-
Notifications
You must be signed in to change notification settings - Fork 14.3k
[flang][cuda] Handle gpu.return in AbstractResult pass #119035
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Merged
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
@llvm/pr-subscribers-flang-fir-hlfir Author: Valentin Clement (バレンタイン クレメン) (clementval) ChangesProcess Full diff: https://github.com/llvm/llvm-project/pull/119035.diff 2 Files Affected:
diff --git a/flang/lib/Optimizer/Transforms/AbstractResult.cpp b/flang/lib/Optimizer/Transforms/AbstractResult.cpp
index 2ed66cc83eefb5..b0327cc10e9de6 100644
--- a/flang/lib/Optimizer/Transforms/AbstractResult.cpp
+++ b/flang/lib/Optimizer/Transforms/AbstractResult.cpp
@@ -234,6 +234,60 @@ class SaveResultOpConversion
}
};
+template <typename OpTy>
+static mlir::LogicalResult
+processReturnLikeOp(OpTy ret, mlir::Value newArg,
+ mlir::PatternRewriter &rewriter) {
+ auto loc = ret.getLoc();
+ rewriter.setInsertionPoint(ret);
+ mlir::Value resultValue = ret.getOperand(0);
+ fir::LoadOp resultLoad;
+ mlir::Value resultStorage;
+ // Identify result local storage.
+ if (auto load = resultValue.getDefiningOp<fir::LoadOp>()) {
+ resultLoad = load;
+ resultStorage = load.getMemref();
+ // The result alloca may be behind a fir.declare, if any.
+ if (auto declare = resultStorage.getDefiningOp<fir::DeclareOp>())
+ resultStorage = declare.getMemref();
+ }
+ // Replace old local storage with new storage argument, unless
+ // the derived type is C_PTR/C_FUN_PTR, in which case the return
+ // type is updated to return void* (no new argument is passed).
+ if (fir::isa_builtin_cptr_type(resultValue.getType())) {
+ auto module = ret->template getParentOfType<mlir::ModuleOp>();
+ FirOpBuilder builder(rewriter, module);
+ mlir::Value cptr = resultValue;
+ if (resultLoad) {
+ // Replace whole derived type load by component load.
+ cptr = resultLoad.getMemref();
+ rewriter.setInsertionPoint(resultLoad);
+ }
+ mlir::Value newResultValue =
+ fir::factory::genCPtrOrCFunptrValue(builder, loc, cptr);
+ newResultValue = builder.createConvert(
+ loc, getVoidPtrType(ret.getContext()), newResultValue);
+ rewriter.setInsertionPoint(ret);
+ rewriter.replaceOpWithNewOp<OpTy>(ret, mlir::ValueRange{newResultValue});
+ } else if (resultStorage) {
+ resultStorage.replaceAllUsesWith(newArg);
+ rewriter.replaceOpWithNewOp<OpTy>(ret);
+ } else {
+ // The result storage may have been optimized out by a memory to
+ // register pass, this is possible for fir.box results, or fir.record
+ // with no length parameters. Simply store the result in the result
+ // storage. at the return point.
+ rewriter.create<fir::StoreOp>(loc, resultValue, newArg);
+ rewriter.replaceOpWithNewOp<OpTy>(ret);
+ }
+ // Delete result old local storage if unused.
+ if (resultStorage)
+ if (auto alloc = resultStorage.getDefiningOp<fir::AllocaOp>())
+ if (alloc->use_empty())
+ rewriter.eraseOp(alloc);
+ return mlir::success();
+}
+
class ReturnOpConversion : public mlir::OpRewritePattern<mlir::func::ReturnOp> {
public:
using OpRewritePattern::OpRewritePattern;
@@ -242,55 +296,23 @@ class ReturnOpConversion : public mlir::OpRewritePattern<mlir::func::ReturnOp> {
llvm::LogicalResult
matchAndRewrite(mlir::func::ReturnOp ret,
mlir::PatternRewriter &rewriter) const override {
- auto loc = ret.getLoc();
- rewriter.setInsertionPoint(ret);
- mlir::Value resultValue = ret.getOperand(0);
- fir::LoadOp resultLoad;
- mlir::Value resultStorage;
- // Identify result local storage.
- if (auto load = resultValue.getDefiningOp<fir::LoadOp>()) {
- resultLoad = load;
- resultStorage = load.getMemref();
- // The result alloca may be behind a fir.declare, if any.
- if (auto declare = resultStorage.getDefiningOp<fir::DeclareOp>())
- resultStorage = declare.getMemref();
- }
- // Replace old local storage with new storage argument, unless
- // the derived type is C_PTR/C_FUN_PTR, in which case the return
- // type is updated to return void* (no new argument is passed).
- if (fir::isa_builtin_cptr_type(resultValue.getType())) {
- auto module = ret->getParentOfType<mlir::ModuleOp>();
- FirOpBuilder builder(rewriter, module);
- mlir::Value cptr = resultValue;
- if (resultLoad) {
- // Replace whole derived type load by component load.
- cptr = resultLoad.getMemref();
- rewriter.setInsertionPoint(resultLoad);
- }
- mlir::Value newResultValue =
- fir::factory::genCPtrOrCFunptrValue(builder, loc, cptr);
- newResultValue = builder.createConvert(
- loc, getVoidPtrType(ret.getContext()), newResultValue);
- rewriter.setInsertionPoint(ret);
- rewriter.replaceOpWithNewOp<mlir::func::ReturnOp>(
- ret, mlir::ValueRange{newResultValue});
- } else if (resultStorage) {
- resultStorage.replaceAllUsesWith(newArg);
- rewriter.replaceOpWithNewOp<mlir::func::ReturnOp>(ret);
- } else {
- // The result storage may have been optimized out by a memory to
- // register pass, this is possible for fir.box results, or fir.record
- // with no length parameters. Simply store the result in the result
- // storage. at the return point.
- rewriter.create<fir::StoreOp>(loc, resultValue, newArg);
- rewriter.replaceOpWithNewOp<mlir::func::ReturnOp>(ret);
- }
- // Delete result old local storage if unused.
- if (resultStorage)
- if (auto alloc = resultStorage.getDefiningOp<fir::AllocaOp>())
- if (alloc->use_empty())
- rewriter.eraseOp(alloc);
- return mlir::success();
+ return processReturnLikeOp(ret, newArg, rewriter);
+ }
+
+private:
+ mlir::Value newArg;
+};
+
+class GPUReturnOpConversion
+ : public mlir::OpRewritePattern<mlir::gpu::ReturnOp> {
+public:
+ using OpRewritePattern::OpRewritePattern;
+ GPUReturnOpConversion(mlir::MLIRContext *context, mlir::Value newArg)
+ : OpRewritePattern(context), newArg{newArg} {}
+ llvm::LogicalResult
+ matchAndRewrite(mlir::gpu::ReturnOp ret,
+ mlir::PatternRewriter &rewriter) const override {
+ return processReturnLikeOp(ret, newArg, rewriter);
}
private:
@@ -373,6 +395,9 @@ class AbstractResultOpt
patterns.insert<ReturnOpConversion>(context, newArg);
target.addDynamicallyLegalOp<mlir::func::ReturnOp>(
[](mlir::func::ReturnOp ret) { return ret.getOperands().empty(); });
+ patterns.insert<GPUReturnOpConversion>(context, newArg);
+ target.addDynamicallyLegalOp<mlir::gpu::ReturnOp>(
+ [](mlir::gpu::ReturnOp ret) { return ret.getOperands().empty(); });
assert(func.getFunctionType() ==
getNewFunctionType(funcTy, shouldBoxResult));
} else {
diff --git a/flang/test/Fir/CUDA/cuda-abstract-result.mlir b/flang/test/Fir/CUDA/cuda-abstract-result.mlir
new file mode 100644
index 00000000000000..8c59487ca5cd5c
--- /dev/null
+++ b/flang/test/Fir/CUDA/cuda-abstract-result.mlir
@@ -0,0 +1,37 @@
+// RUN: fir-opt -pass-pipeline='builtin.module(gpu.module(gpu.func(abstract-result)))' %s | FileCheck %s
+
+gpu.module @test {
+ gpu.func @_QMinterval_mPtest1(%arg0: !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, %arg1: !fir.ref<f32>) -> !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}> {
+ %c1_i32 = arith.constant 1 : i32
+ %18 = fir.dummy_scope : !fir.dscope
+ %19 = fir.declare %arg0 dummy_scope %18 {uniq_name = "_QMinterval_mFtest1Ea"} : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, !fir.dscope) -> !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>
+ %20 = fir.declare %arg1 dummy_scope %18 {uniq_name = "_QMinterval_mFtest1Eb"} : (!fir.ref<f32>, !fir.dscope) -> !fir.ref<f32>
+ %21 = fir.alloca !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}> {bindc_name = "c", uniq_name = "_QMinterval_mFtest1Ec"}
+ %22 = fir.declare %21 {uniq_name = "_QMinterval_mFtest1Ec"} : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>) -> !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>
+ %23 = fir.alloca i32 {bindc_name = "warpsize", uniq_name = "_QMcudadeviceECwarpsize"}
+ %24 = fir.declare %23 {uniq_name = "_QMcudadeviceECwarpsize"} : (!fir.ref<i32>) -> !fir.ref<i32>
+ %25 = fir.field_index inf, !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>
+ %26 = fir.coordinate_of %19, %25 : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, !fir.field) -> !fir.ref<f32>
+ %27 = fir.load %20 : !fir.ref<f32>
+ %28 = arith.negf %27 fastmath<contract> : f32
+ %29 = fir.load %26 : !fir.ref<f32>
+ %30 = fir.call @__fadd_rd(%29, %28) proc_attrs<bind_c> fastmath<contract> : (f32, f32) -> f32
+ %31 = fir.field_index inf, !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>
+ %32 = fir.coordinate_of %22, %31 : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, !fir.field) -> !fir.ref<f32>
+ fir.store %30 to %32 : !fir.ref<f32>
+ %33 = fir.field_index sup, !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>
+ %34 = fir.coordinate_of %19, %33 : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, !fir.field) -> !fir.ref<f32>
+ %35 = fir.load %20 : !fir.ref<f32>
+ %36 = arith.negf %35 fastmath<contract> : f32
+ %37 = fir.load %34 : !fir.ref<f32>
+ %38 = fir.call @__fadd_ru(%37, %36) proc_attrs<bind_c> fastmath<contract> : (f32, f32) -> f32
+ %39 = fir.field_index sup, !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>
+ %40 = fir.coordinate_of %22, %39 : (!fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, !fir.field) -> !fir.ref<f32>
+ fir.store %38 to %40 : !fir.ref<f32>
+ %41 = fir.load %22 : !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>
+ gpu.return %41 : !fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>
+ }
+}
+
+// CHECK: gpu.func @_QMinterval_mPtest1(%arg0: !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, %arg1: !fir.ref<!fir.type<_QMinterval_mTinterval{inf:f32,sup:f32}>>, %arg2: !fir.ref<f32>) {
+// CHECK: gpu.return{{$}}
|
wangzpgi
approved these changes
Dec 6, 2024
jeanPerier
approved these changes
Dec 9, 2024
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Labels
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Process
gpu.return
in AbstractResult pass when the function is agpu.func
.