SIMD support for math intrinsics

[simeonschaub]

closes #376

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diff --git a/lib/intrinsics/ext/SPIRVIntrinsicsSIMDExt.jl b/lib/intrinsics/ext/SPIRVIntrinsicsSIMDExt.jl
index 1ecaa61..b1a3cd9 100644
--- a/lib/intrinsics/ext/SPIRVIntrinsicsSIMDExt.jl
+++ b/lib/intrinsics/ext/SPIRVIntrinsicsSIMDExt.jl
@@ -9,8 +9,8 @@ const known_intrinsics = String[]
 
 # Generate vectorized math intrinsics
 for N in [2, 3, 4, 8, 16], T in [Float16, Float32, Float64]
-    VT = :(Vec{$N,$T})
-    LVT = :(SIMD.LVec{$N,$T})
+    VT = :(Vec{$N, $T})
+    LVT = :(SIMD.LVec{$N, $T})
 
     @eval begin
         # Unary operations
@@ -98,8 +98,8 @@ for N in [2, 3, 4, 8, 16], T in [Float16, Float32, Float64]
     end
 
     # Special operations with Int32 parameters
-    VIntT = :(Vec{$N,Int32})
-    LVIntT = :(SIMD.LVec{$N,Int32})
+    VIntT = :(Vec{$N, Int32})
+    LVIntT = :(SIMD.LVec{$N, Int32})
 
     @eval begin
         @device_function SPIRVIntrinsics.ilogb(x::$VT) = $VIntT(@builtin_ccall("ilogb", $LVIntT, ($LVT,), x.data))
@@ -112,9 +112,9 @@ end
 # nan functions - take unsigned integer codes and return floats
 for N in [2, 3, 4, 8, 16]
     @eval begin
-        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N,UInt16}) = Vec{$N,Float16}(@builtin_ccall("nan", SIMD.LVec{$N,Float16}, (SIMD.LVec{$N,UInt16},), nancode.data))
-        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N,UInt32}) = Vec{$N,Float32}(@builtin_ccall("nan", SIMD.LVec{$N,Float32}, (SIMD.LVec{$N,UInt32},), nancode.data))
-        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N,UInt64}) = Vec{$N,Float64}(@builtin_ccall("nan", SIMD.LVec{$N,Float64}, (SIMD.LVec{$N,UInt64},), nancode.data))
+        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N, UInt16}) = Vec{$N, Float16}(@builtin_ccall("nan", SIMD.LVec{$N, Float16}, (SIMD.LVec{$N, UInt16},), nancode.data))
+        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N, UInt32}) = Vec{$N, Float32}(@builtin_ccall("nan", SIMD.LVec{$N, Float32}, (SIMD.LVec{$N, UInt32},), nancode.data))
+        @device_function SPIRVIntrinsics.nan(nancode::Vec{$N, UInt64}) = Vec{$N, Float64}(@builtin_ccall("nan", SIMD.LVec{$N, Float64}, (SIMD.LVec{$N, UInt64},), nancode.data))
     end
 end
 
diff --git a/lib/intrinsics/src/utils.jl b/lib/intrinsics/src/utils.jl
index 3e81fe7..0fdb74b 100644
--- a/lib/intrinsics/src/utils.jl
+++ b/lib/intrinsics/src/utils.jl
@@ -88,7 +88,7 @@ Base.Experimental.@MethodTable(method_table)
 
 macro device_override(ex)
     esc(quote
-        Base.Experimental.@overlay($method_table, $ex)
+            Base.Experimental.@overlay($method_table, $ex)
     end)
 end
 
diff --git a/src/compiler/compilation.jl b/src/compiler/compilation.jl
index 4ed2811..d68668b 100644
--- a/src/compiler/compilation.jl
+++ b/src/compiler/compilation.jl
@@ -17,8 +17,8 @@ GPUCompiler.isintrinsic(job::OpenCLCompilerJob, fn::String) =
            job, fn) ||
     in(fn, known_intrinsics) ||
     let SPIRVIntrinsicsSIMDExt = Base.get_extension(SPIRVIntrinsics, :SPIRVIntrinsicsSIMDExt)
-        SPIRVIntrinsicsSIMDExt !== nothing && in(fn, SPIRVIntrinsicsSIMDExt.known_intrinsics)
-    end ||
+    SPIRVIntrinsicsSIMDExt !== nothing && in(fn, SPIRVIntrinsicsSIMDExt.known_intrinsics)
+end ||
     contains(fn, "__spirv_")
 
 
diff --git a/test/intrinsics.jl b/test/intrinsics.jl
index ff6ded4..677e9ec 100644
--- a/test/intrinsics.jl
+++ b/test/intrinsics.jl
@@ -165,61 +165,61 @@ end
     @test call_on_device(OpenCL.mad, x, y, z) ≈ x * y + z
 end
 
-@testset "SIMD - $N x $T" for N in simd_ns, T in float_types
-    v = Vec{N, T}(ntuple(_ -> rand(T), N))
-
-    # unary ops: sin, cos, sqrt
-    a = call_on_device(sin, v)
-    @test all(a[i] ≈ sin(v[i]) for i in 1:N)
-
-    b = call_on_device(cos, v)
-    @test all(b[i] ≈ cos(v[i]) for i in 1:N)
-
-    c = call_on_device(sqrt, v)
-    @test all(c[i] ≈ sqrt(v[i]) for i in 1:N)
-
-    # binary ops: max, hypot
-    w = Vec{N, T}(ntuple(_ -> rand(T), N))
-    d = call_on_device(max, v, w)
-    @test all(d[i] == max(v[i], w[i]) for i in 1:N)
-
-    broken = ispocl && T == Float16
-    if !broken
-        h = call_on_device(hypot, v, w)
-        @test all(h[i] ≈ hypot(v[i], w[i]) for i in 1:N)
-    end
-
-    # ternary op: fma
-    x = Vec{N, T}(ntuple(_ -> rand(T), N))
-    e = call_on_device(fma, v, w, x)
-    @test all(e[i] ≈ fma(v[i], w[i], x[i]) for i in 1:N)
-
-    # special cases: ilogb, ldexp, ^ with Int32, rootn
-    v_pos = Vec{N, T}(ntuple(_ -> rand(T) + T(1), N))
-    @test call_on_device(OpenCL.ilogb, v_pos) isa Vec{N, Int32} broken = broken
-
-    k = Vec{N, Int32}(ntuple(_ -> rand(Int32.(-5:5)), N))
-    @test let
-        ldexp_result = call_on_device(ldexp, v_pos, k)
-        all(ldexp_result[i] ≈ ldexp(v_pos[i], k[i]) for i in 1:N)
-    end broken = broken
-
-    base = Vec{N, T}(ntuple(_ -> rand(T) + T(0.5), N))
-    exp_int = Vec{N, Int32}(ntuple(_ -> rand(Int32.(0:3)), N))
-    @test let
-        pow_result = call_on_device(^, base, exp_int)
-        all(pow_result[i] ≈ base[i] ^ exp_int[i] for i in 1:N)
-    end broken = broken
-
-    rootn_base = Vec{N, T}(ntuple(_ -> rand(T) * T(10) + T(1), N))
-    rootn_n = Vec{N, Int32}(ntuple(_ -> rand(Int32.(2:4)), N))
-    @test call_on_device(OpenCL.rootn, rootn_base, rootn_n) isa Vec{N, T} broken = broken
-
-    # special cases: nan
-    nan_code = Vec{N, Base.uinttype(T)}(ntuple(_ -> rand(Base.uinttype(T)), N))
-    nan_result = call_on_device(OpenCL.nan, nan_code)
-    @test all(isnan(nan_result[i]) for i in 1:N)
-end
+        @testset "SIMD - $N x $T" for N in simd_ns, T in float_types
+            v = Vec{N, T}(ntuple(_ -> rand(T), N))
+
+            # unary ops: sin, cos, sqrt
+            a = call_on_device(sin, v)
+            @test all(a[i] ≈ sin(v[i]) for i in 1:N)
+
+            b = call_on_device(cos, v)
+            @test all(b[i] ≈ cos(v[i]) for i in 1:N)
+
+            c = call_on_device(sqrt, v)
+            @test all(c[i] ≈ sqrt(v[i]) for i in 1:N)
+
+            # binary ops: max, hypot
+            w = Vec{N, T}(ntuple(_ -> rand(T), N))
+            d = call_on_device(max, v, w)
+            @test all(d[i] == max(v[i], w[i]) for i in 1:N)
+
+            broken = ispocl && T == Float16
+            if !broken
+                h = call_on_device(hypot, v, w)
+                @test all(h[i] ≈ hypot(v[i], w[i]) for i in 1:N)
+            end
+
+            # ternary op: fma
+            x = Vec{N, T}(ntuple(_ -> rand(T), N))
+            e = call_on_device(fma, v, w, x)
+            @test all(e[i] ≈ fma(v[i], w[i], x[i]) for i in 1:N)
+
+            # special cases: ilogb, ldexp, ^ with Int32, rootn
+            v_pos = Vec{N, T}(ntuple(_ -> rand(T) + T(1), N))
+            @test call_on_device(OpenCL.ilogb, v_pos) isa Vec{N, Int32} broken = broken
+
+            k = Vec{N, Int32}(ntuple(_ -> rand(Int32.(-5:5)), N))
+            @test let
+                ldexp_result = call_on_device(ldexp, v_pos, k)
+                all(ldexp_result[i] ≈ ldexp(v_pos[i], k[i]) for i in 1:N)
+            end broken = broken
+
+            base = Vec{N, T}(ntuple(_ -> rand(T) + T(0.5), N))
+            exp_int = Vec{N, Int32}(ntuple(_ -> rand(Int32.(0:3)), N))
+            @test let
+                pow_result = call_on_device(^, base, exp_int)
+                all(pow_result[i] ≈ base[i]^exp_int[i] for i in 1:N)
+            end broken = broken
+
+            rootn_base = Vec{N, T}(ntuple(_ -> rand(T) * T(10) + T(1), N))
+            rootn_n = Vec{N, Int32}(ntuple(_ -> rand(Int32.(2:4)), N))
+            @test call_on_device(OpenCL.rootn, rootn_base, rootn_n) isa Vec{N, T} broken = broken
+
+            # special cases: nan
+            nan_code = Vec{N, Base.uinttype(T)}(ntuple(_ -> rand(Base.uinttype(T)), N))
+            nan_result = call_on_device(OpenCL.nan, nan_code)
+            @test all(isnan(nan_result[i]) for i in 1:N)
+        end
 
 end
 

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 79.04%. Comparing base (d0e3372) to head (7e0917b).

Additional details and impacted files

@@               Coverage Diff               @@
##           sds/float16     #379      +/-   ##
===============================================
+ Coverage        79.01%   79.04%   +0.03%     
===============================================
  Files               12       12              
  Lines              672      673       +1     
===============================================
+ Hits               531      532       +1     
  Misses             141      141              

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