[pocl] RNG support

Project.toml

@@ -1,7 +1,7 @@
 name = "KernelAbstractions"
 uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
-authors = ["Valentin Churavy <v.churavy@gmail.com> and contributors"]
 version = "0.10.0-dev"
+authors = ["Valentin Churavy <v.churavy@gmail.com> and contributors"]
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -13,6 +13,9 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 OpenCL_jll = "6cb37087-e8b6-5417-8430-1f242f1e46e4"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Random123 = "74087812-796a-5b5d-8853-05524746bad3"
+RandomNumbers = "e6cf234a-135c-5ec9-84dd-332b85af5143"
 SPIRVIntrinsics = "71d1d633-e7e8-4a92-83a1-de8814b09ba8"
 SPIRV_LLVM_Backend_jll = "4376b9bf-cff8-51b6-bb48-39421dff0d0c"
 SPIRV_Tools_jll = "6ac6d60f-d740-5983-97d7-a4482c0689f4"
@@ -34,12 +37,15 @@ SparseArraysExt = "SparseArrays"
 Adapt = "0.4, 1.0, 2.0, 3.0, 4"
 Atomix = "0.1, 1"
 EnzymeCore = "0.7, 0.8.1"
-GPUCompiler = "1.6"
+GPUCompiler = "1.7.1"
 InteractiveUtils = "1.6"
 LLVM = "9.4.1"
 LinearAlgebra = "1.6"
 MacroTools = "0.5"
 PrecompileTools = "1"
+Random = "1"
+Random123 = "1.7.1"
+RandomNumbers = "1.6.0"
 SPIRVIntrinsics = "0.5"
 SPIRV_LLVM_Backend_jll = "20"
 SPIRV_Tools_jll = "2024.4, 2025.1"

src/pocl/compiler/compilation.jl

@@ -19,6 +19,87 @@ GPUCompiler.isintrinsic(job::OpenCLCompilerJob, fn::String) =
     in(fn, known_intrinsics) ||
     contains(fn, "__spirv_")
 
+GPUCompiler.kernel_state_type(::OpenCLCompilerJob) = KernelState
+
+function GPUCompiler.finish_module!(@nospecialize(job::OpenCLCompilerJob),
+                                          mod::LLVM.Module, entry::LLVM.Function)
+    entry = invoke(GPUCompiler.finish_module!,
+                   Tuple{CompilerJob{SPIRVCompilerTarget}, LLVM.Module, LLVM.Function},
+                   job, mod, entry)
+
+    # if this kernel uses our RNG, we should prime the shared state.
+    # XXX: these transformations should really happen at the Julia IR level...
+    if haskey(functions(mod), "julia.opencl.random_keys") && job.config.kernel
+        # insert call to `initialize_rng_state`
+        f = initialize_rng_state
+        ft = typeof(f)
+        tt = Tuple{}
+
+        # create a deferred compilation job for `initialize_rng_state`
+        src = methodinstance(ft, tt, GPUCompiler.tls_world_age())
+        cfg = CompilerConfig(job.config; kernel=false, name=nothing)
+        job = CompilerJob(src, cfg, job.world)
+        id = length(GPUCompiler.deferred_codegen_jobs) + 1
+        GPUCompiler.deferred_codegen_jobs[id] = job
+
+        # generate IR for calls to `deferred_codegen` and the resulting function pointer
+        top_bb = first(blocks(entry))
+        bb = BasicBlock(top_bb, "initialize_rng")
+        @dispose builder=IRBuilder() begin
+            position!(builder, bb)
+            subprogram = LLVM.subprogram(entry)
+            if subprogram !== nothing
+                loc = DILocation(0, 0, subprogram)
+                debuglocation!(builder, loc)
+            end
+            debuglocation!(builder, first(instructions(top_bb)))
+
+            # call the `deferred_codegen` marker function
+            T_ptr = if LLVM.version() >= v"17"
+                LLVM.PointerType()
+            elseif VERSION >= v"1.12.0-DEV.225"
+                LLVM.PointerType(LLVM.Int8Type())
+            else
+                LLVM.Int64Type()
+            end
+            T_id = convert(LLVMType, Int)
+            deferred_codegen_ft = LLVM.FunctionType(T_ptr, [T_id])
+            deferred_codegen = if haskey(functions(mod), "deferred_codegen")
+                functions(mod)["deferred_codegen"]
+            else
+                LLVM.Function(mod, "deferred_codegen", deferred_codegen_ft)
+            end
+            fptr = call!(builder, deferred_codegen_ft, deferred_codegen, [ConstantInt(id)])
+
+            # call the `initialize_rng_state` function
+            rt = Core.Compiler.return_type(f, tt)
+            llvm_rt = convert(LLVMType, rt)
+            llvm_ft = LLVM.FunctionType(llvm_rt)
+            fptr = inttoptr!(builder, fptr, LLVM.PointerType(llvm_ft))
+            call!(builder, llvm_ft, fptr)
+            br!(builder, top_bb)
+
+            # note the use of the device-side RNG in this kernel
+            push!(function_attributes(entry), StringAttribute("julia.opencl.rng", ""))
+        end
+
+        # XXX: put some of the above behind GPUCompiler abstractions
+        #      (e.g., a compile-time version of `deferred_codegen`)
+    end
+    return entry
+end
+
+function GPUCompiler.finish_linked_module!(@nospecialize(job::OpenCLCompilerJob), mod::LLVM.Module)
+    for f in GPUCompiler.kernels(mod)
+        kernel_intrinsics = Dict(
+            "julia.opencl.random_keys" => (; name = "random_keys", typ = LLVMPtr{UInt32, AS.Workgroup}),
+            "julia.opencl.random_counters" => (; name = "random_counters", typ = LLVMPtr{UInt32, AS.Workgroup}),
+        )
+        GPUCompiler.add_input_arguments!(job, mod, f, kernel_intrinsics)
+    end
+    return
+end
+
 
 ## compiler implementation (cache, configure, compile, and link)
 
@@ -60,10 +141,13 @@ end
 function compile(@nospecialize(job::CompilerJob))
     # TODO: this creates a context; cache those.
     obj, meta = JuliaContext() do ctx
-        GPUCompiler.compile(:obj, job)
-    end
+        obj, meta = GPUCompiler.compile(:obj, job)
 
-    return (; obj, entry = LLVM.name(meta.entry))
+        entry = LLVM.name(meta.entry)
+        device_rng = StringAttribute("julia.opencl.rng", "") in collect(function_attributes(meta.entry))
+
+        (; obj, entry, device_rng)
+    end
 end
 
 # link into an executable kernel
@@ -74,5 +158,5 @@ function link(@nospecialize(job::CompilerJob), compiled)
         error("Your device does not support SPIR-V, which is currently required for native execution.")
     end
     cl.build!(prog)
-    return cl.Kernel(prog, compiled.entry)
+    (; kernel=cl.Kernel(prog, compiled.entry), compiled.device_rng)
 end

src/pocl/compiler/execution.jl

@@ -146,6 +146,8 @@ end
 
 abstract type AbstractKernel{F, TT} end
 
+pass_arg(@nospecialize dt) = !(GPUCompiler.isghosttype(dt) || Core.Compiler.isconstType(dt))
+
 @inline @generated function (kernel::AbstractKernel{F, TT})(
         args...;
         call_kwargs...
@@ -154,8 +156,7 @@ abstract type AbstractKernel{F, TT} end
     args = (:(kernel.f), (:(clconvert(args[$i], svm_pointers)) for i in 1:length(args))...)
 
     # filter out ghost arguments that shouldn't be passed
-    predicate = dt -> GPUCompiler.isghosttype(dt) || Core.Compiler.isconstType(dt)
-    to_pass = map(!predicate, sig.parameters)
+    to_pass = map(pass_arg, sig.parameters)
     call_t = Type[x[1] for x in zip(sig.parameters, to_pass) if x[2]]
     call_args = Union{Expr, Symbol}[x[1] for x in zip(args, to_pass)            if x[2]]
 
@@ -167,12 +168,15 @@ abstract type AbstractKernel{F, TT} end
         end
     end
 
+    pushfirst!(call_t, KernelState)
+    pushfirst!(call_args, :(KernelState(kernel.rng_state ? Base.rand(UInt32) : UInt32(0))))
+
     # finalize types
     call_tt = Base.to_tuple_type(call_t)
 
     return quote
         svm_pointers = Ptr{Cvoid}[]
-        $cl.clcall(kernel.fun, $call_tt, $(call_args...); svm_pointers, call_kwargs...)
+        $cl.clcall(kernel.fun, $call_tt, $(call_args...); svm_pointers, kernel.rng_state, call_kwargs...)
     end
 end
 
@@ -182,6 +186,7 @@ end
 struct HostKernel{F, TT} <: AbstractKernel{F, TT}
     f::F
     fun::cl.Kernel
+    rng_state::Bool
 end
 
 
@@ -198,15 +203,15 @@ function clfunction(f::F, tt::TT = Tuple{}; kwargs...) where {F, TT}
         cache = compiler_cache(ctx)
         source = methodinstance(F, tt)
         config = compiler_config(dev; kwargs...)::OpenCLCompilerConfig
-        fun = GPUCompiler.cached_compilation(cache, source, config, compile, link)
+        linked = GPUCompiler.cached_compilation(cache, source, config, compile, link)
 
         # create a callable object that captures the function instance. we don't need to think
         # about world age here, as GPUCompiler already does and will return a different object
-        h = hash(fun, hash(f, hash(tt)))
+        h = hash(linked.kernel, hash(f, hash(tt)))
         kernel = get(_kernel_instances, h, nothing)
         if kernel === nothing
             # create the kernel state object
-            kernel = HostKernel{F, tt}(f, fun)
+            kernel = HostKernel{F, tt}(f, linked.kernel, linked.device_rng)
             _kernel_instances[h] = kernel
         end
         return kernel::HostKernel{F, tt}