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Prepare for pre-release 0.4.1~beta2
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‎arrayjit.opam‎

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@@ -1,6 +1,6 @@
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# This file is generated by dune, edit dune-project instead
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opam-version: "2.0"
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version: "0.4.1~beta"
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version: "0.4.1~beta2"
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synopsis:
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"An array language compiler with multiple backends (CPU, CUDA), staged compilation"
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description:

‎bin/moons_benchmark.ml‎

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@@ -46,8 +46,8 @@ let classify_moons ~seed ~on_device ~inlining_cutoff ~num_devices ~batch_size ~b
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(* let data_len = 3 * 4 in *)
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let flat_len = data_len / 2 in
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(* Note: [minibatch_size = batch_size / num_devices] is the actual per-device batch used. *)
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let epochs = 200 in
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(* let epochs = 50 in *)
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(* let epochs = 200 in *)
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let epochs = 100 in
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(* TINY for debugging: *)
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(* let epochs = 2 in *)
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(* let epochs = 1 in *)

‎dune-project‎

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@@ -4,7 +4,7 @@
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(name ocannl)
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(version 0.4.1~beta)
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(version 0.4.1~beta2)
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(generate_opam_files true)
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‎lib/tensor.mli‎

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@@ -44,16 +44,16 @@ val with_unchanged_roots : f:(unit -> 'a) -> 'a
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val default_value_prec : Arrayjit.Ops.prec ref
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(** The default precision for the value node of terminal (i.e. non-composite) tensors.
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Note: the precision of a node can be set arbitrarily via {!Tnode.update_precision}. The default
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precision for value nodes of composite tensors is the maximum of precisions of the value nodes
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of sub-tensors. *)
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Note: the precision of a node can be set arbitrarily via {!Arrayjit.Tnode.update_precision}. The
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default precision for value nodes of composite tensors is the maximum of precisions of the value
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nodes of sub-tensors. *)
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val default_grad_prec : Arrayjit.Ops.prec ref
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(** The default precision for the gradient node of terminal (i.e. non-composite) tensors.
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Note: the precision of a node can be set arbitrarily via {!Tnode.update_precision}. The default
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precision for gradient nodes of composite tensors is the maximum of precisions of the gradient
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nodes of sub-tensors. *)
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Note: the precision of a node can be set arbitrarily via {!Arrayjit.Tnode.update_precision}. The
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default precision for gradient nodes of composite tensors is the maximum of precisions of the
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gradient nodes of sub-tensors. *)
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exception Session_error of string * t option
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‎neural_nets_lib.opam‎

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@@ -1,6 +1,6 @@
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# This file is generated by dune, edit dune-project instead
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opam-version: "2.0"
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version: "0.4.1~beta"
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version: "0.4.1~beta2"
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synopsis:
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"A from-scratch Deep Learning framework with an optimizing compiler, shape inference, concise syntax"
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description:

‎ocannl_npy.opam‎

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@@ -1,6 +1,6 @@
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# This file is generated by dune, edit dune-project instead
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opam-version: "2.0"
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version: "0.4.1~beta"
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version: "0.4.1~beta2"
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synopsis: "Numpy file format support for ocaml"
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maintainer: ["Lukasz Stafiniak <lukstafi@gmail.com>"]
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authors: ["Laurent Mazare"]

‎test/micrograd_demo.ml‎

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@@ -304,8 +304,8 @@ let%expect_test "Micrograd half-moons example" =
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│ - - - - - -
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│ - -
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│ -
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│ - - - -
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│
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│ - - -
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│ -
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│ -
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│
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│
@@ -326,7 +326,7 @@ let%expect_test "Micrograd half-moons example" =
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[%expect
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{|
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Learning rate:
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-1.002e-1│ -
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-1.003e-1│ -
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│ -----
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│ -----
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│ -----
@@ -358,7 +358,8 @@ let%expect_test "Micrograd half-moons example" =
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-2.000e-1│----
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──────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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│0.000e+0 3.990e+2
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│ step |}];
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│ step
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|}];
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(* Testing how the syntax extension %op creates labels for the resulting tensors: *)
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Stdio.printf "mlp_result's name: %s\n%!" @@ Tensor.debug_name mlp_result;

‎test/moons_demo_parallel.ml‎

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@@ -168,6 +168,91 @@ Half-moons scatterplot and decision boundary:
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***********.................................................%%%%%%%%%%%%%%%%%.%%%%%%%%%%%%%%%%%%%.......................
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**********......................................................%%%%%%.%%%%%%%%%%%%%%%%%%%%%%%..........................
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*********..........................................................%....%%%%%.%%..%%%%...%..............................|}
171+
in
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let new_typical_target =
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{|
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Batch=19, step=20, lr=0.195250, batch loss=0.263683, epoch loss=45.768524
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Epoch=0, step=20, lr=0.195250, epoch loss=45.768524
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Batch=19, step=40, lr=0.190250, batch loss=0.220793, epoch loss=5.662821
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Epoch=1, step=40, lr=0.190250, epoch loss=5.662821
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Batch=19, step=60, lr=0.185250, batch loss=0.197911, epoch loss=5.259723
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Epoch=2, step=60, lr=0.185250, epoch loss=5.259723
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Batch=19, step=80, lr=0.180250, batch loss=0.191768, epoch loss=5.281074
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Epoch=3, step=80, lr=0.180250, epoch loss=5.281074
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Batch=19, step=100, lr=0.175250, batch loss=0.186862, epoch loss=5.097180
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Epoch=4, step=100, lr=0.175250, epoch loss=5.097180
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Batch=19, step=120, lr=0.170250, batch loss=0.181911, epoch loss=4.987223
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Epoch=5, step=120, lr=0.170250, epoch loss=4.987223
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Batch=19, step=140, lr=0.165250, batch loss=0.178275, epoch loss=4.835103
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Epoch=6, step=140, lr=0.165250, epoch loss=4.835103
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Batch=19, step=160, lr=0.160250, batch loss=0.165620, epoch loss=4.702625
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Epoch=7, step=160, lr=0.160250, epoch loss=4.702625
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Batch=19, step=180, lr=0.155250, batch loss=0.156137, epoch loss=4.458982
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Epoch=8, step=180, lr=0.155250, epoch loss=4.458982
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Batch=19, step=200, lr=0.150250, batch loss=0.139483, epoch loss=4.074086
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Epoch=9, step=200, lr=0.150250, epoch loss=4.074086
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Batch=19, step=220, lr=0.145250, batch loss=0.118495, epoch loss=3.605957
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Epoch=10, step=220, lr=0.145250, epoch loss=3.605957
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Batch=19, step=240, lr=0.140250, batch loss=0.091701, epoch loss=3.061533
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Epoch=11, step=240, lr=0.140250, epoch loss=3.061533
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Batch=19, step=260, lr=0.135250, batch loss=0.062137, epoch loss=2.342423
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Epoch=12, step=260, lr=0.135250, epoch loss=2.342423
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Batch=19, step=280, lr=0.130250, batch loss=0.030009, epoch loss=1.588885
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Epoch=13, step=280, lr=0.130250, epoch loss=1.588885
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Batch=19, step=300, lr=0.125250, batch loss=0.016336, epoch loss=0.904919
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Epoch=14, step=300, lr=0.125250, epoch loss=0.904919
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Batch=19, step=320, lr=0.120250, batch loss=0.009264, epoch loss=0.579104
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Epoch=15, step=320, lr=0.120250, epoch loss=0.579104
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Batch=19, step=340, lr=0.115250, batch loss=0.007289, epoch loss=0.451346
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Epoch=16, step=340, lr=0.115250, epoch loss=0.451346
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Batch=19, step=360, lr=0.110250, batch loss=0.005304, epoch loss=0.342835
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Epoch=17, step=360, lr=0.110250, epoch loss=0.342835
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Batch=19, step=380, lr=0.105250, batch loss=0.004483, epoch loss=0.259518
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Epoch=18, step=380, lr=0.105250, epoch loss=0.259518
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Batch=19, step=400, lr=0.100250, batch loss=0.004777, epoch loss=0.212706
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Epoch=19, step=400, lr=0.100250, epoch loss=0.212706
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215+
Half-moons scatterplot and decision boundary:
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***************************************#********************************************************************************
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***************************#*#*#########*###**######********************************************************************
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***************************######*####*#*#####*########*#***************************************************************
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*********************#**#########**#######*###############*###**********************************************************
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******************####*####################################*###*********************************************************
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***************#*#*###*###*###########*#*##*#####################*******************************************************
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************#*######**#########*##*****************##*##*########*#*****************************************************
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*************########*#*###*#**********************#******####*######*************************************************..
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**************#######*#*##******************************#########*##*##********************************************.....
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**********#######*###*#****************************************###**###*#***************************************........
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********#*######**##****************.....*********************#*##*####*#************************************...........
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********###*#*#**##************..............******************###########*#*******************************.............
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******########**************.........%....%.%...*******************##########*****************************............%.
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*******#######*************...........%...........******************##*######***************************.........%.%..%.
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****##########************............%%%.%%%......*****************##########*************************........%..%%%%%.
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*****######*#************............%%%.%...........**************#*#########***********************..........%.%.%%..%
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**######*#***************............%%%%%%%%..........****************#*##*###*********************............%%%%%%%.
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**##*#####**************..............%%%%%%%...........**************#########*******************..............%%.%%%..
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**########*************..............%%%%%%%%.............**************##*######****************...............%%%%%%%.
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*########**************..............%%%.%%%.%%.............*************#####*****************...............%%%%%%%%%.
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*########*************................%%%%%%%%%..............************###*##*#*************.................%%%%%%%..
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##*######*************................%%%%%%%.%................***********######*#**********..................%%%%%%%%..
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######*##************.................%%.%%%%%%..................*********########*********...................%%%%.%%.%.
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###*##**#***********...................%.%%%%%%%%.................*********#####*#********...................%%%%%%%%...
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##*#####************....................%%%%%%.%.%..................******#*#*####******....................%%.%%%%%....
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#####*##***********.....................%.%%%%%%%%...................*****##**##*******...................%%%%%%%%%%%...
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**#*##*#**********.......................%%%.%%%%%.%...................***#####*#****......................%%%%%%%......
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##****##**********........................%%.%%%%%%%%....................***###*##**....................%%%%%%%%%%......
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*****************.........................%%.%%%%%%%......................********......................%..%%.%%%.......
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****************............................%...%%%%%.%%....................*****..................%.%%%%%%%%%%.........
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****************..............................%.%%%%%.%%%%....................*....................%%%%%%%%.%.%%........
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***************.................................%..%%%%%...%......................................%%%%%%%%%%............
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**************....................................%%%.%%%%%%%%..............................%%..%%%%.%%%%%.%............
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**************...................................%%%.%%%%%%.%%...%.........................%.%%%%%%%.%%%.%..............
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*************........................................%.%%%.%%%%%%%%%...................%.%%%%%%%%%%%%%.%.%..............
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************..........................................%.%%%%.%%%%%%%%%.%%%%%%%%%.%.%%%%%%%%%%%%%%%%%%%.%................
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************............................................%%%%%%%%%%%%%%%%%%%%%.%%%%%%%.%%%.%%%%%%%%%%....................
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***********.................................................%%%%%%%%%%%%%%%%%.%%%%%%%%%%%%%%%%%%%.......................
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**********......................................................%%%%%%.%%%%%%%%%%%%%%%%%%%%%%%..........................
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**********.........................................................%....%%%%%.%%..%%%%...%..............................|}
171256
in
172257
let arm64_and_s390x_target =
173258
{|
@@ -340,8 +425,8 @@ Half-moons scatterplot and decision boundary:
340425
**********.........................................................%....%%%%%.%%..%%%%...%..............................|}
341426
in
342427
let result_deltas =
343-
List.map [ typical_target; arm64_and_s390x_target; ppc64_target ] ~f:(fun target ->
344-
Expect_test_patdiff.patdiff target result)
428+
List.map [ new_typical_target; typical_target; arm64_and_s390x_target; ppc64_target ]
429+
~f:(fun target -> Expect_test_patdiff.patdiff target result)
345430
in
346431
(if List.exists ~f:String.is_empty result_deltas then
347432
Stdio.print_string "moons_demo_parallel result is as expected"

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