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William Moses
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2 files changed

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‎Project.toml‎

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Original file line numberDiff line numberDiff line change
@@ -6,6 +6,7 @@ version = "0.2.7"
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[deps]
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Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
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Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
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EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"

‎ext/ReactantCUDAExt.jl‎

Lines changed: 191 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -8,35 +8,217 @@ using ReactantCore: @trace
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using Adapt
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function Adapt.adapt_storage(::CUDA.KernelAdaptor, xs::TracedRArray{T,N}) where {T,N}
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CuDeviceArray{T,N,CUDA.AS.Global}(pointer(xs.mlir_data.value), size(xs))
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res = CuDeviceArray{T,N,CUDA.AS.Global}(Base.reinterpret(Core.LLVMPtr{T,CUDA.AS.Global}, xs.mlir_data.value.ptr), size(xs))
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@show res, xs
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return res
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end
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const _kernel_instances = Dict{Any, Any}()
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# compile to executable machine code
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function compile(job)
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# lower to PTX
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# TODO: on 1.9, this actually creates a context. cache those.
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modstr = JuliaContext() do ctx
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mod, meta = GPUCompiler.compile(:llvm, job)
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string(mod)
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end
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return modstr
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#=
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# check if we'll need the device runtime
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undefined_fs = filter(collect(functions(meta.ir))) do f
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isdeclaration(f) && !LLVM.isintrinsic(f)
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end
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intrinsic_fns = ["vprintf", "malloc", "free", "__assertfail",
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"__nvvm_reflect" #= TODO: should have been optimized away =#]
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needs_cudadevrt = !isempty(setdiff(LLVM.name.(undefined_fs), intrinsic_fns))
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# prepare invocations of CUDA compiler tools
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ptxas_opts = String[]
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nvlink_opts = String[]
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## debug flags
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if Base.JLOptions().debug_level == 1
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push!(ptxas_opts, "--generate-line-info")
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elseif Base.JLOptions().debug_level >= 2
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push!(ptxas_opts, "--device-debug")
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push!(nvlink_opts, "--debug")
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end
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## relocatable device code
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if needs_cudadevrt
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push!(ptxas_opts, "--compile-only")
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end
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ptx = job.config.params.ptx
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cap = job.config.params.cap
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arch = "sm_$(cap.major)$(cap.minor)"
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# validate use of parameter memory
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argtypes = filter([KernelState, job.source.specTypes.parameters...]) do dt
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!isghosttype(dt) && !Core.Compiler.isconstType(dt)
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end
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param_usage = sum(sizeof, argtypes)
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param_limit = 4096
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if cap >= v"7.0" && ptx >= v"8.1"
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param_limit = 32764
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end
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if param_usage > param_limit
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msg = """Kernel invocation uses too much parameter memory.
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$(Base.format_bytes(param_usage)) exceeds the $(Base.format_bytes(param_limit)) limit imposed by sm_$(cap.major)$(cap.minor) / PTX v$(ptx.major).$(ptx.minor)."""
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try
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details = "\n\nRelevant parameters:"
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source_types = job.source.specTypes.parameters
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source_argnames = Base.method_argnames(job.source.def)
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while length(source_argnames) < length(source_types)
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# this is probably due to a trailing vararg; repeat its name
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push!(source_argnames, source_argnames[end])
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end
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for (i, typ) in enumerate(source_types)
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if isghosttype(typ) || Core.Compiler.isconstType(typ)
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continue
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end
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name = source_argnames[i]
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details *= "\n [$(i-1)] $name::$typ uses $(Base.format_bytes(sizeof(typ)))"
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end
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details *= "\n"
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if cap >= v"7.0" && ptx < v"8.1" && param_usage < 32764
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details *= "\nNote: use a newer CUDA to support more parameters on your device.\n"
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end
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msg *= details
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catch err
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@error "Failed to analyze kernel parameter usage; please file an issue with a reproducer."
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end
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error(msg)
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end
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# compile to machine code
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# NOTE: we use tempname since mktemp doesn't support suffixes, and mktempdir is slow
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ptx_input = tempname(cleanup=false) * ".ptx"
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ptxas_output = tempname(cleanup=false) * ".cubin"
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write(ptx_input, asm)
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# we could use the driver's embedded JIT compiler, but that has several disadvantages:
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# 1. fixes and improvements are slower to arrive, by using `ptxas` we only need to
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# upgrade the toolkit to get a newer compiler;
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# 2. version checking is simpler, we otherwise need to use NVML to query the driver
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# version, which is hard to correlate to PTX JIT improvements;
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# 3. if we want to be able to use newer (minor upgrades) of the CUDA toolkit on an
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# older driver, we should use the newer compiler to ensure compatibility.
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append!(ptxas_opts, [
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"--verbose",
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"--gpu-name", arch,
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"--output-file", ptxas_output,
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ptx_input
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])
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proc, log = run_and_collect(`$(ptxas()) $ptxas_opts`)
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log = strip(log)
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if !success(proc)
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reason = proc.termsignal > 0 ? "ptxas received signal $(proc.termsignal)" :
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"ptxas exited with code $(proc.exitcode)"
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msg = "Failed to compile PTX code ($reason)"
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msg *= "\nInvocation arguments: $(join(ptxas_opts, ' '))"
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if !isempty(log)
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msg *= "\n" * log
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end
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msg *= "\nIf you think this is a bug, please file an issue and attach $(ptx_input)"
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if parse(Bool, get(ENV, "BUILDKITE", "false"))
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run(`buildkite-agent artifact upload $(ptx_input)`)
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end
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error(msg)
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elseif !isempty(log)
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@debug "PTX compiler log:\n" * log
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end
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rm(ptx_input)
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=#
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#=
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# link device libraries, if necessary
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#
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# this requires relocatable device code, which prevents certain optimizations and
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# hurts performance. as such, we only do so when absolutely necessary.
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# TODO: try LTO, `--link-time-opt --nvvmpath /opt/cuda/nvvm`.
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# fails with `Ignoring -lto option because no LTO objects found`
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if needs_cudadevrt
147+
nvlink_output = tempname(cleanup=false) * ".cubin"
148+
append!(nvlink_opts, [
149+
"--verbose", "--extra-warnings",
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"--arch", arch,
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"--library-path", dirname(libcudadevrt),
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"--library", "cudadevrt",
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"--output-file", nvlink_output,
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ptxas_output
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])
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proc, log = run_and_collect(`$(nvlink()) $nvlink_opts`)
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log = strip(log)
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if !success(proc)
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reason = proc.termsignal > 0 ? "nvlink received signal $(proc.termsignal)" :
160+
"nvlink exited with code $(proc.exitcode)"
161+
msg = "Failed to link PTX code ($reason)"
162+
msg *= "\nInvocation arguments: $(join(nvlink_opts, ' '))"
163+
if !isempty(log)
164+
msg *= "\n" * log
165+
end
166+
msg *= "\nIf you think this is a bug, please file an issue and attach $(ptxas_output)"
167+
error(msg)
168+
elseif !isempty(log)
169+
@debug "PTX linker info log:\n" * log
170+
end
171+
rm(ptxas_output)
172+
173+
image = read(nvlink_output)
174+
rm(nvlink_output)
175+
else
176+
image = read(ptxas_output)
177+
rm(ptxas_output)
178+
end
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=#
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return (image, entry=LLVM.name(meta.entry))
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end
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# link into an executable kernel
184+
function link(job, compiled)
185+
# load as an executable kernel object
186+
return compiled
187+
end
188+
189+
struct LLVMFunc{F,tt}
190+
f::F
191+
mod::String
192+
end
193+
194+
function (func::LLVMFunc{F,tt})(args...) where{F, tt}
195+
196+
end
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16198
function recufunction(f::F, tt::TT=Tuple{}; kwargs...) where {F,TT}
17199
cuda = CUDA.active_state()
200+
@show f, tt
201+
flush(stdout)
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19203
Base.@lock CUDA.cufunction_lock begin
20204
# compile the function
21205
cache = CUDA.compiler_cache(cuda.context)
22206
source = CUDA.methodinstance(F, tt)
23207
config = CUDA.compiler_config(cuda.device; kwargs...)::CUDA.CUDACompilerConfig
24-
fun = CUDA.GPUCompiler.cached_compilation(cache, source, config, CUDA.compile, CUDA.link)
208+
fun = CUDA.GPUCompiler.cached_compilation(cache, source, config, compile, link)
25209

26210
@show fun
27-
@show fun.mod
211+
println(string(fun))
212+
#@show fun.mod
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# create a callable object that captures the function instance. we don't need to think
29214
# about world age here, as GPUCompiler already does and will return a different object
30-
key = (objectid(source), hash(fun), f)
215+
key = (objectid(source))
31216
kernel = get(_kernel_instances, key, nothing)
32217
if kernel === nothing
33-
# create the kernel state object
34-
state = CUDA.KernelState(create_exceptions!(fun.mod), UInt32(0))
35-
36-
kernel = CUDA.HostKernel{F,tt}(f, fun, state)
218+
kernel = LLVMFunc{F,tt}(f, fun)
37219
_kernel_instances[key] = kernel
38220
end
39-
return kernel::CUDA.HostKernel{F,tt}
221+
return kernel::LLVMFunc{F,tt}
40222
end
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end
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