[mlir] Plumb missing paramter to gpu transform op

[ThomasRaoux]

Summary

rewriteMapNestedForeachThreadToGpuThreads was dropping the parameter to
skip inserting barrier

[guraypp]

It makes a lot of sense that this is optional. Thanks for adding this.

mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td

let description = [{
         }
         gpu.barrier
         gpu.terminator
       }      
       ```
     }];
 
   let arguments = (ins PDL_Operation:$target,
-                   DefaultValuedAttr<I64ArrayAttr, "{}">:$blockDim);
+                   DefaultValuedAttr<I64ArrayAttr, "{}">:$blockDim,
+                   DefaultValuedAttr<BoolAttr, "true">:$syncAfterDistribute);
   let results = (outs PDL_Operation:$result);
 
   let assemblyFormat = "$target attr-dict";
   let extraClassDeclaration = [{
     ::mlir::DiagnosedSilenceableFailure applyToOne(
         ::mlir::Operation *target, 
         ::llvm::SmallVectorImpl<::mlir::Operation *> &results, 
         ::mlir::transform::TransformState &state);

mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp

static FailureOr<SmallVector<OpFoldResult>> rewriteOneForeachThreadToGpuThreads(
   return *potentialBlockDim;
 }
 
 mlir::WalkResult mlir::linalg::rewriteMapNestedForeachThreadToGpuThreads(
     RewriterBase &rewriter, Operation *target,
     const SmallVector<int64_t> &blockDim, bool syncAfterDistribute) {
   auto walkResult = target->walk([&](scf::ForeachThreadOp foreachThreadOp) {
     rewriter.setInsertionPoint(foreachThreadOp);
-    if (failed(rewriteOneForeachThreadToGpuThreads(rewriter, foreachThreadOp,
-                                                   blockDim, true)))
+    if (failed(rewriteOneForeachThreadToGpuThreads(
+            rewriter, foreachThreadOp, blockDim, syncAfterDistribute)))
       return WalkResult::interrupt();
     return WalkResult::advance();
   });
   return walkResult;
 }
 
 static LogicalResult
 checkGpuLimits(Optional<int64_t> gridDimX, Optional<int64_t> gridDimY,
if (!gpuLaunch) {
     return DiagnosedSilenceableFailure::definiteFailure();
   }
 
   SmallVector<int64_t> blockDim = extractFromI64ArrayAttr(getBlockDim());
   blockDim.resize(/*size=*/3, /*value=*/1);
   SimpleRewriter rewriter(getContext());
   rewriter.setInsertionPoint(target);
   auto walkResult = mlir::linalg::rewriteMapNestedForeachThreadToGpuThreads(
-      rewriter, target, blockDim, true);
+      rewriter, target, blockDim, getSyncAfterDistribute());
   if (walkResult.wasInterrupted())
     return DiagnosedSilenceableFailure(reportUnknownTransformError(target));
 
   LogicalResult result =
       alterGpuLaunch(rewriter, gpuLaunch, llvm::None, llvm::None, llvm::None,
                      blockDim[0], blockDim[1], blockDim[2]);
   if (failed(result))
     return DiagnosedSilenceableFailure::definiteFailure();

mlir/test/Dialect/Linalg/transform-gpu.mlir

 ^bb0(%arg0: !pdl.operation):
   transform.sequence %arg0 failures(propagate) {
   ^bb1(%arg1: !pdl.operation):
     %funcop = transform.structured.match ops{["func.func"]} in %arg0
     %gpuLaunch = transform.structured.map_nested_foreach_thread_to_gpu_blocks %funcop { generate_gpu_launch }
     transform.structured.map_nested_foreach_thread_to_gpu_threads %gpuLaunch { blockDim = [32, 4, 1] }
   }
 }
+
+// -----
+
+!type = memref<2 x 32 x f32>
+!type1d = memref<32 x f32>
+
+// CHECK-LABEL: func.func @saxpy2d_no_barrier(
+func.func @saxpy2d_no_barrier(%x: !type, %y: !type, %t: !type1d, %alpha : f32, %stream : !gpu.async.token) -> !type {
+  %one = arith.constant 1 : index
+  %c12 = arith.constant 12 : index
+  %c9 = arith.constant 9 : index
+  %c7 = arith.constant 7 : index
+//  CHECK-NOT:   gpu.barrier
+//      CHECK:   return
+  %name = gpu.launch async[%stream] blocks(%arg3, %arg4, %arg5) in (%arg9 = %one, %arg10 = %one, %arg11 = %one)
+            threads(%arg6, %arg7, %arg8) in (%arg12 = %one, %arg13 = %one, %arg14 = %one)
+  {
+    scf.foreach_thread (%i, %j) in (%c7, %c9) {
+        %4 = memref.load %x[%i, %j] : !type
+        %5 = memref.load %y[%i, %j] : !type
+        %6 = math.fma %alpha, %4, %5 : f32
+        memref.store %6, %y[%i, %j] : !type
+     }  {thread_dim_mapping = [1, 0, 2]}
+    gpu.terminator
+  }
+  return %y : !type
+}
+
+transform.with_pdl_patterns {
+^bb0(%arg0: !pdl.operation):
+  transform.sequence %arg0 failures(propagate) {
+  ^bb1(%arg1: !pdl.operation):
+    %funcop = transform.structured.match ops{["gpu.launch"]} in %arg0
+    transform.structured.map_nested_foreach_thread_to_gpu_threads %funcop { blockDim = [12, 9, 1], syncAfterDistribute = false }
+  }
+}