[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
This commit is contained in:
Yaxun (Sam) Liu
2023-07-02 00:40:52 -04:00
parent f263f45ba6
commit ea72a4e654
2 changed files with 64 additions and 4 deletions

View File

@@ -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 differennt 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 different 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 different CUDA preference than the result,
// there is no ambiguity. Otherwise check whether they have different
// constraints.
if (getLangOpts().CUDA && CheckCUDAPreference(Skipped, Result) != 0)
continue;
if (!CheckMoreConstrained(Skipped, Result))
return nullptr;
}
Pair = DAP;
}
return Result;

View File

@@ -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);
}