[clang] ea72a4e - [CUDA][HIP] Fix template argument deduction

[Yaxun Liu via cfe-commits]

Author: Yaxun (Sam) Liu
Date: 2023-08-08T17:39:01-04:00
New Revision: ea72a4e6547feaa82e132746c6777b3b69aed0d5

URL: https://github.com/llvm/llvm-project/commit/ea72a4e6547feaa82e132746c6777b3b69aed0d5
DIFF: https://github.com/llvm/llvm-project/commit/ea72a4e6547feaa82e132746c6777b3b69aed0d5.diff

LOG: [CUDA][HIP] Fix template argument deduction

nvcc allows using std::malloc and std::free in device code.
When std::malloc or std::free is passed as a template
function argument with template argument deduction,
there is no diagnostics. e.g.

__global__ void kern() {
    void *p = std::malloc(1);
    std::free(p);
}
int main()
{

    std::shared_ptr<float> a;
    a = std::shared_ptr<float>(
      (float*)std::malloc(sizeof(float) * 100),
      std::free
    );
    return 0;
}
However, the same code fails to compile with clang
(https://godbolt.org/z/1roGvo6YY). The reason is
that clang does not have logic to choose a function
argument from an overloaded set of candidates
based on host/device attributes for template argument
deduction.

Currently, clang does have a logic to choose a candidate
based on the constraints of the candidates. This patch
extends that logic to account for the CUDA host/device-based
preference.

Reviewed by: Artem Belevich

Differential Revision: https://reviews.llvm.org/D154300

Added: 
    clang/test/SemaCUDA/template-arg-deduction.cu

Modified: 
    clang/lib/Sema/SemaOverload.cpp

Removed: 
    


################################################################################
diff  --git a/clang/lib/Sema/SemaOverload.cpp b/clang/lib/Sema/SemaOverload.cpp
index 3b14fb6b66e450..5d0299dfa752f9 100644
--- a/clang/lib/Sema/SemaOverload.cpp
+++ b/clang/lib/Sema/SemaOverload.cpp
@@ -12770,6 +12770,13 @@ Sema::resolveAddressOfSingleOverloadCandidate(Expr *E, DeclAccessPair &Pair) {
   DeclAccessPair DAP;
   SmallVector<FunctionDecl *, 2> AmbiguousDecls;
 
+  // Return positive for better, negative for worse, 0 for equal preference.
+  auto CheckCUDAPreference = [&](FunctionDecl *FD1, FunctionDecl *FD2) {
+    FunctionDecl *Caller = getCurFunctionDecl(/*AllowLambda=*/true);
+    return static_cast<int>(IdentifyCUDAPreference(Caller, FD1)) -
+           static_cast<int>(IdentifyCUDAPreference(Caller, FD2));
+  };
+
   auto CheckMoreConstrained = [&](FunctionDecl *FD1,
                                   FunctionDecl *FD2) -> std::optional<bool> {
     if (FunctionDecl *MF = FD1->getInstantiatedFromMemberFunction())
@@ -12800,9 +12807,31 @@ Sema::resolveAddressOfSingleOverloadCandidate(Expr *E, DeclAccessPair &Pair) {
     if (!checkAddressOfFunctionIsAvailable(FD))
       continue;
 
+    // If we found a better result, update Result.
+    auto FoundBetter = [&]() {
+      IsResultAmbiguous = false;
+      DAP = I.getPair();
+      Result = FD;
+    };
+
     // We have more than one result - see if it is more constrained than the
     // previous one.
     if (Result) {
+      // Check CUDA preference first. If the candidates have 
diff erennt CUDA
+      // preference, choose the one with higher CUDA preference. Otherwise,
+      // choose the one with more constraints.
+      if (getLangOpts().CUDA) {
+        int PreferenceByCUDA = CheckCUDAPreference(FD, Result);
+        // FD has 
diff erent preference than Result.
+        if (PreferenceByCUDA != 0) {
+          // FD is more preferable than Result.
+          if (PreferenceByCUDA > 0)
+            FoundBetter();
+          continue;
+        }
+      }
+      // FD has the same CUDA prefernece than Result. Continue check
+      // constraints.
       std::optional<bool> MoreConstrainedThanPrevious =
           CheckMoreConstrained(FD, Result);
       if (!MoreConstrainedThanPrevious) {
@@ -12814,9 +12843,7 @@ Sema::resolveAddressOfSingleOverloadCandidate(Expr *E, DeclAccessPair &Pair) {
         continue;
       // FD is more constrained - replace Result with it.
     }
-    IsResultAmbiguous = false;
-    DAP = I.getPair();
-    Result = FD;
+    FoundBetter();
   }
 
   if (IsResultAmbiguous)
@@ -12826,9 +12853,15 @@ Sema::resolveAddressOfSingleOverloadCandidate(Expr *E, DeclAccessPair &Pair) {
     SmallVector<const Expr *, 1> ResultAC;
     // We skipped over some ambiguous declarations which might be ambiguous with
     // the selected result.
-    for (FunctionDecl *Skipped : AmbiguousDecls)
+    for (FunctionDecl *Skipped : AmbiguousDecls) {
+      // If skipped candidate has 
diff erent CUDA preference than the result,
+      // there is no ambiguity. Otherwise check whether they have 
diff erent
+      // constraints.
+      if (getLangOpts().CUDA && CheckCUDAPreference(Skipped, Result) != 0)
+        continue;
       if (!CheckMoreConstrained(Skipped, Result))
         return nullptr;
+    }
     Pair = DAP;
   }
   return Result;

diff  --git a/clang/test/SemaCUDA/template-arg-deduction.cu b/clang/test/SemaCUDA/template-arg-deduction.cu
new file mode 100644
index 00000000000000..22ff34fabdb08f
--- /dev/null
+++ b/clang/test/SemaCUDA/template-arg-deduction.cu
@@ -0,0 +1,27 @@
+// RUN: %clang_cc1 -triple x86_64-unknown-linux-gnu -fsyntax-only -verify %s
+// RUN: %clang_cc1 -triple nvptx64-nvidia-cuda -fsyntax-only -fcuda-is-device -verify %s
+
+// expected-no-diagnostics
+
+#include "Inputs/cuda.h"
+
+void foo();
+__device__ void foo();
+
+template<class F>
+void host_temp(F f);
+
+template<class F>
+__device__ void device_temp(F f);
+
+void host_caller() {
+  host_temp(foo);
+}
+
+__global__ void kernel_caller() {
+  device_temp(foo);
+}
+
+__device__ void device_caller() {
+  device_temp(foo);
+}