Differentiating Gridap code #447

mohamed82008 · 2022-09-10T23:40:12Z

Gridap is a great example of code that can only be differentiated by Enzyme. The following is a snippet that can be used as a test case. It currently fails. This isn't minimal at all but it would be great to understand why it's failing:

using Pkg
Pkg.add("https://github.com/JuliaTopOpt/TopOptGridap.jl")

using TopOptGridap, Enzyme

f, x = TopOptGridap.get_domain()
g = x -> sum(f(x))
# Enzyme.gradient(Enzyme.ForwardMode(), g, x)
dx = similar(x)
Enzyme.gradient!(Enzyme.ReverseMode(), dx, g, x)

wsmoses · 2022-09-11T04:59:59Z

Can you post how it is failing, and also try on latest main (and if so, what commit)

mohamed82008 · 2022-09-11T11:14:11Z

I tried the latest main branch today. For forward mode, I get a segfault after printing a wall of text starting with

not handling more than 6 pointer lookups deep dt:{[]:Pointer, [0,0]:Float@double, [0,8]:Float@double, [0,16]:Float@double, [0,24]:Float@double, [0,32]:Integer, [0,33]:Integer, [0,34]:Integer, [0,35]:Integer, [0,36]:Integer, [0,37]:Integer, [0,38]:Integer, [0,39]:Integer, [0,40]:Integer, [0,41]:Integer, [0,42]:Integer, [0,43]:Integer, [0,44]:Integer, [0,45]:Integer, [0,46]:Integer, [0,47]:Integer, [0,48]:Integer, [0,49]:Integer, [0,56]:Integer, [0,57]:Integer, [0,58]:Integer, [0,59]:Integer, [0,60]:Integer, [0,61]:Integer, [0,62]:Integer, [0,63]:Integer, [0,64]:Integer, [0,65]:Integer, [0,66]:Integer, [0,67]:Integer, [0,68]:Integer, [0,69]:Integer, [0,70]:Integer, [0,71]:Integer, [0,72]:Integer, [0,80]:Integer, [0,81]:Integer, [0,82]:Integer, [0,83]:Integer, [0,84]:Integer, [0,85]:Integer, [0,86]:Integer, [0,87]:Integer, [0,88]:Pointer, [0,88,0]:Pointer, [0,88,0,0]:Float@double, [0,88,0,8]:Float@double, [0,88,8]:Integer, [0,88,9]:Integer, [0,88,10]:Integer, [0,88,11]:Integer, 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[1,120,0,0,0,3]:Integer, [1,120,0,0,8]:Integer, [1,120,0,0,9]:Integer, [1,120,0,0,10]:Integer, [1,120,0,0,11]:Integer, [1,120,0,0,12]:Integer, [1,120,0,0,13]:Integer, [1,120,0,0,14]:Integer, [1,120,0,0,15]:Integer, [1,120,0,0,16]:Integer, [1,120,0,0,17]:Integer, [1,120,0,0,18]:Integer, [1,120,0,0,19]:Integer, [1,120,0,0,20]:Integer, [1,120,0,0,21]:Integer, [1,120,0,0,22]:Integer, [1,120,0,0,23]:Integer, [1,120,0,0,24]:Integer, [1,120,0,0,25]:Integer, [1,120,0,0,26]:Integer, [1,120,0,0,27]:Integer, [1,120,0,0,28]:Integer, [1,120,0,0,29]:Integer, [1,120,0,0,30]:Integer, [1,120,0,0,31]:Integer, [1,120,0,0,32]:Integer, [1,120,0,0,33]:Integer, [1,120,0,0,34]:Integer, [1,120,0,0,35]:Integer, [1,120,0,0,36]:Integer, [1,120,0,0,37]:Integer, [1,120,0,0,38]:Integer, [1,120,0,0,39]:Integer, [1,120,0,0,40]:Integer, [1,120,8]:Integer, [1,120,9]:Integer, [1,120,10]:Integer, [1,120,11]:Integer, [1,120,12]:Integer, [1,120,13]:Integer, [1,120,14]:Integer, [1,120,15]:Integer, [1,120,16]:Integer, [1,120,17]:Integer, [1,120,18]:Integer, [1,120,19]:Integer, [1,120,20]:Integer, [1,120,21]:Integer, [1,120,22]:Integer, [1,120,23]:Integer, [1,120,24]:Integer, [1,120,25]:Integer, [1,120,26]:Integer, [1,120,27]:Integer, [1,120,28]:Integer, [1,120,29]:Integer, [1,120,30]:Integer, [1,120,31]:Integer, [1,120,32]:Integer, [1,120,33]:Integer, [1,120,34]:Integer, [1,120,35]:Integer, [1,120,36]:Integer, [1,120,37]:Integer, [1,120,38]:Integer, [1,120,39]:Integer, [1,120,40]:Integer, [1,128]:Pointer, [1,128,0]:Pointer, [1,128,0,0]:Pointer, [1,128,0,0,0]:Pointer, [1,128,0,0,0,0]:Integer, [1,128,0,0,0,1]:Integer, [1,128,0,0,0,2]:Integer, [1,128,0,0,0,3]:Integer, [1,128,0,0,8]:Integer, [1,128,0,0,9]:Integer, [1,128,0,0,10]:Integer, [1,128,0,0,11]:Integer, [1,128,0,0,12]:Integer, [1,128,0,0,13]:Integer, [1,128,0,0,14]:Integer, [1,128,0,0,15]:Integer, [1,128,0,0,16]:Integer, [1,128,0,0,17]:Integer, [1,128,0,0,18]:Integer, [1,128,0,0,19]:Integer, [1,128,0,0,20]:Integer, [1,128,0,0,21]:Integer, [1,128,0,0,22]:Integer, [1,128,0,0,23]:Integer, [1,128,0,0,24]:Integer, [1,128,0,0,25]:Integer, [1,128,0,0,26]:Integer, [1,128,0,0,27]:Integer, [1,128,0,0,28]:Integer, [1,128,0,0,29]:Integer, [1,128,0,0,30]:Integer, [1,128,0,0,31]:Integer, [1,128,0,0,32]:Integer, [1,128,0,0,33]:Integer, [1,128,0,0,34]:Integer, [1,128,0,0,35]:Integer, [1,128,0,0,36]:Integer, [1,128,0,0,37]:Integer, [1,128,0,0,38]:Integer, [1,128,0,0,39]:Integer, [1,128,0,0,40]:Integer, [1,128,8]:Integer, [1,128,9]:Integer, [1,128,10]:Integer, [1,128,11]:Integer, [1,128,12]:Integer, [1,128,13]:Integer, [1,128,14]:Integer, [1,128,15]:Integer, [1,128,16]:Integer, [1,128,17]:Integer, [1,128,18]:Integer,

For reverse mode, I get the following error:

ERROR: Duplicated Returns not yet handled
Stacktrace:
 [1] autodiff
   @ ~/.julia/packages/Enzyme/M2q6i/src/Enzyme.jl:294 [inlined]
 [2] autodiff(mode::Enzyme.ReverseMode, f::var"#1#2", args::Duplicated{Vector{Float64}})
   @ Enzyme ~/.julia/packages/Enzyme/M2q6i/src/Enzyme.jl:330
 [3] gradient!(#unused#::Enzyme.ReverseMode, dx::Vector{Float64}, f::Function, x::Vector{Float64})
   @ Enzyme ~/.julia/packages/Enzyme/M2q6i/src/Enzyme.jl:628
 [4] top-level scope
   @ REPL[10]:1

I am on an M1 machine.

wsmoses · 2022-09-11T16:15:35Z

Can you post the entire error log for forward mode.

The reverse mode error indicates that the return of the function is not a float or floating-point like (perhaps because of a type instability).

mohamed82008 · 2022-09-12T00:57:09Z

Can you post the entire error log for forward mode.

forwardmode_stacktrace.txt

mohamed82008 · 2022-09-12T01:04:51Z

I used a type assertion to force at least the final output type to be inferred. I got the following error.

reversemode_stacktrace.txt

wsmoses · 2022-09-12T19:21:07Z

From forward mode:

_ZN16LateLowerGCFrame13NumberAllBaseER5StatePN4llvm5ValueE at /Applications/Julia-1.8.app/Contents/Resources/julia/lib/julia/libjulia-codegen.1.8.dylib (unknown line)

This is GC related. We'll need a minimal reproducer to fix, if you're up to minimizing it to one.

wsmoses · 2022-09-26T19:19:38Z

@mohamed82008 did you get a chance to minimize this?

mohamed82008 · 2022-09-26T22:12:36Z

Not yet. I don't think I will be able to find the time for a few weeks.

wsmoses · 2022-10-04T02:04:14Z

The forward mode issue you see seems to be a GC error coming from batch mode, setting the batch size to 1, we get a different error:

Enzyme.gradient(Enzyme.ForwardMode(), g, x, Val(1))

ERROR: LoadError: UndefRefError: access to undefined reference
Stacktrace:
 [1] getindex(t::Tuple, i::Int64)
   @ Base ./tuple.jl:29
in expression starting at /home/wmoses/git/Enzyme.jl/ga2.jl:5

wsmoses · 2022-10-04T02:18:28Z

The current state of reverse mode is:

┌ Warning: Returned rooting not fully handled, segfault likely
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/07qaN/src/utils.jl:35

signal (11): Segmentation fault
in expression starting at /home/wmoses/git/Enzyme.jl/ga.jl:7
ijl_isa at /cache/build/default-amdci5-0/julialang/julia-release-1-dot-8/src/subtype.c:2064
unknown function (ip: 0x7f34456c9503)
unknown function (ip: 0x7f34456ca555)
Allocations: 251758458 (Pool: 251629232; Big: 129226); GC: 161

...which per the warning...I suppose is fair (it did require a quick Enzyme proper fix here: EnzymeAD/Enzyme#879).

Again, if you are able to minimize these to a root issue, we'll be able to debug/fix it. (ideally a pure call to autodiff/etc, as little code as possible that causes the issue, and ideally no or minimal external dependencies)

mohamed82008 · 2022-10-04T04:03:29Z

Thanks for looking into this. Your ask is reasonable but can be very hard for the kind of work I need Enzyme for. The function is discretising a PDE, assembling a sparse matrix, solving a linear system and then doing some post-processing of the output. A minimal example would require snipping this pipeline and doing an Enzyme call to bisect-find the problematic code. That's a lot of work especially without a helpful stacktrace although I understand segfault stacktraces are not really helpful in general so it's not Enzyme's fault.

vchuravy · 2022-10-12T01:16:18Z

@wsmoses you mentioned in chat that you still hit a GC error here?

I get a proper runtime error:

ERROR: LoadError: Duplicated Returns not yet handled
Stacktrace:
 [1] autodiff
   @ ~/src/Enzyme/src/Enzyme.jl:197 [inlined]
 [2] autodiff(mode::EnzymeCore.ReverseMode, f::var"#1#2", args::Duplicated{Vector{Float64}})
   @ Enzyme ~/src/Enzyme/src/Enzyme.jl:236
 [3] gradient!(#unused#::EnzymeCore.ReverseMode, dx::Vector{Float64}, f::Function, x::Vector{Float64})
   @ Enzyme ~/src/Enzyme/src/Enzyme.jl:553
 [4] top-level scope
   @ ~/src/Enzyme/issue447.jl:7
in expression starting at /home/vchuravy/src/Enzyme/issue447.jl:7

But no GC error? Can you post your reproducer?

Edit: Okay I see the GC error on fwddiff

wsmoses · 2022-10-12T01:32:38Z

@vchuravy

using Pkg
Pkg.add(url="https://github.com/JuliaTopOpt/TopOptGridap.jl")

using TopOptGridap, Enzyme

f, x = TopOptGridap.get_domain()
g = x -> sum(f(x))::Float64
# Enzyme.gradient(Enzyme.ForwardMode(), g, x)
dx = similar(x)
Enzyme.gradient!(Enzyme.ReverseMode(), dx, g, x)

vchuravy · 2022-10-12T17:15:08Z

For the FWD mode failure

Thread 1 received signal SIGSEGV, Segmentation fault.
0x00007fb690adb52f in ijl_isa (x=0x21, t=0x7fb67ad10720 <jl_system_image_data+1529120>) at /home/vchuravy/src/julia/src/subtype.c:2067
warning: Source file is more recent than executable.
2067	        if (((jl_datatype_t*)t)->name == jl_type_typename)
(rr) bt
#0  0x00007fb690adb52f in ijl_isa (x=0x21, t=0x7fb67ad10720 <jl_system_image_data+1529120>) at /home/vchuravy/src/julia/src/subtype.c:2067
#1  0x00007fb69083e5f8 in Typeof () at boot.jl:243
#2  wrap_annotated_args () at /home/vchuravy/src/Enzyme/src/compiler.jl:516
#3  julia_runtime_generic_fwd_8197 (fn=0x0, arg_ptr=..., shadow_ptr=..., activity_ptr=<optimized out>, width=...) at /home/vchuravy/src/Enzyme/src/compiler.jl:618
#4  0x00007fb69083e4b2 in julia_scatter_free_and_dirichlet_values_8120 (f=..., free_values=<optimized out>, dirichlet_values=<optimized out>)
    at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/UnconstrainedFESpaces.jl:76
#5  0x00007fb690841700 in scatter_free_and_dirichlet_values () at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/TrialFESpaces.jl:110
#6  julia_FEFunction_8117 (fs=..., free_values=<optimized out>, dirichlet_values=<optimized out>)
    at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/SingleFieldFESpaces.jl:163
#7  0x00007fb690841700 in julia_FEFunction_8117 (fs=..., free_values=<optimized out>, dirichlet_values=<optimized out>)
#8  0x00007fb6787fda19 in macro expansion () at /home/vchuravy/src/Enzyme/src/compiler.jl:6162
#9  julia_enzyme_call_8241 (fptr=0, tt=<optimized out>, rt=<optimized out>, f=0x21, df=..., args...=...) at /home/vchuravy/src/Enzyme/src/compiler.jl:5900
#10 0x00007fb6787fdaad in jfptr_enzyme_call_8242 ()
#11 0x00007fb690a4e535 in _jl_invoke (F=0x7fb648e53658, args=0x7ffdd3f0a698, nargs=13, mfunc=0x7fb658146db0, world=32452) at /home/vchuravy/src/julia/src/gf.c:2367
#12 0x00007fb690a4eeed in ijl_apply_generic (F=0x7fb648e53658, args=0x7ffdd3f0a698, nargs=13) at /home/vchuravy/src/julia/src/gf.c:2549
#13 0x00007fb690a5e62d in jl_apply (args=0x7ffdd3f0a690, nargs=14) at /home/vchuravy/src/julia/src/julia.h:1838
#14 0x00007fb690a61eb0 in do_apply (args=0x7ffdd3f0a8d0, nargs=3, iterate=0x7fb67ae6f5b0 <jl_system_image_data+2966448>) at /home/vchuravy/src/julia/src/builtins.c:730
#15 0x00007fb690a61f3d in jl_f__apply_iterate (F=0x0, args=0x7ffdd3f0a8c8, nargs=4) at /home/vchuravy/src/julia/src/builtins.c:738
#16 0x00007fb6787fd74f in japi1_ForwardModeThunk_8239 (thunk=..., args...=0x7fb64fbd30c0) at /home/vchuravy/src/Enzyme/src/compiler.jl:5880
#17 0x00007fb690a4d8cd in jl_fptr_args (f=0x7fb64e309f90, args=0x7ffdd3f0aaa8, nargs=3, m=0x7fb6501f1f90) at /home/vchuravy/src/julia/src/gf.c:2128
#18 0x00007fb690a4e535 in _jl_invoke (F=0x7fb64e309f90, args=0x7ffdd3f0aaa8, nargs=3, mfunc=0x7fb658128e70, world=32452) at /home/vchuravy/src/julia/src/gf.c:2367
#19 0x00007fb690a4eeed in ijl_apply_generic (F=0x7fb64e309f90, args=0x7ffdd3f0aaa8, nargs=3) at /home/vchuravy/src/julia/src/gf.c:2549
#20 0x00007fb690a5e62d in jl_apply (args=0x7ffdd3f0aaa0, nargs=4) at /home/vchuravy/src/julia/src/julia.h:1838
#21 0x00007fb690a61eb0 in do_apply (args=0x7ffdd3f0acc0, nargs=2, iterate=0x7fb67ae6f5b0 <jl_system_image_data+2966448>) at /home/vchuravy/src/julia/src/builtins.c:730
#22 0x00007fb690a61f3d in jl_f__apply_iterate (F=0x0, args=0x7ffdd3f0acb8, nargs=3) at /home/vchuravy/src/julia/src/builtins.c:738
#23 0x00007fb6904383fb in julia_runtime_generic_fwd_7063 (fn=0x0, arg_ptr=..., shadow_ptr=..., activity_ptr=<optimized out>, width=...)
    at /home/vchuravy/src/Enzyme/src/compiler.jl:636
#24 0x00007fb690437c93 in julia_FEFunction_6341 (fe=..., free_values=<optimized out>) at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/SingleFieldFESpaces.jl:170
#25 0x00007fb69043263a in EvaluationFunction () at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/FESpaceInterface.jl:113
#26 zero () at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/FESpaceInterface.jl:120
#27 julia_AffineFEOperator_6334 (weakform=..., trial=..., test=..., assem=...) at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/AffineFEOperators.jl:35
#28 0x00007fb690445f81 in AffineFEOperator () at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/AffineFEOperators.jl:47
#29 AffineFEOperator () at /home/vchuravy/.julia/packages/Gridap/TyUsh/src/FESpaces/AffineFEOperators.jl:51
#30 julia_displacements_6325 (cell_param=<optimized out>) at /home/vchuravy/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:42
#31 0x00007fb690445f81 in julia_displacements_6325 (cell_param=<optimized out>)
#32 0x00007fb67874660b in macro expansion () at /home/vchuravy/src/Enzyme/src/compiler.jl:6162
#33 enzyme_call () at /home/vchuravy/src/Enzyme/src/compiler.jl:5900
#34 julia_ForwardModeThunk_7380 (thunk=<error reading variable: Cannot access memory at address 0x19>, args...=...) at /home/vchuravy/src/Enzyme/src/compiler.jl:5880
#35 0x00007fb678746694 in jfptr_ForwardModeThunk_7381 ()
#36 0x00007fb690a4e535 in _jl_invoke (F=0x7fb64b290450, args=0x7ffdd3f0bd18, nargs=1, mfunc=0x7fb649f1d0a0, world=32452) at /home/vchuravy/src/julia/src/gf.c:2367
#37 0x00007fb690a4eeed in ijl_apply_generic (F=0x7fb64b290450, args=0x7ffdd3f0bd18, nargs=1) at /home/vchuravy/src/julia/src/gf.c:2549
#38 0x00007fb690a5e62d in jl_apply (args=0x7ffdd3f0bd10, nargs=2) at /home/vchuravy/src/julia/src/julia.h:1838
--Type <RET> for more, q to quit, c to continue without paging--c
#39 0x00007fb690a61eb0 in do_apply (args=0x7ffdd3f0bf10, nargs=2, iterate=0x7fb67ae6f5b0 <jl_system_image_data+2966448>) at /home/vchuravy/src/julia/src/builtins.c:730
#40 0x00007fb690a61f3d in jl_f__apply_iterate (F=0x0, args=0x7ffdd3f0bf08, nargs=3) at /home/vchuravy/src/julia/src/builtins.c:738
#41 0x00007fb6915c35c9 in julia_runtime_generic_fwd_5702 (fn=0x0, arg_ptr=..., shadow_ptr=..., activity_ptr=<optimized out>, width=...) at /home/vchuravy/src/Enzyme/src/compiler.jl:636
#42 0x00007fb6915c4f4a in julia_#1_4928 (x=<optimized out>) at /home/vchuravy/src/Enzyme/issue447.jl:4
#43 0x00007fb6915c4f4a in julia_#1_4928 (x=<optimized out>)
#44 0x00007fb678701794 in macro expansion () at /home/vchuravy/src/Enzyme/src/compiler.jl:6162
#45 enzyme_call () at /home/vchuravy/src/Enzyme/src/compiler.jl:5900
#46 ForwardModeThunk () at /home/vchuravy/src/Enzyme/src/compiler.jl:5880
#47 autodiff () at /home/vchuravy/src/Enzyme/src/Enzyme.jl:314
#48 #37 () at /home/vchuravy/src/Enzyme/src/Enzyme.jl:629
#49 0x00007fb678701794 in #193 ()
#50 iterate () at generator.jl:47
#51 julia_collect_6225 (itr=...) at array.jl:787
#52 0x00007fb678701d65 in julia__ntuple_6222 (f=..., n=100) at ntuple.jl:37
#53 0x00007fb678701fe7 in julia_ntuple_6164 (f=..., n=140421721294624) at ntuple.jl:19
#54 0x00007fb678702935 in jfptr_ntuple_6165 ()
#55 0x00007fb690a4e535 in _jl_invoke (F=0x7fb67b5838e0 <jl_system_image_data+10389216>, args=0x7ffdd3f0cbe0, nargs=2, mfunc=0x7fb65a340ce0, world=32452) at /home/vchuravy/src/julia/src/gf.c:2367
#56 0x00007fb690a4eeed in ijl_apply_generic (F=0x7fb67b5838e0 <jl_system_image_data+10389216>, args=0x7ffdd3f0cbe0, nargs=2) at /home/vchuravy/src/julia/src/gf.c:2549
#57 0x00007fb678b0931a in julia_#gradient#36_3879 (shadow=..., f=0x0, x=<error reading variable: Cannot access memory at address 0x0>) at /home/vchuravy/src/Enzyme/src/Enzyme.jl:628
#58 0x00007fb678b093b9 in jfptr_#gradient#36_3880 ()

vchuravy · 2022-10-12T17:18:21Z

(rr) up
#2  wrap_annotated_args () at /home/vchuravy/src/Enzyme/src/compiler.jl:516
516	        T = Core.Typeof(p)
(rr) list
511	    __activity = Base.unsafe_wrap(Array, activity_ptr, length(arg_ptr))
512	    args = Any[]
513	    
514	    for i in start:length(arg_ptr)
515	        p = arg_ptr[i]
516	        T = Core.Typeof(p)
517	        if __activity[i] != 0 && !(GPUCompiler.isghosttype(T) || Core.Compiler.isconstType(T))
518	            if !forwardMode && (T <: AbstractFloat || T <: Complex{<:AbstractFloat})
519	                push!(args, Active(p))
520	            else

So whatever p is wrong.

wsmoses · 2023-02-17T23:18:51Z

@vchuravy the reverse mode still errs in GC with the latest jll. Forward mode is now a different but still error:

[2218940] signal (11.1): Segmentation fault
in expression starting at REPL[9]:1
typekeyvalue_hash at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/jltypes.c:1222 [inlined]
lookup_typevalue at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/jltypes.c:754
jl_inst_arg_tuple_type at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/jltypes.c:1670
arg_type_tuple at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/gf.c:2059 [inlined]
jl_lookup_generic_ at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/gf.c:2807 [inlined]
ijl_apply_generic at /cache/build/default-amdci4-7/julialang/julia-release-1-dot-9/src/gf.c:2859
unknown function (ip: 0x7fa76aa61623)
Allocations: 183686838 (Pool: 183609914; Big: 76924); GC: 275
Segmentation fault (core dumped)

@mohamed82008 have you had a chance to minimize this yet? If so, we can attempt to fix and get this working.

mohamed82008 · 2023-02-18T05:47:25Z

I can try to spend time on it next weekend.

wsmoses · 2023-03-06T04:22:51Z

any luck @mohamed82008

mohamed82008 · 2023-03-06T05:58:34Z

Not yet, got busy with something else. Sorry for the delay.

wsmoses · 2023-04-09T22:06:52Z

bump @mohamed82008

wsmoses · 2023-04-10T22:31:02Z

Reverse mode seems to no longer error on main (instead a runtime unimplemented jl_f_apply_iterate). Forward mode remains the segmentation fault.

wsmoses · 2023-04-10T22:35:47Z

using Enzyme
Enzyme.API.printall!(true)

using Gridap

function get_domain()
    #domain = (0, 160, 0, 40)
    #partition = (160, 40)
    domain = (0, 1, 0, 1)
    partition = (10, 10)
    model = CartesianDiscreteModel(domain, partition)
    labels = get_face_labeling(model)
    add_tag_from_tags!(labels, "diri_0", [1, 3, 7])
    add_tag_from_tags!(labels, "diri_1", [2, 4, 8])

    order = 1
    E = 70.0e9
    ν = 0.33
    λ = (E * ν) / ((1 + ν) * (1 - 2 * ν))
    μ = E / (2 * (1 + ν))
    σ(ε) = λ * tr(ε) * one(ε) + 2 * μ * ε

    degree = 2 * order
    Ω = Triangulation(model)
    dΩ = Measure(Ω, degree)
    T = Float64
    reffe = ReferenceFE(lagrangian, VectorValue{2, T}, order)
    V0 = TestFESpace(
        model, reffe;
        vector_type = Vector{T},
        conformity = :H1,
        dirichlet_tags = ["diri_0", "diri_1"],
        dirichlet_masks = [(true, false), (true, true)],
    )
    g1(x) = VectorValue(convert(T, 0.005), convert(T, 0.0))
    g2(x) = VectorValue(convert(T, 0.0), convert(T, 0.0))
    U = TrialFESpace(V0, [g1, g2])
    l(v) = zero(T)

    function displacements(cell_param)
        ρ = CellField(cell_param, Ω)
        a(u, v) = ρ * ε(v) ⊙ (σ ∘ ε(u))
        op = AffineFEOperator(a, l, U, V0)
        solver = LinearFESolver()
        uh = solve(solver, op)
        return uh.free_values[1]
    end, rand(num_cells(model))
end

f, x = get_domain()
# Enzyme.gradient(Enzyme.ForwardMode(), g, x)
dx = similar(x)
Enzyme.autodiff(Forward, f, Duplicated(x, dx))

@mohamed82008 yeah I have no idea what is going on here, but if you're able to make this smaller/simpler while still triggering the segfault, I can try to fix it.

mohamed82008 · 2023-04-12T01:40:33Z

Thanks Billy. I am not very proud of the many broken promises to look into this. I will try to narrow it down when I can though.

wsmoses · 2023-04-24T02:47:47Z

Should be solved by #772 please reopen if it persists.

mohamed82008 · 2023-04-28T11:36:31Z

Did you actually test this works? It doesn't on my machine.

wsmoses · 2023-04-28T14:35:02Z

I did (we fixed a GC error, which then stopped this error from reproducing after merging).

If it's still erroring for you I definitely need a MWE since it no longer reproduces for me.

mohamed82008 · 2023-04-28T14:53:24Z

Well that's odd I tested it on Mac M1 and WSL. Perhaps I need to test it on a more standard setup.

wsmoses · 2023-06-23T23:30:06Z

significant GC related fix has now landed on main. Retry to see if resolved?

unfortunately again I could not reproduce your issue even without that fix

mohamed82008 · 2023-06-26T15:35:11Z

I tried it on my M1 and it still fails. Let me try it again on Linux and Windows and get back to you.

wsmoses · 2023-06-26T16:16:35Z

Can you paste the error and also confirm you're on the latest commit?

wsmoses · 2023-07-07T15:56:15Z

Several additional GC fixes have been landed that allow turing to run.

Again retry, and see if it fixed for you (and if not we really need something to go on to try to fix).

mohamed82008 · 2023-07-07T16:13:21Z

On M1 and latest Enzyme#main and Julia 1.10, I get the following error when calling the code in #447 (comment)

mod = ; ModuleID = 'start'
source_filename = "start"
target datalayout = "e-m:o-i64:64-i128:128-n32:64-S128-ni:10:11:12:13"
target triple = "arm64-apple-darwin22.4.0"

@_j_const1 = private unnamed_addr constant double 0.000000e+00

; Function Attrs: noinline
define dso_local swiftcc nonnull {} addrspace(10)* @julia_string_4147({}*** nonnull swiftself %0, {} addrspace(10)* noundef nonnull %1) #0 !dbg !10 {
top:
  %a = alloca {} addrspace(10)*, align 8
  %2 = call {}*** @julia.get_pgcstack()
  store {} addrspace(10)* null, {} addrspace(10)** %a, align 8
  %3 = bitcast {}*** %2 to {}**
  %current_task = getelementptr inbounds {}*, {}** %3, i64 -14
  %4 = bitcast {}** %current_task to i64*
  %world_age = getelementptr inbounds i64, i64* %4, i64 15
  store {} addrspace(10)* %1, {} addrspace(10)** %a, align 8
  %5 = bitcast {}*** %2 to {}**
  %current_task1 = getelementptr inbounds {}*, {}** %5, i64 -14
  %ptls_field = getelementptr inbounds {}*, {}** %current_task1, i64 16
  %ptls_load = lIncorrect number of arguments passed to called function!
  %3 = call swiftcc {} addrspace(10)* @julia_displacements_4142_inner({}*** %2, { [1 x {} addrspace(10)*], { {} addrspace(10)*, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, i64, [5 x {} addrspace(10)*] } }, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, i64, [5 x {} addrspace(10)*] }, { { { [1 x { [1 x [2 x double]], [2 x double], [2 x i64], [2 x i8] }], [1 x [2 x i64]], { i8, [1 x [1 x i64]] } }, { {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)* }, [3 x {} addrspace(10)*] }, { [1 x { [1 x [2 x double]], [2 x double], [2 x i64], [2 x i8] }], [1 x [2 x i64]], { i8, [1 x [1 x i64]] } }, [1 x i64] }, [2 x double] } addrspace(11)* %1), !dbg !394
LLVM.API.LLVMVerifyFunction(wrapper_f, LLVM.API.LLVMPrintMessageAction) = 1
wrapper_f = ; Function Attrs: returns_twice
define {} addrspace(10)* @julia_displacements_4142_inner.1({}** %0, { [1 x {} addrspace(10)*], { {} addrspace(10)*, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, i64, [5 x {} addrspace(10)*] } }, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, i64, [5 x {} addrspace(10)*] }, { { { [1 x { [1 x [2 x double]], [2 x double], [2 x i64], [2 x i8] }], [1 x [2 x i64]], { i8, [1 x [1 x i64]] } }, { {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)* }, [3 x {} addrspace(10)*] }, { [1 x { [1 x [2 x double]], [2 x double], [2 x i64], [2 x i8] }], [1 x [2 x i64]], { i8, [1 x [1 x i64]] } }, [1 x i64] }, [2 x double] } addrspace(11)* nocapture noundef nonnull readonly align 8 dereferenceable(512) %1) #13 !dbg !57 {
entry:
  %2 = alloca {}*(parmsRemoved, retRemoved, prargs) = (UInt64[], false, Any[(cc = GPUCompiler.BITS_REF, typ = var"#displacements#5"{var"#l#4"{DataType}, TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}}, Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}, Gridap.Geometry.BodyFittedTriangulation{2, 2, CartesianDiscreteModel{2, Float64, typeof(identity)}, CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, var"#σ#1"{Float64, Float64}}, arg_i = 1, codegen = (typ = {}***, i = 1)), (cc = GPUCompiler.BITS_REF, typ = Vector{Float64}, arg_i = 2, codegen = (typ = { [1 x {} addrspace(10)*], { {} addrspace(10)*, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, {} addrspace(10)*, i64, [5 x {} addrspace(10)*] } }, { {} addrspace(10)*, i64, i64, {} addrspace(10)*, {} addrspace(10)*ERROR: LLVM error: broken function
Stacktrace:
  [1] lower_convention(functy::Type, mod::LLVM.Module, entry_f::LLVM.Function, actualRetType::Type)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:8652
  [2] codegen(output::Symbol, job::GPUCompiler.CompilerJob{…}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, ctx::LLVM.ThreadSafeContext, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9044
  [3] _thunk(job::GPUCompiler.CompilerJob{…}, ctx::Nothing, postopt::Bool)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9603
  [4] _thunk(job::Any, ctx::Any, postopt::Any)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9600 [inlined]
  [5] cached_compilation(job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams})
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9638 [inlined]
  [6] #s272#222
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9700 [inlined]
  [7] 
    @ Enzyme.Compiler ./none:0
  [8] (::Core.GeneratedFunctionStub)(::UInt64, ::LineNumberNode, ::Any, ::Vararg{Any})
    @ Core ./boot.jl:600
  [9] thunk(::Val{…}, ::Type{…}, ::Type{…}, tt::Type{…}, ::Val{…}, ::Val{…}, ::Val{…}, ::Val{…})
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9655 [inlined]
 [10] autodiff(::EnzymeCore.ForwardMode, f::Const{…}, ::Type{…}, args::Duplicated{…})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:320
 [11] autodiff(mode::EnzymeCore.ForwardMode, f::Const{var"#displacements#5"{…}}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:228 [inlined]
 [12] autodiff(mode::EnzymeCore.ForwardMode, f::var"#displacements#5"{…}, args::Duplicated{…})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:214
 [13] top-level scope
    @ REPL[12]:1
Some type information was truncated. Use `show(err)` to see complete types.

Calling show(err) gives:

1-element ExceptionStack:
LLVM error: broken function
Stacktrace:
  [1] lower_convention(functy::Type, mod::LLVM.Module, entry_f::LLVM.Function, actualRetType::Type)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:8652
  [2] codegen(output::Symbol, job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, ctx::LLVM.ThreadSafeContext, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9044
  [3] _thunk(job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams}, ctx::Nothing, postopt::Bool)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9603
  [4] _thunk(job::Any, ctx::Any, postopt::Any)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9600 [inlined]
  [5] cached_compilation(job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams})
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9638 [inlined]
  [6] #s272#222
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9700 [inlined]
  [7] var"#s272#222"(FA::Any, A::Any, TT::Any, Mode::Any, ModifiedBetween::Any, width::Any, ReturnPrimal::Any, ShadowInit::Any, World::Any, ::Any, ::Any, ::Any, ::Any, tt::Any, ::Any, ::Any, ::Any, ::Any, ::Any)
    @ Enzyme.Compiler ./none:0
  [8] (::Core.GeneratedFunctionStub)(::UInt64, ::LineNumberNode, ::Any, ::Vararg{Any})
    @ Core ./boot.jl:600
  [9] thunk(::Val{0x0000000000007b61}, ::Type{Const{var"#displacements#5"{var"#l#4"{DataType}, TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}}, Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}, Gridap.Geometry.BodyFittedTriangulation{2, 2, CartesianDiscreteModel{2, Float64, typeof(identity)}, CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, var"#σ#1"{Float64, Float64}}}}, ::Type{DuplicatedNoNeed{Any}}, tt::Type{Tuple{Duplicated{Vector{Float64}}}}, ::Val{Enzyme.API.DEM_ForwardMode}, ::Val{1}, ::Val{(false, false)}, ::Val{false})
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:9655 [inlined]
 [10] autodiff(::EnzymeCore.ForwardMode, f::Const{var"#displacements#5"{var"#l#4"{DataType}, TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}}, Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}, Gridap.Geometry.BodyFittedTriangulation{2, 2, CartesianDiscreteModel{2, Float64, typeof(identity)}, CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, var"#σ#1"{Float64, Float64}}}, ::Type{DuplicatedNoNeed{Any}}, args::Duplicated{Vector{Float64}})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:320
 [11] autodiff(mode::EnzymeCore.ForwardMode, f::Const{var"#displacements#5"{var"#l#4"{DataType}, TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}}, Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}, Gridap.Geometry.BodyFittedTriangulation{2, 2, CartesianDiscreteModel{2, Float64, typeof(identity)}, CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, var"#σ#1"{Float64, Float64}}}, args::Duplicated{Vector{Float64}})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:228 [inlined]
 [12] autodiff(mode::EnzymeCore.ForwardMode, f::var"#displacements#5"{var"#l#4"{DataType}, TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}}, Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{VectorValue{2, Int32}}}, Gridap.Geometry.BodyFittedTriangulation{2, 2, CartesianDiscreteModel{2, Float64, typeof(identity)}, CartesianGrid{2, Float64, typeof(identity)}, Gridap.Arrays.IdentityVector{Int64}}, var"#σ#1"{Float64, Float64}}, args::Duplicated{Vector{Float64}})
    @ Enzyme ~/.julia/packages/Enzyme/gS4lp/src/Enzyme.jl:214
 [13] top-level scope
    @ REPL[12]:1

mohamed82008 · 2023-07-07T16:15:02Z

Ah the function f returns an error now. Let me fix it.

wsmoses · 2023-07-07T16:27:18Z

1.10 is not supported yet. Please use 1.9 or lower.

mohamed82008 · 2023-07-07T16:34:07Z

Does this work for you? It doesn't for me on M1 and Julia 1.9.

using Pkg
Pkg.add(url = "https://github.com/JuliaTopOpt/TopOptGridap.jl")
Pkg.add(name = "Gridap", rev = "master")
Pkg.add(name = "Enzyme_jll", rev = "main")
Pkg.add(name = "Enzyme", rev = "main")

using TopOptGridap, Enzyme

f, x = TopOptGridap.get_domain()
g = x -> sum(f(x))
dx = similar(x)
g(x)
Enzyme.autodiff(Forward, g, Duplicated(x, dx))

wsmoses · 2023-07-07T16:36:51Z

For future debugging sake, can you please also post the error message (as well as the particular has of the various repositories). Also you should not do Enzyme_jll#main ever. The version between Enzyme.jl and Enzyme_jll must align with each other. Enzyme_jll represents the tagged builds of the main Enzyme repo (EnzymeAD/Enzyme). Doing #main on the jll does not get the latest build, but the latest tag.

mohamed82008 · 2023-07-07T16:44:51Z

Ok let's try again. I added Enzyme_jll main because Enzyme#main required Enzyme_jll 0.0.74 which was not released it seems. I reverted to using the latest release versions.

(GridapEnv) pkg> st
Status `~/JuliaEnvs/GridapEnv/Project.toml`
  [7da242da] Enzyme v0.11.2
  [56d4f2e9] Gridap v0.17.17
  [69521377] TopOptGridap v0.1.0 `https://github.com/JuliaTopOpt/TopOptGridap.jl#master`

The following code errors.

using Pkg
Pkg.add(url = "https://github.com/JuliaTopOpt/TopOptGridap.jl")
Pkg.add(name = "Gridap")
Pkg.add(name = "Enzyme")

using TopOptGridap, Enzyme

f, x = TopOptGridap.get_domain()
g = x -> sum(f(x))
dx = similar(x)
g(x)
Enzyme.autodiff(Forward, g, Duplicated(x, dx))

Here is the error:

┌ Warning: TypeAnalysisDepthLimit
│   store {} addrspace(10)* %27, {} addrspace(10)** %.fca.0.0.1.gep, align 8, !dbg !105, !noalias !20
│ {[]:Pointer, [0,0]:Float@double, [0,8]:Float@double, [0,16]:Float@double, [0,24]:Float@double, [0,32]:Integer, [0,33]:Integer, [0,34]:Integer, [0,35]:Integer, [0,36]:Integer, [0,37]:Integer, [0,38]:Integer, [0,39]:Integer, [0,40]:Integer, [0,41]:Integer, [0,42]:Integer, [0,43]:Integer, [0,44]:Integer, [0,45]:Integer, [0,46]:Integer, [0,47]:Integer, [0,48]:Integer, [0,49]:Integer, [0,56]:Integer, [0,57]:Integer, [0,58]:Integer, [0,59]:Integer, [0,60]:Integer, [0,61]:Integer, [0,62]:Integer, [0,63]:Integer, [0,64]:Integer, [0,65]:Integer, [0,66]:Integer, [0,67]:Integer, [0,68]:Integer, [0,69]:Integer, [0,70]:Integer, [0,71]:Integer, [0,72]:Integer, [0,80]:Integer, [0,81]:Integer, [0,82]:Integer, [0,83]:Integer, [0,84]:Integer, [0,85]:Integer, [0,86]:Integer, [0,87]:Integer, [0,88]:Pointer, [0,88,0]:Pointer, [0,88,0,0]:Float@double, [0,88,8]:Integer, [0,88,9]:Integer, [0,88,10]:Integer, [0,88,11]:Integer, [0,88,12]:Integer, [0,88,13]:Integer, [0,88,14]:Integer, [0,88,15]:Integer, [0,88,16]:Integer, [0,88,17]:Integer, [0,88,18]:Integer, [0,88,19]:Integer, [0,88,20]:Integer, [0,88,21]:Integer, [0,88,22]:Integer, [0,88,23]:Integer, [0,88,24]:Integer, [0,88,25]:Integer, [0,88,26]:Integer, [0,88,27]:Integer, [0,88,28]:Integer, [0,88,29]:Integer, [0,88,30]:Integer, [0,88,31]:Integer, [0,88,32]:Integer, [0,88,33]:Integer, [0,88,34]:Integer, [0,88,35]:Integer, [0,88,36]:Integer, [0,88,37]:Integer, [0,88,38]:Integer, [0,88,39]:Integer, [0,96]:Pointer, [0,96,0]:Pointer, [0,96,0,0]:Pointer, [0,96,0,0,0]:Pointer, [0,96,0,0,0,0]:Integer, [0,96,0,0,8]:Integer, [0,96,0,0,9]:Integer, [0,96,0,0,10]:Integer, [0,96,0,0,11]:Integer, [0,96,0,0,12]:Integer, [0,96,0,0,13]:Integer, [0,96,0,0,14]:Integer, [0,96,0,0,15]:Integer, [0,96,0,0,16]:Integer, [0,96,0,0,17]:Integer, [0,96,0,0,18]:Integer, [0,96,0,0,19]:Integer, [0,96,0,0,20]:Integer, [0,96,0,0,21]:Integer, [0,96,0,0,22]:Integer, [0,96,0,0,23]:Integer, [0,96,0,0,24]:Integer, [0,96,0,0,25]:Integer, [0,96,0,0,26]:Integer, [0,96,0,0,27]:Integer, [0,96,0,0,28]:Integer, [0,96,0,0,29]:Integer, [0,96,0,0,30]:Integer, [0,96,0,0,31]:Integer, [0,96,0,0,32]:Integer, [0,96,0,0,33]:Integer, [0,96,0,0,34]:Integer, [0,96,0,0,35]:Integer, [0,96,0,0,36]:Integer, [0,96,0,0,37]:Integer, [0,96,0,0,38]:Integer, [0,96,0,0,39]:Integer, [0,96,8]:Integer, [0,96,9]:Integer, [0,96,10]:Integer, [0,96,11]:Integer, [0,96,12]:Integer, [0,96,13]:Integer, [0,96,14]:Integer, [0,96,15]:Integer, [0,96,16]:Integer, [0,96,17]:Integer, [0,96,18]:Integer, [0,96,19]:Integer, [0,96,20]:Integer, [0,96,21]:Integer, [0,96,22]:Integer, [0,96,23]:Integer, [0,96,24]:Integer, [0,96,25]:Integer, [0,96,26]:Integer, [0,96,27]:Integer, [0,96,28]:Integer, [0,96,29]:Integer, [0,96,30]:Integer, [0,96,31]:Integer, [0,96,32]:Integer, [0,96,33]:Integer, [0,96,34]:Integer, [0,96,35]:Integer, [0,96,36]:Integer, [0,96,37]:Integer, [0,96,38]:Integer, [0,96,39]:Integer, [0,104]:Pointer, [0,104,0]:Pointer, [0,104,0,0]:Integer, [0,104,8]:Integer, [0,104,9]:Integer, 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[7,239]:Integer}
│ 
│ Stacktrace:
│  [1] AffineFEOperator
│    @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:47
│  [2] AffineFEOperator
│    @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:51
│  [3] displacements
│    @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:42
│  [4] displacements
│    @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:0
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/cy24l/src/utils.jl:56
┌ Warning: TypeAnalysisDepthLimit
│   store {} addrspace(10)* %27, {} addrspace(10)** %.fca.0.0.1.gep, align 8, !dbg !105, !noalias !20
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[0,238]:Integer, [0,239]:Integer}
│ 
│ Stacktrace:
│  [1] AffineFEOperator
│    @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:47
│  [2] AffineFEOperator
│    @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:51
│  [3] displacements
│    @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:42
│  [4] displacements
│    @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:0
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/cy24l/src/utils.jl:56
ERROR: Enzyme execution failed.
Mismatched activity for:   store {} addrspace(10)* %.fca.0.load, {} addrspace(10)** %.fca.0.1.0.gep, align 8, !dbg !105, !noalias !20 const val:   %.fca.0.load = load {} addrspace(10)*, {} addrspace(10)** %.fca.3.0.gep, align 8, !dbg !65, !alias.scope !66, !noalias !67
Type tree: {}
You may be using a constant variable as temporary storage for active memory (https://enzyme.mit.edu/julia/stable/#Activity-of-temporary-storage). If not, please open an issue, and either rewrite this variable to not be conditionally active or use Enzyme.API.runtimeActivity!(true) as a workaround for now

Stacktrace:
 [1] AffineFEOperator
   @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:47
 [2] AffineFEOperator
   @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:51
 [3] displacements
   @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:42
 [4] displacements
   @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:0

Stacktrace:
 [1] throwerr(cstr::Cstring)
   @ Enzyme.Compiler ~/.julia/packages/Enzyme/gS4lp/src/compiler.jl:2924

wsmoses · 2023-07-07T16:51:55Z

It was released, but recently so you may need to run an update. (e.g. ] up).

Also please try the latest commit of Enzyme (it includes the GC fixes).

Locally I see it nolonger segfault, but throw an error about a type instability in your code:

# see the error message thrown above
Enzyme.API.runtimeActivity!(true)
# to not print those warnings
Enzyme.API.typeWarning!(false)
julia> Enzyme.autodiff(Forward, g, Duplicated(x, dx))


ERROR: Enzyme execution failed.
Enzyme: unhandled augmented forward for jl_f__apply_iterate
Stacktrace:
 [1] return_value
   @ ~/.julia/packages/Gridap/hKSot/src/Arrays/PosNegReindex.jl:21
 [2] return_type
   @ ~/.julia/packages/Gridap/hKSot/src/Arrays/Maps.jl:62
 [3] return_value
   @ ~/.julia/packages/Gridap/hKSot/src/Arrays/Maps.jl:190

Stacktrace:
 [1] throwerr(cstr::Cstring)
   @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:2834
 [2] macro expansion
   @ ~/git/Enzyme.jl/src/compiler.jl:9463 [inlined]
 [3] enzyme_call
   @ ~/git/Enzyme.jl/src/compiler.jl:9140 [inlined]
 [4] ForwardModeThunk
   @ ~/git/Enzyme.jl/src/compiler.jl:9106 [inlined]
 [5] runtime_generic_fwd(activity::Val{(false, true, true)}, width::Val{1}, RT::Val{NamedTuple{(Symbol("1"), Symbol("2")), Tuple{Any, Any}}}, f::typeof(Gridap.Arrays.lazy_map), df::Nothing, primal_1::Gridap.Arrays.Broadcasting{Gridap.Arrays.PosNegReindex{Vector{Float64}, Vector{Float64}}}, shadow_1_1::Gridap.Arrays.Broadcasting{Gridap.Arrays.PosNegReindex{Vector{Float64}, Vector{Float64}}}, primal_2::Gridap.Arrays.Table{Int32, Vector{Int32}, Vector{Int32}}, shadow_2_1::Gridap.Arrays.Table{Int32, Vector{Int32}, Vector{Int32}})
   @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:1223
 [6] macro expansion
   @ ~/git/Enzyme.jl/src/compiler.jl:9463 [inlined]
 [7] enzyme_call
   @ ~/git/Enzyme.jl/src/compiler.jl:9140 [inlined]
 [8] ForwardModeThunk
   @ ~/git/Enzyme.jl/src/compiler.jl:9106 [inlined]
 [9] runtime_generic_fwd(activity::Val{(false, true, true, false)}, width::Val{1}, RT::Val{NamedTuple{(Symbol("1"), Symbol("2")), Tuple{Any, Any}}}, f::Type{Gridap.FESpaces.FEFunction}, df::Nothing, primal_1::Gridap.FESpaces.TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{Gridap.TensorValues.VectorValue{2, Int32}}}}, shadow_1_1::Gridap.FESpaces.TrialFESpace{Gridap.FESpaces.UnconstrainedFESpace{Vector{Float64}, Gridap.FESpaces.NodeToDofGlue{Gridap.TensorValues.VectorValue{2, Int32}}}}, primal_2::Vector{Float64}, shadow_2_1::Vector{Float64}, primal_3::Vector{Float64}, shadow_3_1::Nothing)
   @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:1223

wsmoses · 2023-08-09T00:11:43Z

Since I still cannot reproduce an error, closing. Please reopen if it still has an unexpected error for you.

Note that per the first error message you see:

Mismatched activity for:   store {} addrspace(10)* %.fca.0.load, {} addrspace(10)** %.fca.0.1.0.gep, align 8, !dbg !105, !noalias !20 const val:   %.fca.0.load = load {} addrspace(10)*, {} addrspace(10)** %.fca.3.0.gep, align 8, !dbg !65, !alias.scope !66, !noalias !67
Type tree: {}
You may be using a constant variable as temporary storage for active memory (https://enzyme.mit.edu/julia/stable/#Activity-of-temporary-storage). If not, please open an issue, and either rewrite this variable to not be conditionally active or use Enzyme.API.runtimeActivity!(true) as a workaround for now

Stacktrace:
 [1] AffineFEOperator
   @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:47
 [2] AffineFEOperator
   @ ~/.julia/packages/Gridap/971dU/src/FESpaces/AffineFEOperators.jl:51
 [3] displacements
   @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:42
 [4] displacements
   @ ~/.julia/packages/TopOptGridap/uKKM6/src/TopOptGridap.jl:0

you have a part of the code that mixes constant and active data in the same struct (the workaround suggested indeed gets past it).

And finally as mentioned just above, there's a type unstable part of your code that is not yet implemented and noted here: #599.

wsmoses added the segfault label Oct 22, 2022

mohamed82008 mentioned this issue Nov 3, 2022

Element-based topology optimization JuliaTopOpt/TopOptGridap.jl#2

Open

wsmoses added the gc garbage collection label Dec 25, 2022

wsmoses added the more information needed label Mar 6, 2023

wsmoses closed this as completed Apr 24, 2023

wsmoses reopened this Apr 28, 2023

wsmoses closed this as completed Aug 9, 2023

Differentiating Gridap code #447

Differentiating Gridap code #447

Comments

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edited