Prepare for pre-release 0.4.1~beta2

ahrefs · Sep 13, 2024 · 72d12ba · 72d12ba
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diff --git a/arrayjit.opam b/arrayjit.opam
@@ -1,6 +1,6 @@
 # This file is generated by dune, edit dune-project instead
 opam-version: "2.0"
-version: "0.4.1~beta"
+version: "0.4.1~beta2"
 synopsis:
   "An array language compiler with multiple backends (CPU, CUDA), staged compilation"
 description:

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

diff --git a/dune-project b/dune-project
@@ -4,7 +4,7 @@
 
 (name ocannl)
 
-(version 0.4.1~beta)
+(version 0.4.1~beta2)
 
 (generate_opam_files true)
 

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

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

diff --git a/ocannl_npy.opam b/ocannl_npy.opam
@@ -1,6 +1,6 @@
 # This file is generated by dune, edit dune-project instead
 opam-version: "2.0"
-version: "0.4.1~beta"
+version: "0.4.1~beta2"
 synopsis: "Numpy file format support for ocaml"
 maintainer: ["Lukasz Stafiniak <[email protected]>"]
 authors: ["Laurent Mazare"]

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

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