Merge pull request #658 from SciML/depwarn

Depwarn

Author: ChrisRackauckas

.buildkite/pipeline.yml

@@ -1,5 +1,5 @@
 steps:
-  - label: "Julia 1"
+  - label: "GPU"
     plugins:
       - JuliaCI/julia#v1:
           version: "1"
@@ -8,11 +8,36 @@ steps:
     agents:
       queue: "juliagpu"
       cuda: "*"
+    env:
+      GROUP: 'GPU'
+      JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
+      # SECRET_CODECOV_TOKEN: "..."
     timeout_in_minutes: 240
     # Don't run Buildkite if the commit message includes the text [skip tests]
     if: build.message !~ /\[skip tests\]/
 
-env:
-  GROUP: GPU
-  JULIA_PKG_SERVER: "" # it often struggles with our large artifacts
-  # SECRET_CODECOV_TOKEN: "..."
+  - label: "{{matrix.group}} v{{matrix.version}}"
+    matrix:
+      setup:
+        version:
+          - "1.6"
+          - "1"
+        group:
+          - "NNODE"
+          - "NeuralAdapter"
+          - "IntegroDiff"
+    env:
+      BUILDKITE_PLUGIN_JULIA_VERSION: "{{matrix.version}}"
+      GROUP: "{{matrix.group}}"
+    plugins:
+      - JuliaCI/julia#v1
+      - JuliaCI/julia-test#v1:
+          coverage: false
+          julia_args: "--threads=auto"
+    agents:
+      os: "linux"
+      queue: "juliaecosystem"
+      arch: "x86_64"
+    timeout_in_minutes: 680
+    # Don't run Buildkite if the commit message includes the text [skip tests]
+    if: build.message !~ /\[skip tests\]/

.github/workflows/CI.yml

@@ -13,12 +13,9 @@ jobs:
       fail-fast: false
       matrix:
         group:
-          - NNODE
           - NNPDE1
           - NNPDE2
           - AdaptiveLoss
-          - NeuralAdapter
-          - IntegroDiff
           - Logging
           - Forward
         version:

src/NeuralPDE.jl

@@ -24,11 +24,11 @@ import ModelingToolkit: value, nameof, toexpr, build_expr, expand_derivatives
 import DomainSets: Domain, ClosedInterval
 import ModelingToolkit: Interval, infimum, supremum #,Ball
 import SciMLBase: @add_kwonly, parameterless_type
-using Flux: @nograd
 import Optimisers
 import UnPack: @unpack
 import RecursiveArrayTools
 import ChainRulesCore, Flux, Lux, ComponentArrays
+import ChainRulesCore: @non_differentiable
 
 RuntimeGeneratedFunctions.init(@__MODULE__)
 

src/ode_solve.jl

@@ -413,7 +413,7 @@ function DiffEqBase.__solve(prob::DiffEqBase.AbstractODEProblem,
                                     k = res, dense = true,
                                     interp = NNODEInterpolation(phi, res.u),
                                     calculate_error = false,
-                                    retcode = :Success)
+                                    retcode = ReturnCode.Success)
     DiffEqBase.has_analytic(prob.f) &&
         DiffEqBase.calculate_solution_errors!(sol; timeseries_errors = true,
                                               dense_errors = false)

src/training_strategies.jl

@@ -71,7 +71,7 @@ function StochasticTraining(points; bcs_points = points)
     StochasticTraining(points, bcs_points)
 end
 
-@nograd function generate_random_points(points, bound, eltypeθ)
+function generate_random_points(points, bound, eltypeθ)
     lb, ub = bound
     rand(eltypeθ, length(lb), points) .* (ub .- lb) .+ lb
 end
@@ -150,7 +150,7 @@ function QuasiRandomTraining(points; bcs_points = points,
     QuasiRandomTraining(points, bcs_points, sampling_alg, resampling, minibatch)
 end
 
-@nograd function generate_quasi_random_points_batch(points, bound, eltypeθ, sampling_alg,
+function generate_quasi_random_points_batch(points, bound, eltypeθ, sampling_alg,
                                                     minibatch)
     lb, ub = bound
     set = QuasiMonteCarlo.generate_design_matrices(points, lb, ub, sampling_alg, minibatch)