " { $link set-nth } " if the array being used to index has a different number "
|
||||
"of dimensions than the tensor." } ;
|
||||
|
||||
HELP: t{
|
||||
{ $syntax "t{ elements... }" }
|
||||
{ $values { "elements" "a list of numbers" } }
|
||||
{ $description "Initializes a tensor with the given elements."
|
||||
" Preserves the shape of nested sequences. Assumes uniformly nested sequences." }
|
||||
{ $errors "Throws a " { $link non-uniform-seq-error } " if the given "
|
||||
"sequence have subsequences of varying lengths. Throws a "
|
||||
{ $link lexer-error } " if the given sequence is not uniformly nested." } ;
|
||||
|
||||
HELP: (tensor)
|
||||
{ $values { "shape" sequence } { "tensor" tensor } }
|
||||
{ $description "Creates a tensor with shape " { $snippet "shape" }
|
||||
" containing uninitialized values. Allows non-positive shapes." } ;
|
||||
|
||||
HELP: zeros
|
||||
{ $values { "shape" sequence } { "tensor" tensor } }
|
||||
{ $description "Initializes a tensor with shape " { $snippet "shape" }
|
||||
|
|
@ -87,6 +134,14 @@ HELP: dims
|
|||
{ $values { "tensor" tensor } { "n" integer } }
|
||||
{ $description "Returns the dimension of " { $snippet "tensor" } "." } ;
|
||||
|
||||
HELP: >tensor
|
||||
{ $values { "seq" sequence } { "tensor" tensor } }
|
||||
{ $description "Turns a nested sequence " { $snippet "seq" }
|
||||
" into a tensor of the corresponding shape. Assumes a uniformly nested sequence." }
|
||||
{ $errors "Throws a " { $link non-uniform-seq-error } " if the given "
|
||||
"sequence have subsequences of varying lengths. Throws a "
|
||||
{ $link lexer-error } " if the given sequence is not uniformly nested." } ;
|
||||
|
||||
HELP: t+
|
||||
{ $values { "x" { $or tensor number } } { "y" { $or tensor number } } { "tensor" tensor } }
|
||||
{ $description "Element-wise addition. Intakes two tensors or a tensor and a number (in either order)." }
|
||||
|
|
@ -131,6 +186,33 @@ HELP: matmul
|
|||
|
||||
HELP: transpose
|
||||
{ $values { "tensor" tensor } { "tensor'" tensor } }
|
||||
{ $description "Performs n-dimensional matrix transposition on " { $snippet "tens" } "." } ;
|
||||
{ $description "Performs n-dimensional matrix transposition on " { $snippet "tensor" } "." } ;
|
||||
|
||||
HELP: stack
|
||||
{ $values { "seq" sequence } { "tensor" tensor } }
|
||||
{ $description "Joins the sequences in " { $snippet "seq" } " along a new axis. "
|
||||
{ $snippet "tensor" } " will have one more dimension than the arrays in " { $snippet "seq" } "." }
|
||||
{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
|
||||
{ $snippet "seq" } " do not have the same shape."} ;
|
||||
|
||||
|
||||
HELP: hstack
|
||||
{ $values { "seq" sequence } { "tensor" tensor } }
|
||||
{ $description "Joins the sequences in " { $snippet "seq" } " column-wise." }
|
||||
{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
|
||||
{ $snippet "seq" } " do not have the same shape along all but the second axis."} ;
|
||||
|
||||
HELP: vstack
|
||||
{ $values { "seq" sequence } { "tensor" tensor } }
|
||||
{ $description "Joins the sequences in " { $snippet "seq" } " row-wise." }
|
||||
{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
|
||||
{ $snippet "seq" } " do not have the same shape along all but the first axis."} ;
|
||||
|
||||
HELP: t-concat
|
||||
{ $values { "seq" sequence } { "tensor" tensor } }
|
||||
{ $description "Joins the sequences in " { $snippet "seq" } " along the first axis." }
|
||||
{ $errors "Throws a " { $link shape-mismatch-error } " if the sequences in "
|
||||
{ $snippet "seq" } " do not have the same shape along all but the first axis."} ;
|
||||
|
||||
|
||||
ABOUT: "tensors"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
! Copyright (C) 2019 HMC Clinic.
|
||||
! See http://factorcode.org/license.txt for BSD license.
|
||||
USING: accessors alien.c-types kernel math math.order math.vectors
|
||||
USING: accessors alien.c-types arrays kernel math math.order math.vectors
|
||||
sequences specialized-arrays tensors tools.test ;
|
||||
QUALIFIED-WITH: alien.c-types c
|
||||
SPECIALIZED-ARRAY: c:float
|
||||
|
|
@ -106,6 +106,19 @@ IN: tensors.tests
|
|||
]
|
||||
[ { -3 5 } \ non-positive-shape-error boa = ] must-fail-with
|
||||
|
||||
! Test (tensor)
|
||||
{ { 2 4 } } [
|
||||
{ 2 4 } (tensor) shape>>
|
||||
] unit-test
|
||||
|
||||
{ { 0 } } [
|
||||
{ 0 } (tensor) shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ } } [
|
||||
{ 0 } (tensor) vec>>
|
||||
] unit-test
|
||||
|
||||
|
||||
! Test reshape
|
||||
{ float-array{ 0.0 0.0 0.0 0.0 } } [
|
||||
|
|
@ -156,6 +169,173 @@ IN: tensors.tests
|
|||
{ 1 2 3 } zeros dims
|
||||
] unit-test
|
||||
|
||||
! Test sequence operations
|
||||
! TODO: add tests for clone-like
|
||||
! test length
|
||||
{ 20 } [
|
||||
{ 2 2 5 } naturals length
|
||||
] unit-test
|
||||
|
||||
{ 0 } [
|
||||
t{ } length
|
||||
] unit-test
|
||||
|
||||
! test new-sequence
|
||||
{ 10 } [
|
||||
10 { 2 5 } ones new-sequence shape>> product
|
||||
] unit-test
|
||||
|
||||
{ 2 } [
|
||||
2 { 3 4 5 } ones new-sequence shape>> product
|
||||
] unit-test
|
||||
|
||||
{ 20 } [
|
||||
20 { 2 5 } ones new-sequence shape>> product
|
||||
] unit-test
|
||||
|
||||
! test nth
|
||||
{ 1.0 } [
|
||||
1 { 5 } naturals nth
|
||||
] unit-test
|
||||
|
||||
{ 1.0 } [
|
||||
{ 1 } { 5 } naturals nth
|
||||
] unit-test
|
||||
|
||||
{ 3.0 } [
|
||||
{ 1 1 } { 2 2 } naturals nth
|
||||
] unit-test
|
||||
|
||||
{ 5.0 } [
|
||||
{ 1 0 1 } { 2 2 2 } naturals nth
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 1 2 3 } t{ 1 2 3 } nth
|
||||
]
|
||||
[ 1 3 \ dimension-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! test set-nth
|
||||
{ t{ 1 5 3 } } [
|
||||
t{ 1 2 3 } dup [ 5 { 1 } ] dip set-nth
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0 1 } { 5 3 } } } [
|
||||
{ 2 2 } naturals dup [ 5 { 1 0 } ] dip set-nth
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0 1 } { 2 3 } } { { 4 10 } { 6 7 } } } } [
|
||||
{ 2 2 2 } naturals dup [ 10 { 1 0 1 } ] dip set-nth
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup [ 5 { 1 } ] dip set-nth
|
||||
]
|
||||
[ 2 1 \ dimension-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! test clone
|
||||
{ t{ 1 2 3 } } [
|
||||
t{ 1 2 3 } dup clone [ 5 1 ] dip set-nth
|
||||
] unit-test
|
||||
|
||||
{ t } [
|
||||
t{ 1 2 3 } dup clone =
|
||||
] unit-test
|
||||
|
||||
{ f } [
|
||||
t{ 1 2 3 } dup clone dup [ 5 1 ] dip set-nth =
|
||||
] unit-test
|
||||
|
||||
! Test like
|
||||
{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
|
||||
{ 2 3 } naturals dup like vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 2 3 } } [
|
||||
{ 2 3 } naturals dup like shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
|
||||
{ 0 1 2 3 4 5 } { 2 3 } naturals like vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 2 3 } } [
|
||||
{ 0 1 2 3 4 5 } { 2 3 } naturals like shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 0.0 1.0 2.0 3.0 4.0 5.0 } } [
|
||||
float-array{ 0 1 2 3 4 5 } { 2 3 } naturals like vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 2 3 } } [
|
||||
float-array{ 0 1 2 3 4 5 } { 2 3 } naturals like shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 0.0 1.0 2.0 3.0 4.0 } } [
|
||||
{ 0 1 2 3 4 } { 2 3 } naturals like vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 5 } } [
|
||||
{ 0 1 2 3 4 } { 2 3 } naturals like shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 0.0 1.0 2.0 3.0 4.0 } } [
|
||||
float-array{ 0 1 2 3 4 } { 2 3 } naturals like vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 5 } } [
|
||||
float-array{ 0 1 2 3 4 } { 2 3 } naturals like shape>>
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0.0 1.0 } { 2.0 3.0 } } } [
|
||||
{ { 0 1 } { 2 3 } } t{ } like
|
||||
] unit-test
|
||||
|
||||
! test clone-like
|
||||
{ float-array{ 1.0 2.0 3.0 } } [
|
||||
{ 1 2 3 } t{ } clone-like vec>>
|
||||
] unit-test
|
||||
|
||||
{ f } [
|
||||
float-array{ 1.0 2.0 3.0 } dup t{ } clone-like
|
||||
dup [ 5 1 ] dip set-nth vec>> =
|
||||
] unit-test
|
||||
|
||||
! Test sum
|
||||
{ 21.0 } [
|
||||
t{ 1 2 3 4 5 6 } sum
|
||||
] unit-test
|
||||
|
||||
{ 50005000.0 } [
|
||||
{ 100 100 } naturals 1 t+ sum
|
||||
] unit-test
|
||||
|
||||
! Test tensor parsing word
|
||||
{ float-array{ 1 2 3 4 5 6 7 8 } } [
|
||||
t{ 1 2 3 4 5 6 7 8 } vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 8 } } [
|
||||
t{ 1 2 3 4 5 6 7 8 } shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 1 2 3 4 5 6 7 8 } } [
|
||||
t{ { 1 2 3 4 } { 5 6 7 8 } } vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 2 4 } } [
|
||||
t{ { 1 2 3 4 } { 5 6 7 8 } } shape>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 } } [
|
||||
t{ { { 1 2 3 4 } { 5 6 7 8 } { 9 10 11 12 } } { { 13 14 15 16 } { 17 18 19 20 } { 21 22 23 24 } } } vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 2 3 4 } } [
|
||||
t{ { { 1 2 3 4 } { 5 6 7 8 } { 9 10 11 12 } } { { 13 14 15 16 } { 17 18 19 20 } { 21 22 23 24 } } } shape>>
|
||||
] unit-test
|
||||
|
||||
|
||||
! Test addition
|
||||
{ float-array{ 1.0 2.0 3.0 4.0 } } [
|
||||
{ 4 } naturals { 4 } ones t+ vec>>
|
||||
|
|
@ -461,6 +641,188 @@ IN: tensors.tests
|
|||
t% shape>>
|
||||
] unit-test
|
||||
|
||||
! t-concat
|
||||
{ t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } [
|
||||
{ 2 3 } naturals dup 2array t-concat
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } { 0.0 1.0 2.0 } { 3.0 4.0 5.0 }
|
||||
{ 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } [
|
||||
{ 2 3 } naturals dup dup 3array t-concat
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0.0 1.0 2.0 }
|
||||
{ 3.0 4.0 5.0 }
|
||||
{ 0.0 1.0 2.0 }
|
||||
{ 3.0 4.0 5.0 }
|
||||
{ 6.0 7.0 8.0 }
|
||||
{ 9.0 10.0 11.0 }
|
||||
{ 12.0 13.0 14.0 } } } [
|
||||
{ 2 3 } naturals { 5 3 } naturals 2array t-concat
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
|
||||
{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } [
|
||||
{ 2 3 2 } naturals dup 2array t-concat
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
|
||||
{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } }
|
||||
{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } [
|
||||
{ 2 3 2 } naturals dup dup 3array t-concat
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 2 2 } naturals { 2 3 } naturals 2array t-concat
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup { 2 3 } naturals 3array t-concat
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! stack
|
||||
{ t{ { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
|
||||
{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } } [
|
||||
{ 2 3 } naturals dup 2array stack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
|
||||
{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } }
|
||||
{ { 0.0 1.0 2.0 } { 3.0 4.0 5.0 } } } } [
|
||||
{ 2 3 } naturals dup dup 3array stack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
|
||||
{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } } [
|
||||
{ 2 3 2 } naturals dup 2array stack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
|
||||
{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } }
|
||||
{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } } } } } [
|
||||
{ 2 3 2 } naturals dup dup 3array stack
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 2 2 } naturals { 2 3 } naturals 2array stack
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals { 3 2 } naturals 2array stack
|
||||
]
|
||||
[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup { 2 3 } naturals 3array stack
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup { 3 2 } naturals 3array stack
|
||||
]
|
||||
[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! hstack
|
||||
|
||||
{ t{ { 0.0 1.0 2.0 3.0 1.0 }
|
||||
{ 4.0 5.0 6.0 7.0 1.0 }
|
||||
{ 8.0 9.0 10.0 11.0 1.0 } } } [
|
||||
{ 3 4 } naturals { 3 1 } ones 2array hstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0.0 1.0 2.0 3.0 1.0 0.0 0.0 }
|
||||
{ 4.0 5.0 6.0 7.0 1.0 0.0 0.0 }
|
||||
{ 8.0 9.0 10.0 11.0 1.0 0.0 0.0 } } } [
|
||||
{ 3 4 } naturals { 3 1 } ones { 3 2 } zeros 3array hstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 2.0 3.0 1.0 } { 4.0 5.0 6.0 7.0 1.0 } }
|
||||
{ { 8.0 9.0 10.0 11.0 1.0 } { 12.0 13.0 14.0 15.0 1.0 } } } } [
|
||||
{ 2 2 4 } naturals { 2 2 1 } ones 2array hstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 2.0 3.0 1.0 0.0 0.0 }
|
||||
{ 4.0 5.0 6.0 7.0 1.0 0.0 0.0 } }
|
||||
{ { 8.0 9.0 10.0 11.0 1.0 0.0 0.0 }
|
||||
{ 12.0 13.0 14.0 15.0 1.0 0.0 0.0 } } } } [
|
||||
{ 2 2 4 } naturals { 2 2 1 } ones { 2 2 2 } zeros 3array hstack
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 2 2 } naturals { 3 2 } naturals 2array hstack
|
||||
]
|
||||
[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup { 3 2 } naturals 3array hstack
|
||||
]
|
||||
[ { 2 2 } { 3 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 2 } naturals { 3 2 2 } naturals 2array hstack
|
||||
]
|
||||
[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 2 } naturals dup { 3 2 2 } naturals 3array hstack
|
||||
]
|
||||
[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! vstack
|
||||
{ t{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } { 0.0 1.0 } } } [
|
||||
{ 3 2 } naturals { 1 2 } naturals 2array vstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 }
|
||||
{ 0.0 1.0 } { 0.0 1.0 } { 2.0 3.0 } } } [
|
||||
{ 3 2 } naturals { 1 2 } naturals { 2 2 } naturals 3array vstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 } { 0.0 1.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 } { 2.0 3.0 } } } } [
|
||||
{ 2 3 2 } naturals { 2 1 2 } naturals 2array vstack
|
||||
] unit-test
|
||||
|
||||
{ t{ { { 0.0 1.0 } { 2.0 3.0 } { 4.0 5.0 }
|
||||
{ 0.0 1.0 } { 0.0 1.0 } { 2.0 3.0 } }
|
||||
{ { 6.0 7.0 } { 8.0 9.0 } { 10.0 11.0 }
|
||||
{ 2.0 3.0 } { 4.0 5.0 } { 6.0 7.0 } } } } [
|
||||
{ 2 3 2 } naturals { 2 1 2 } naturals { 2 2 2 } naturals 3array vstack
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ 2 2 } naturals { 2 3 } naturals 2array vstack
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 } naturals dup { 2 3 } naturals 3array vstack
|
||||
]
|
||||
[ { 2 2 } { 2 3 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 2 } naturals { 3 2 2 } naturals 2array vstack
|
||||
]
|
||||
[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
[
|
||||
{ 2 2 2 } naturals dup { 3 2 2 } naturals 3array vstack
|
||||
]
|
||||
[ { 2 2 2 } { 3 2 2 } \ shape-mismatch-error boa = ] must-fail-with
|
||||
|
||||
! test tensor>array
|
||||
{ { 0.0 0.0 } } [
|
||||
{ 2 } zeros tensor>array
|
||||
|
|
@ -475,6 +837,36 @@ IN: tensors.tests
|
|||
{ 2 3 2 } ones tensor>array
|
||||
] unit-test
|
||||
|
||||
! test >tensor
|
||||
{ t } [
|
||||
{ 2 3 4 } naturals dup tensor>array >tensor =
|
||||
] unit-test
|
||||
|
||||
{ t } [
|
||||
{ { { 1.0 2.0 } { 3.0 4.0 } }
|
||||
{ { 5.0 6.0 } { 7.0 8.0 } }
|
||||
{ { 9.0 10.0 } { 11.0 12.0 } } }
|
||||
dup >tensor tensor>array =
|
||||
] unit-test
|
||||
|
||||
{ t } [
|
||||
{ 2 3 } naturals
|
||||
{ { 0 1 2 } { 3 4 5 } } >tensor =
|
||||
] unit-test
|
||||
|
||||
[
|
||||
{ { 1 2 } { 3 } } >tensor
|
||||
]
|
||||
[ { { 1 2 } { 3 } } \ non-uniform-seq-error boa = ] must-fail-with
|
||||
|
||||
{ float-array{ } } [
|
||||
t{ } vec>>
|
||||
] unit-test
|
||||
|
||||
{ { 0 } } [
|
||||
t{ } shape>>
|
||||
] unit-test
|
||||
|
||||
! test matmul
|
||||
{ float-array{ 70.0 76.0 82.0 88.0 94.0 190.0 212.0 234.0
|
||||
256.0 278.0 310.0 348.0 386.0 424.0 462.0 } } [
|
||||
|
|
@ -510,6 +902,45 @@ IN: tensors.tests
|
|||
{ 2 2 3 4 } naturals { 2 2 4 5 } naturals matmul shape>>
|
||||
] unit-test
|
||||
|
||||
! where n mod 4 is not 0 and m & p have same mod 4 val
|
||||
{ float-array{ 45.0 48.0 51.0 54.0 57.0 60.0 63.0 66.0 69.0 126.0
|
||||
138.0 150.0 162.0 174.0 186.0 198.0 210.0 222.0 207.0 228.0
|
||||
249.0 270.0 291.0 312.0 333.0 354.0 375.0 288.0 318.0 348.0
|
||||
378.0 408.0 438.0 468.0 498.0 528.0 369.0 408.0 447.0 486.0
|
||||
525.0 564.0 603.0 642.0 681.0 } } [
|
||||
{ 5 3 } naturals { 3 9 } naturals matmul vec>>
|
||||
] unit-test
|
||||
|
||||
! where n mod 4 is not 0 and m & p have same mod 4 val
|
||||
{ float-array{ 270.0 280.0 290.0 300.0 310.0 320.0 330.0 340.0 350.0
|
||||
720.0 755.0 790.0 825.0 860.0 895.0 930.0 965.0 1000.0
|
||||
1170.0 1230.0 1290.0 1350.0 1410.0 1470.0 1530.0 1590.0
|
||||
1650.0 1620.0 1705.0 1790.0 1875.0 1960.0 2045.0 2130.0
|
||||
2215.0 2300.0 2070.0 2180.0 2290.0 2400.0 2510.0 2620.0
|
||||
2730.0 2840.0 2950.0 } } [
|
||||
{ 5 5 } naturals { 5 9 } naturals matmul vec>>
|
||||
] unit-test
|
||||
|
||||
! where n mod 4 is not 0 and m & p have different mod 4 vals
|
||||
{ float-array{ 35.0 38.0 41.0 44.0 47.0 50.0 53.0 98.0 110.0
|
||||
122.0 134.0 146.0 158.0 170.0 161.0 182.0 203.0 224.0
|
||||
245.0 266.0 287.0 224.0 254.0 284.0 314.0 344.0 374.0
|
||||
404.0 287.0 326.0 365.0 404.0 443.0 482.0 521.0 } } [
|
||||
{ 5 3 } naturals { 3 7 } naturals matmul vec>>
|
||||
] unit-test
|
||||
|
||||
{ float-array{ 546.0 567.0 588.0 609.0 630.0 651.0 1428.0 1498.0 1568.0 1638.0
|
||||
1708.0 1778.0 2310.0 2429.0 2548.0 2667.0 2786.0 2905.0 3192.0 3360.0
|
||||
3528.0 3696.0 3864.0 4032.0 4074.0 4291.0 4508.0 4725.0 4942.0 5159.0
|
||||
4956.0 5222.0 5488.0 5754.0 6020.0 6286.0 5838.0 6153.0 6468.0 6783.0
|
||||
7098.0 7413.0 22008.0 22372.0 22736.0 23100.0 23464.0 23828.0 24948.0
|
||||
25361.0 25774.0 26187.0 26600.0 27013.0 27888.0 28350.0 28812.0 29274.0
|
||||
29736.0 30198.0 30828.0 31339.0 31850.0 32361.0 32872.0 33383.0 33768.0
|
||||
34328.0 34888.0 35448.0 36008.0 36568.0 36708.0 37317.0 37926.0 38535.0
|
||||
39144.0 39753.0 39648.0 40306.0 40964.0 41622.0 42280.0 42938.0 } } [
|
||||
{ 2 7 7 } naturals { 2 7 6 } naturals matmul vec>>
|
||||
] unit-test
|
||||
|
||||
! test transpose
|
||||
{ float-array{ 0.0 2.0 1.0 3.0 } } [
|
||||
{ 2 2 } naturals transpose vec>>
|
||||
|
|
|
|||
|
|
@ -1,12 +1,14 @@
|
|||
! Copyright (C) 2019 HMC Clinic.
|
||||
! See http://factorcode.org/license.txt for BSD license.
|
||||
|
||||
USING: accessors alien.data arrays grouping kernel locals math
|
||||
math.functions math.ranges multi-methods sequences
|
||||
sequences.extras sequences.private specialized-arrays typed ;
|
||||
USING: accessors alien alien.c-types alien.data arrays byte-arrays combinators
|
||||
grouping kernel locals kernel.private math math.functions math.ranges math.vectors
|
||||
math.vectors.simd multi-methods parser prettyprint.custom sequences sequences.extras
|
||||
sequences.private specialized-arrays typed ;
|
||||
|
||||
QUALIFIED-WITH: alien.c-types c
|
||||
SPECIALIZED-ARRAY: c:float
|
||||
SPECIALIZED-ARRAY: float-4
|
||||
IN: tensors
|
||||
|
||||
! Tensor class definition
|
||||
|
|
@ -17,6 +19,8 @@ TUPLE: tensor
|
|||
! Errors
|
||||
ERROR: non-positive-shape-error shape ;
|
||||
ERROR: shape-mismatch-error shape1 shape2 ;
|
||||
ERROR: non-uniform-seq-error seq ;
|
||||
ERROR: dimension-mismatch-error tensor-dim index-dim ;
|
||||
|
||||
<PRIVATE
|
||||
|
||||
|
|
@ -28,6 +32,7 @@ ERROR: shape-mismatch-error shape1 shape2 ;
|
|||
: <tensor> ( shape seq -- tensor )
|
||||
tensor boa ;
|
||||
|
||||
! Creates a freshly-allocated float-array with the desired c-type values
|
||||
: >float-array ( seq -- float-array )
|
||||
c:float >c-array ;
|
||||
|
||||
|
|
@ -50,10 +55,14 @@ PRIVATE>
|
|||
: arange ( a b step -- tensor )
|
||||
<range> [ length >fixnum 1array ] keep >float-array <tensor> ;
|
||||
|
||||
! Construct a tensors with vec { 0 1 2 ... } and reshape to the desired shape
|
||||
! Construct a tensor with vec { 0 1 2 ... } and reshape to the desired shape
|
||||
: naturals ( shape -- tensor )
|
||||
check-shape [ ] [ product [0,b) >float-array ] bi <tensor> ;
|
||||
|
||||
! Construct a tensor without initializing its values
|
||||
: (tensor) ( shape -- tensor )
|
||||
dup product (float-array) <tensor> ;
|
||||
|
||||
<PRIVATE
|
||||
|
||||
: check-reshape ( shape1 shape2 -- shape1 shape2 )
|
||||
|
|
@ -81,6 +90,117 @@ TYPED: tensor>array ( tensor: tensor -- seq: array )
|
|||
[ rest-slice reverse [ group ] each ] unless-empty ;
|
||||
|
||||
<PRIVATE
|
||||
! recursively finds shape of nested array
|
||||
! assumes properly shaped array (all sub-arrays are same size)
|
||||
:: find-shape ( seq shape -- shape' )
|
||||
seq empty? [ { 0 } ] [
|
||||
! add length of seq element to shape
|
||||
shape seq length 1array append :> shape'
|
||||
! base case: check if the first element is a seq
|
||||
seq first :> 1st
|
||||
1st sequence?
|
||||
! is a sequence: recurse on 1st element
|
||||
[ 1st shape' find-shape ]
|
||||
! not a sequence: return shape'
|
||||
[ shape' ] if
|
||||
] if ;
|
||||
PRIVATE>
|
||||
|
||||
! turns a nested array into a tensor
|
||||
:: >tensor ( seq -- tensor )
|
||||
! get the shape
|
||||
seq { } find-shape :> shape
|
||||
! flatten the array
|
||||
seq
|
||||
shape length 1 - [
|
||||
drop concat
|
||||
] each-integer :> flatseq
|
||||
! check that the size is good
|
||||
shape product flatseq length =
|
||||
[ seq non-uniform-seq-error ] unless
|
||||
! turn into a tensor
|
||||
shape flatseq >float-array <tensor> ;
|
||||
|
||||
SYNTAX: t{ \ } [ >tensor ] parse-literal ;
|
||||
|
||||
! Pretty printing
|
||||
syntax:M: tensor pprint-delims drop \ t{ \ } ;
|
||||
syntax:M: tensor >pprint-sequence tensor>array ;
|
||||
syntax:M: tensor pprint* pprint-object ;
|
||||
|
||||
|
||||
<PRIVATE
|
||||
! turns a shape into a list of things by which to multiply
|
||||
! indices to get a full index (e.g. { 2 3 4 } -> { 12 4 1 })
|
||||
: ind-mults ( shape -- seq )
|
||||
<reversed> 1 swap [ swap [ * ] keep ] map nip reverse ;
|
||||
|
||||
! turns a num/seq index & tensor into num index & tensor
|
||||
! also throws a dimension mismatch if seq & tens shape>> arent the same len
|
||||
: num-index ( n/seq tensor -- n tensor )
|
||||
! check form of index (num or seq)
|
||||
swap dup array? not
|
||||
[ ! if array, first check if it's a valid index
|
||||
2dup [ shape>> length ] dip length 2dup =
|
||||
[ dimension-mismatch-error ] unless 2drop
|
||||
! turn into num
|
||||
[ dup shape>> ind-mults ] dip [ * ] 2map-sum
|
||||
] unless swap ;
|
||||
|
||||
PRIVATE>
|
||||
|
||||
|
||||
! Sequence protocol implementation
|
||||
syntax:M: tensor clone [ shape>> clone ] [ vec>> clone ] bi <tensor> ;
|
||||
|
||||
syntax:M: tensor length vec>> length ;
|
||||
|
||||
syntax:M: tensor nth num-index vec>> nth ;
|
||||
|
||||
syntax:M: tensor nth-unsafe num-index vec>> nth-unsafe ;
|
||||
|
||||
syntax:M: tensor set-nth num-index vec>> set-nth ;
|
||||
|
||||
syntax:M: tensor set-nth-unsafe num-index vec>> set-nth-unsafe ;
|
||||
|
||||
syntax:M: tensor new-sequence
|
||||
! Check if the old and new tensors are the same size
|
||||
shape>> 2dup product =
|
||||
! If so preserve the shape, otherwise create a 1D tensor
|
||||
[ nip (tensor) ] [ drop 1array (tensor) ] if ;
|
||||
|
||||
syntax:M: tensor like
|
||||
! If the original sequence is already a tensor, we are done
|
||||
over tensor?
|
||||
[ drop ] [
|
||||
over float-array? [
|
||||
[ dup [ length 1array ] dip <tensor> ] dip
|
||||
] [
|
||||
[ >tensor ] dip
|
||||
] if
|
||||
2dup [ length ] bi@ = [ shape>> reshape ] [ drop ] if
|
||||
] if ;
|
||||
|
||||
syntax:M: tensor clone-like
|
||||
! If the original sequence is already a tensor, we just need to clone it
|
||||
over tensor?
|
||||
[ drop clone ] [
|
||||
[ >tensor ] dip
|
||||
2dup [ length ] bi@ = [ shape>> reshape ] [ drop ] if
|
||||
] if ;
|
||||
|
||||
INSTANCE: tensor sequence
|
||||
|
||||
|
||||
<PRIVATE
|
||||
|
||||
:: make-subseq ( arr start len -- arr )
|
||||
! Find the index
|
||||
c:float heap-size start *
|
||||
! Compute the starting pointer
|
||||
arr underlying>> <displaced-alien>
|
||||
! Push length and type to create the new array
|
||||
len c:float <c-direct-array> ; inline
|
||||
|
||||
: check-bop-shape ( shape1 shape2 -- shape )
|
||||
2dup = [ shape-mismatch-error ] unless drop ;
|
||||
|
|
@ -90,42 +210,245 @@ TYPED:: t-bop ( tensor1: tensor tensor2: tensor quot: ( x y -- z ) -- tensor: te
|
|||
tensor1 shape>> tensor2 shape>> check-bop-shape
|
||||
tensor1 vec>> tensor2 vec>> quot 2map <tensor> ; inline
|
||||
|
||||
! Create an array of 4-element SIMD arrays for processing floats
|
||||
: simd-for-bop ( array -- simd-array rest-slice/f )
|
||||
dup length dup 4 mod [ drop f ] [ - cut-slice ] if-zero
|
||||
[ float-4 cast-array ] dip ; inline
|
||||
|
||||
! Create an array of 4-element SIMD arrays for processing floats
|
||||
! Tensor class definition
|
||||
TUPLE: simd-slice
|
||||
{ first-slice float-array }
|
||||
{ simd-slice float-4-array }
|
||||
{ end-slice float-array } ;
|
||||
|
||||
:: (simd-slice) ( arr start len -- arr/f )
|
||||
len [ float-array{ } ] [ drop arr start len make-subseq ] if-zero ; inline
|
||||
|
||||
:: <simd-slice> ( arr start -- simd-slice )
|
||||
! Compute the beginning
|
||||
arr 0 start (simd-slice)
|
||||
! Compute the SIMD part
|
||||
arr length start - :> len
|
||||
len 4 mod :> end
|
||||
arr start len end - (simd-slice) float-4 cast-array
|
||||
! Compute the end
|
||||
arr dup length end - end (simd-slice)
|
||||
simd-slice boa ; inline
|
||||
|
||||
! Apply the binary operators simd-quot and quot to quickly combine the tensors
|
||||
:: t-bop-simd ( tensor1 tensor2 simd-quot: ( x y -- z ) quot: ( x y -- z ) -- tensor )
|
||||
tensor1 shape>> tensor2 shape>> check-bop-shape
|
||||
tensor1 vec>> tensor2 vec>>
|
||||
dup length (float-array) dup :> vec3
|
||||
[ simd-for-bop ] tri@ :> ( simd1 rest1 simd2 rest2 simd3 rest3 )
|
||||
simd1 simd2 simd-quot simd3 2map-into
|
||||
rest1 rest2 quot rest3 2map-into
|
||||
vec3 <tensor> ; inline
|
||||
|
||||
! Apply the operation to the tensor
|
||||
TYPED:: t-uop ( tensor: tensor quot: ( x -- y ) -- tensor: tensor )
|
||||
tensor vec>> quot map [ tensor shape>> ] dip <tensor> ; inline
|
||||
|
||||
! Apply the binary operators simd-quot and quot to quickly combine a tensor and
|
||||
! a number
|
||||
:: t-uop-simd ( tensor n simd-quot: ( x y -- z ) quot: ( x y -- z ) -- tensor )
|
||||
tensor dup [ shape>> ] [ vec>> ] bi*
|
||||
dup length (float-array) dup :> vec2
|
||||
[ simd-for-bop ] bi@ :> ( simd1 rest1 simd2 rest2 )
|
||||
simd1 n n n n float-4-boa simd-quot curry simd2 map-into
|
||||
rest1 n quot curry rest2 map-into
|
||||
vec2 <tensor> ; inline
|
||||
|
||||
PRIVATE>
|
||||
|
||||
! Add a tensor to either another tensor or a scalar
|
||||
multi-methods:GENERIC: t+ ( x y -- tensor )
|
||||
METHOD: t+ { tensor tensor } [ + ] t-bop ;
|
||||
METHOD: t+ { tensor number } >float [ + ] curry t-uop ;
|
||||
METHOD: t+ { number tensor } [ >float ] dip [ + ] with t-uop ;
|
||||
METHOD: t+ { tensor tensor } [ v+ ] [ + ] t-bop-simd ;
|
||||
METHOD: t+ { tensor number } >float [ v+ ] [ + ] t-uop-simd ;
|
||||
METHOD: t+ { number tensor } swap >float [ swap v+ ] [ swap + ] t-uop-simd ;
|
||||
|
||||
! Subtraction between two tensors or a tensor and a scalar
|
||||
multi-methods:GENERIC: t- ( x y -- tensor )
|
||||
METHOD: t- { tensor tensor } [ - ] t-bop ;
|
||||
METHOD: t- { tensor number } >float [ - ] curry t-uop ;
|
||||
METHOD: t- { number tensor } [ >float ] dip [ - ] with t-uop ;
|
||||
METHOD: t- { tensor tensor } [ v- ] [ - ] t-bop-simd ;
|
||||
METHOD: t- { tensor number } >float [ v- ] [ - ] t-uop-simd ;
|
||||
METHOD: t- { number tensor } swap >float [ swap v- ] [ swap - ] t-uop-simd ;
|
||||
|
||||
! Multiply a tensor with either another tensor or a scalar
|
||||
multi-methods:GENERIC: t* ( x y -- tensor )
|
||||
METHOD: t* { tensor tensor } [ * ] t-bop ;
|
||||
METHOD: t* { tensor number } >float [ * ] curry t-uop ;
|
||||
METHOD: t* { number tensor } [ >float ] dip [ * ] with t-uop ;
|
||||
METHOD: t* { tensor tensor } [ v* ] [ * ] t-bop-simd ;
|
||||
METHOD: t* { tensor number } >float [ v* ] [ * ] t-uop-simd ;
|
||||
METHOD: t* { number tensor } swap >float [ swap v* ] [ swap * ] t-uop-simd ;
|
||||
|
||||
! Divide two tensors or a tensor and a scalar
|
||||
multi-methods:GENERIC: t/ ( x y -- tensor )
|
||||
METHOD: t/ { tensor tensor } [ / ] t-bop ;
|
||||
METHOD: t/ { tensor number } >float [ / ] curry t-uop ;
|
||||
METHOD: t/ { number tensor } [ >float ] dip [ / ] with t-uop ;
|
||||
METHOD: t/ { tensor tensor } [ v/ ] [ / ] t-bop-simd ;
|
||||
METHOD: t/ { tensor number } >float [ v/ ] [ / ] t-uop-simd ;
|
||||
METHOD: t/ { number tensor } swap >float [ swap v/ ] [ swap / ] t-uop-simd ;
|
||||
|
||||
! Divide two tensors or a tensor and a scalar
|
||||
! Mod two tensors or a tensor and a scalar
|
||||
multi-methods:GENERIC: t% ( x y -- tensor )
|
||||
METHOD: t% { tensor tensor } [ mod ] t-bop ;
|
||||
METHOD: t% { tensor number } >float [ mod ] curry t-uop ;
|
||||
METHOD: t% { number tensor } [ >float ] dip [ mod ] with t-uop ;
|
||||
|
||||
! Sum together all elements in the tensor
|
||||
syntax:M: tensor sum vec>> 0 <simd-slice>
|
||||
[ simd-slice>> 0 [ sum + ] reduce ]
|
||||
[ end-slice>> sum ] bi + ;
|
||||
|
||||
<PRIVATE
|
||||
|
||||
! Also converts all elements of the sequence to tensors
|
||||
:: check-concat-shape ( seq -- seq )
|
||||
! Compute the bottom shape of the first element in the sequence
|
||||
seq first { } >tensor dup :> empty-tensor
|
||||
like shape>> dup :> first-shape rest :> rest-shape
|
||||
seq [
|
||||
! Compute the bottom shape of this element
|
||||
empty-tensor like dup shape>> rest
|
||||
! Compare; if they are different, throw an error
|
||||
rest-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
|
||||
] map ;
|
||||
|
||||
! Also converts all elements of the sequence to tensors
|
||||
:: check-stack-shape ( seq -- seq )
|
||||
! Compute the bottom shape of the first element in the sequence
|
||||
seq first { } >tensor dup :> empty-tensor
|
||||
like shape>> :> first-shape
|
||||
seq [
|
||||
! Compute the bottom shape of this element
|
||||
empty-tensor like dup shape>>
|
||||
! Compare; if they are different, throw an error
|
||||
first-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
|
||||
] map ;
|
||||
|
||||
! Also converts all elements of the sequence to tensors
|
||||
:: check-hstack-shape ( seq -- seq )
|
||||
! Compute the top shape of the first element in the sequence
|
||||
seq first { } >tensor dup :> empty-tensor
|
||||
like shape>> dup :> first-shape but-last :> but-last-shape
|
||||
seq [
|
||||
! Compute the top shape of this element
|
||||
empty-tensor like dup shape>> but-last
|
||||
! Compare; if they are different, throw an error
|
||||
but-last-shape = [ shape>> first-shape swap shape-mismatch-error ] unless
|
||||
] map ;
|
||||
|
||||
: final-hstack-shape ( seq -- shape )
|
||||
! Get the top part
|
||||
dup first shape>> but-last swap
|
||||
! Compute the last part of the shape
|
||||
[ shape>> last ] map sum 1array append ;
|
||||
|
||||
! Returns an guide for hstacking where the index corresponds to the postion
|
||||
! in the last dimension of the resulting tensor, and the elements are
|
||||
! { which tensor, len of tensor, index }
|
||||
:: hstack-guide ( seq -- guide )
|
||||
! Compute the list of last shape parts
|
||||
seq [ shape>> last ] map :> last-dims
|
||||
! Curr tensor and index in tensor
|
||||
0 0
|
||||
last-dims sum [0,b) [
|
||||
drop :> old-t-ind :> last-dims-i
|
||||
last-dims-i last-dims nth
|
||||
old-t-ind -
|
||||
! If we need to move onto the next tensor
|
||||
[ last-dims-i 1 + 0 ]
|
||||
! Otherwise, stay with the current tensor
|
||||
[ drop last-dims-i old-t-ind ] if-zero
|
||||
2dup [ dup last-dims nth ] dip 3array
|
||||
[ 1 + ] dip
|
||||
] map nip nip ;
|
||||
|
||||
! Given a sequence of tensors, stack them across the last dimension
|
||||
:: hstack-unsafe ( tseq -- tensor )
|
||||
! Create the final tensor
|
||||
tseq final-hstack-shape (tensor)
|
||||
! Compute the guide information
|
||||
tseq hstack-guide dup length :> repeat :> guide
|
||||
dup vec>> [
|
||||
:> i drop
|
||||
! First get the correct tensor
|
||||
i repeat /mod guide nth
|
||||
dup first tseq nth
|
||||
! Now find the correct value within that tensor
|
||||
[ [ second ] [ third ] bi -rot * + ] dip nth
|
||||
] map-index! drop ;
|
||||
|
||||
! Also converts all elements of the sequence to tensors
|
||||
:: check-vstack-shape ( seq -- seq )
|
||||
! Compute the shape of the first sequence
|
||||
seq first { } >tensor dup :> empty-tensor
|
||||
like shape>> dup :> first-shape
|
||||
! Compute the index of the dimension to be stacked across
|
||||
length 2 - :> vdim
|
||||
seq [
|
||||
! Convert this element to a tensor
|
||||
empty-tensor like dup
|
||||
! Compare the shapes
|
||||
shape>> first-shape [ = ] 2map
|
||||
vdim swap remove-nth
|
||||
! If the shapes differ in anything except the second-to-last dimension
|
||||
! this sequence cannot be vstacked
|
||||
t [ = ] reduce [ shape>> first-shape swap shape-mismatch-error ] unless
|
||||
] map ;
|
||||
|
||||
! Compute the shape after the vstack has been completed
|
||||
:: final-vstack-shape ( seq -- shape )
|
||||
! Compute the new second-to-last dimension
|
||||
seq first dims 2 - :> vdim
|
||||
seq 0 [ shape>> vdim swap nth + ] reduce
|
||||
! Combine it to create the new shape
|
||||
seq first shape>> clone :> new-shape
|
||||
vdim new-shape set-nth
|
||||
new-shape ;
|
||||
|
||||
! Combine the second-to-last and last dimensions of each tensor for stacking
|
||||
:: reshape-for-vstack ( seq -- seq )
|
||||
seq first dims 2 - :> vdim
|
||||
seq [
|
||||
dup shape>> vdim cut product 1array append >>shape
|
||||
] map! ;
|
||||
|
||||
|
||||
PRIVATE>
|
||||
|
||||
! Concatenation operations
|
||||
! Concatenate across the last dimension
|
||||
: t-concat ( seq -- tensor )
|
||||
check-concat-shape
|
||||
! Compute the final shape
|
||||
[
|
||||
! Compute the first dimension
|
||||
[ 0 [ shape>> first + ] reduce 1array ]
|
||||
! Compute the other dimensions
|
||||
[ first shape>> rest ] bi append
|
||||
]
|
||||
! Concatenate all of the float-arrays
|
||||
[ [ vec>> ] map concat ] bi <tensor> ;
|
||||
|
||||
: stack ( seq -- tensor )
|
||||
check-stack-shape
|
||||
! Compute the new shape
|
||||
[ [ length 1array ] [ first shape>> ] bi append ]
|
||||
! Concatenate all of the tensors
|
||||
[ [ vec>> ] map concat ] bi <tensor> ;
|
||||
|
||||
: hstack ( seq -- tensor )
|
||||
! Check shape and convert everything to tensors
|
||||
check-hstack-shape hstack-unsafe ;
|
||||
|
||||
: vstack ( seq -- tensor )
|
||||
! Check shape and convert everything to tensors
|
||||
check-vstack-shape
|
||||
! Find the final shape
|
||||
[ final-vstack-shape ]
|
||||
! Reshape each of the tensors and stack
|
||||
[ reshape-for-vstack hstack-unsafe ] bi
|
||||
! Finally reshape and return
|
||||
swap >>shape ;
|
||||
|
||||
<PRIVATE
|
||||
|
||||
! Check that the tensor has an acceptable shape for matrix multiplication
|
||||
|
|
@ -147,25 +470,101 @@ METHOD: t% { number tensor } [ >float ] dip [ mod ] with t-uop ;
|
|||
! Take a slice
|
||||
rot <slice> ;
|
||||
|
||||
! much quicker transpose for 2d tensors
|
||||
TYPED:: 2d-transpose ( tensor: tensor -- tensor': tensor )
|
||||
tensor shape>> :> old-shape
|
||||
tensor vec>> :> vec
|
||||
old-shape first2 :> ( s1 s2 )
|
||||
! loop through new tensor
|
||||
old-shape reverse dup product <iota> [
|
||||
! find y*b val in original tensor
|
||||
s1 /mod s2 *
|
||||
! find x val in original tensor
|
||||
[ s2 /mod ] dip + nip
|
||||
! get that index in original tensor
|
||||
vec nth-unsafe
|
||||
] float-array{ } map-as <tensor> ;
|
||||
|
||||
! Perform matrix multiplication muliplying an
|
||||
! mxn matrix with a nxp matrix
|
||||
TYPED:: 2d-matmul ( vec1: float-array start1: fixnum
|
||||
vec2: float-array start2: fixnum
|
||||
res: float-array start3: fixnum
|
||||
TYPED:: 2d-matmul ( vec1: float-array vec2: float-array res: float-array
|
||||
m: fixnum n: fixnum p: fixnum -- )
|
||||
! For each element in the range, we want to compute the dot product of the
|
||||
! corresponding row and column
|
||||
! Transpose vec2 so that we are doing row * row (as opposed to row * col)
|
||||
{ n p } vec2 <tensor> 2d-transpose vec>> :> vec2
|
||||
|
||||
m [ :> i
|
||||
i n * :> in
|
||||
i p * :> ip
|
||||
vec1 in n make-subseq
|
||||
p [ :> j
|
||||
0.0 ! This is the sum
|
||||
n [ :> k
|
||||
! Add to the sum
|
||||
i n * k + start1 + vec1 nth-unsafe
|
||||
k p * j + start2 + vec2 nth-unsafe
|
||||
* +
|
||||
] each-integer
|
||||
i p * j + start3 + res set-nth-unsafe
|
||||
dup
|
||||
vec2 j n * n make-subseq
|
||||
0.0 [ * + ] 2reduce
|
||||
ip j + res set-nth-unsafe
|
||||
] each-integer
|
||||
drop
|
||||
] each-integer ;
|
||||
|
||||
! Perform matrix multiplication muliplying an
|
||||
! mxn matrix with a nxp matrix
|
||||
TYPED:: 2d-matmul-mixed ( vec1: float-array vec2: float-array res: float-array
|
||||
m: fixnum n: fixnum p: fixnum start: fixnum -- )
|
||||
! For each element in the range, we want to compute the dot product of the
|
||||
! corresponding row and column
|
||||
! Transpose vec2 so that we are doing row * row (as opposed to row * col)
|
||||
{ n p } vec2 <tensor> 2d-transpose vec>> :> vec2
|
||||
|
||||
! Compute the location in the float-array each 2D matrix will start at
|
||||
start m n * * :> start1
|
||||
start n p * * :> start2
|
||||
|
||||
m [ :> i
|
||||
i n * :> in
|
||||
4 4 in start1 + 4 mod - swap mod :> in4m
|
||||
i p * :> ip
|
||||
vec1 in n make-subseq :> sub1
|
||||
sub1 in4m <simd-slice> :> slice1
|
||||
p [ :> j
|
||||
j n * :> jn
|
||||
4 4 jn 4 mod - swap mod :> jn4m
|
||||
vec2 jn n make-subseq
|
||||
in4m jn4m = [
|
||||
jn4m <simd-slice> slice1 swap
|
||||
2dup [ first-slice>> ] bi@ 0.0 [ * + ] 2reduce
|
||||
[ 2dup [ simd-slice>> ] bi@ ] dip [ vdot + ] 2reduce
|
||||
[ [ end-slice>> ] bi@ ] dip [ * + ] 2reduce
|
||||
] [
|
||||
sub1 swap
|
||||
0.0 [ * + ] 2reduce
|
||||
] if
|
||||
ip j + res set-nth-unsafe
|
||||
] each-integer
|
||||
] each-integer ;
|
||||
|
||||
! ! Perform matrix multiplication muliplying an
|
||||
! mxn matrix with a nxp matrix
|
||||
! Should only be called when n is a multiple of 4
|
||||
TYPED:: 2d-matmul-simd ( vec1: float-array vec2: float-array
|
||||
res: float-array
|
||||
m: fixnum n: fixnum p: fixnum -- )
|
||||
! For each element in the range, we want to compute the dot product of the
|
||||
! corresponding row and column
|
||||
! Transpose vec2 so that we are doing row * row (as opposed to row * col)
|
||||
{ n p } vec2 <tensor> 2d-transpose vec>> :> vec2
|
||||
|
||||
m [ :> i
|
||||
i n * :> in
|
||||
i p * :> ip
|
||||
vec1 in n make-subseq float-4 cast-array
|
||||
p [ :> j
|
||||
dup
|
||||
vec2 j n * n make-subseq float-4 cast-array
|
||||
0.0 [ vdot + ] 2reduce
|
||||
ip j + res set-nth-unsafe
|
||||
] each-integer
|
||||
drop
|
||||
] each-integer ;
|
||||
|
||||
PRIVATE>
|
||||
|
|
@ -187,47 +586,43 @@ TYPED:: matmul ( tensor1: tensor tensor2: tensor -- tensor3: tensor )
|
|||
top-shape { m p } append
|
||||
|
||||
! Now create the new float array to store the underlying result
|
||||
dup product c:float (c-array) :> vec3
|
||||
dup product (float-array) :> vec3
|
||||
|
||||
! Now update the tensor3 to contain the multiplied matricies
|
||||
top-prod [
|
||||
:> i
|
||||
! Compute vec1 and start1
|
||||
tensor1 vec>> m n * i *
|
||||
|
||||
! Compute vec1 using direct C arrays
|
||||
tensor1 vec>> m n * i * m n * make-subseq
|
||||
|
||||
! Compute vec2 and start2
|
||||
tensor2 vec>> n p * i *
|
||||
tensor2 vec>> n p * i * n p * make-subseq
|
||||
|
||||
! Compute the result
|
||||
vec3 m p * i *
|
||||
vec3 m p * i * m p * make-subseq
|
||||
! Push m, n, and p and multiply the arrays
|
||||
m n p 2d-matmul
|
||||
m n p
|
||||
{ { [ n 4 mod 0 = ] [ 2d-matmul-simd ] }
|
||||
{ [ n 4 < ] [ 2d-matmul ] }
|
||||
[ i 2d-matmul-mixed ]
|
||||
} cond
|
||||
|
||||
] each-integer
|
||||
vec3 <tensor> ;
|
||||
|
||||
|
||||
<PRIVATE
|
||||
! helper for transpose: turns a shape into a list of things
|
||||
! by which to multiply indices to get a full index
|
||||
: ind-mults ( shape -- seq )
|
||||
<reversed> 1 swap [ swap [ * ] keep ] map nip ;
|
||||
|
||||
! helper for transpose: given shape, flat index, & mults for
|
||||
! the shape, gives nd index
|
||||
: transpose-index ( i shape -- seq )
|
||||
<reversed> [ /mod ] map reverse nip ;
|
||||
PRIVATE>
|
||||
|
||||
! Transpose an n-dimensional tensor by flipping the axes
|
||||
TYPED:: transpose ( tensor: tensor -- tensor': tensor )
|
||||
tensor shape>> :> old-shape
|
||||
tensor vec>> :> vec
|
||||
old-shape reverse :> new-shape
|
||||
! check that the size is fine
|
||||
new-shape product vec length assert=
|
||||
old-shape ind-mults reverse :> mults
|
||||
! loop through new tensor
|
||||
new-shape dup product <iota> [
|
||||
! find index in original tensor
|
||||
old-shape mults [ [ /mod ] dip * ] 2map-sum nip
|
||||
! get that index in original tensor
|
||||
vec nth-unsafe
|
||||
] float-array{ } map-as <tensor> ;
|
||||
tensor shape>> length 2 =
|
||||
[ tensor 2d-transpose ]
|
||||
[ tensor shape>> :> old-shape
|
||||
tensor vec>> :> vec
|
||||
old-shape reverse :> new-shape
|
||||
old-shape ind-mults :> mults
|
||||
! loop through new tensor
|
||||
new-shape dup product <iota> [
|
||||
! find index in original tensor
|
||||
old-shape mults [ [ /mod ] dip * ] 2map-sum nip
|
||||
! get that index in original tensor
|
||||
vec nth-unsafe
|
||||
] float-array{ } map-as <tensor>
|
||||
] if ;
|
||||
|
|
|
|||
Loading…
Reference in New Issue