Revert "[AMD] Add basics to allow bypass LDS for dot RHS (#5350)" (#5708)

Reverting, as I have to revert
[cec1db5](triton-lang/triton@cec1db5),
(which this change relies on) due to regression in internal tests.

Author: pawelszczerbuk

bin/RegisterTritonDialects.h

@@ -60,7 +60,6 @@ inline void registerTritonDialects(mlir::DialectRegistry &registry) {
   // TritonAMDGPUTransforms passes
   mlir::registerTritonAMDGPUAccelerateMatmul();
   mlir::registerTritonAMDGPUOptimizeEpilogue();
-  mlir::registerTritonAMDGPUBypassLDSForDotOperand();
   mlir::registerTritonAMDGPUReorderInstructions();
   mlir::registerTritonAMDGPUBlockPingpong();
   mlir::registerTritonAMDGPUStreamPipeline();

include/triton/Dialect/TritonGPU/Transforms/Utility.h

@@ -205,8 +205,6 @@ enum class MMALoadType {
 };
 MMALoadType getMMALoadType(Operation *loadOp);
 
-// Convert \param op operands and results to layout \param encoding.
-void convertOpEncoding(Attribute encoding, Operation *op);
 } // namespace mlir
 
 #endif // TRITON_DIALECT_TRITONGPU_TRANSFORMS_UTILITY_H_

include/triton/Tools/Sys/GetEnv.hpp

@@ -31,7 +31,6 @@ inline const std::set<std::string> CACHE_INVALIDATING_ENV_VARS = {
     "TRITON_ENABLE_LLVM_DEBUG",
     "TRITON_HIP_STREAM_PREFETCH",
     "TRITON_HIP_USE_BLOCK_PINGPONG",
-    "TRITON_HIP_BYPASS_LDS_FOR_DOT",
     "TRITON_LLVM_DEBUG_ONLY",
     "TRITON_ENABLE_ASAN",
     "TRITON_OVERRIDE_ARCH",

lib/Dialect/TritonGPU/Transforms/Coalesce.cpp

@@ -104,6 +104,55 @@ struct CoalescePass : public impl::TritonGPUCoalesceBase<CoalescePass> {
         threadsPerWarp, CTALayout);
   }
 
+  static Type getNewType(Type type, Attribute encoding) {
+    RankedTensorType tensorType = cast<RankedTensorType>(type);
+    return RankedTensorType::get(tensorType.getShape(),
+                                 tensorType.getElementType(), encoding);
+  }
+
+  void coalesceOp(Attribute encoding, Operation *op) {
+    OpBuilder builder(op);
+    // Convert operands
+    // For load/store with tensor pointers, we don't have to change the
+    // operands' type, we do this by changing the outputs' type of
+    // `make_tensor_ptr`
+    SmallVector<Value, 4> newArgs;
+    for (auto operand : op->getOperands()) {
+      auto tensorType = dyn_cast<RankedTensorType>(operand.getType());
+      if (tensorType &&
+          !isa<triton::gpu::SharedEncodingAttr>(tensorType.getEncoding())) {
+        Type newType = getNewType(tensorType, encoding);
+        newArgs.push_back(builder.create<triton::gpu::ConvertLayoutOp>(
+            op->getLoc(), newType, operand));
+      } else {
+        newArgs.push_back(operand);
+      }
+    }
+
+    // Convert output types
+    SmallVector<Type, 4> newTypes;
+    for (auto t : op->getResultTypes()) {
+      bool isAsync = isa<triton::gpu::AsyncCopyGlobalToLocalOp>(op);
+      newTypes.push_back(isAsync ? t : getNewType(t, encoding));
+    }
+
+    // Construct new op with the new encoding
+    Operation *newOp =
+        builder.create(op->getLoc(), op->getName().getIdentifier(), newArgs,
+                       newTypes, op->getAttrs());
+
+    // Cast the results back to the original layout
+    for (size_t i = 0; i < op->getNumResults(); i++) {
+      Value newResult = newOp->getResult(i);
+      if (newTypes[i] != op->getResultTypes()[i]) {
+        newResult = builder.create<triton::gpu::ConvertLayoutOp>(
+            op->getLoc(), op->getResult(i).getType(), newResult);
+      }
+      op->getResult(i).replaceAllUsesWith(newResult);
+    }
+    op->erase();
+  }
+
   void runOnOperation() override {
     // Run axis info analysis
     ModuleOp moduleOp = getOperation();
@@ -138,7 +187,7 @@ struct CoalescePass : public impl::TritonGPUCoalesceBase<CoalescePass> {
     // 4. Convert the output of this new memory op back to L1
     // 5. Replace all the uses of the original memory op by the new one
     for (auto &kv : layoutMap) {
-      convertOpEncoding(kv.second, kv.first);
+      coalesceOp(kv.second, kv.first);
     }
   }
 };

lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp

@@ -1022,43 +1022,6 @@ void LayoutRematerialization::hoistConvertOnTopOfExtOrBroadcast() {
   }
 }
 
-bool shouldPropagateConversion(ConvertLayoutOp convertOp) {
-  RankedTensorType targetType = convertOp.getType();
-  auto dotEnc = dyn_cast<DotOperandEncodingAttr>(targetType.getEncoding());
-  // If the target encoding is not DotOperandEncodingAttr, allow propagation.
-  if (!dotEnc) {
-    return true;
-  }
-  // Skip conversions to DotOperandEncodingAttr when the operand index is 0.
-  // This heuristic is applied to prevent moving the blocked->dot conversion of
-  // the Q tensor (a loop invariant in Flash Attention) outside the loop. Doing
-  // so can increase register pressure and cause spilling in some cases.
-  if (dotEnc.getOpIdx() == 0) {
-    return false;
-  }
-  // Skip conversions to DotOperandEncodingAttr when the operand index is 1 if
-  // it's not intentionally placed above a load as we have to be a bit more
-  // careful with the heuristics for both correctness and performance.
-  // TODO: Fix this logic to avoid propagating conversions backward unless
-  // it reduces the total number of conversions.
-  assert(dotEnc.getOpIdx() == 1);
-  SetVector<Operation *> slice;
-  BackwardSliceOptions opt;
-  opt.omitBlockArguments = true;
-  opt.filter = [&](Operation *op) {
-    return op->getParentRegion() == convertOp->getParentRegion();
-  };
-  getBackwardSlice(convertOp.getOperation(), &slice, opt);
-
-  for (Operation *currOp : slice) {
-    if (isa<LoadOp>(currOp)) {
-      return false;
-    }
-  }
-  // Allow propagation if no LoadOp is found.
-  return true;
-}
-
 void LayoutRematerialization::hoistConvertIntoConditionals() {
   // Go through each ConvertLayoutOp.
   SmallVector<ConvertLayoutOp> convertOps;
@@ -1077,11 +1040,11 @@ void LayoutRematerialization::hoistConvertIntoConditionals() {
 
 void LayoutRematerialization::backwardRematerialization(
     ConvertLayoutOp convertOp) {
+  // we don't handle conversions to DotOperandEncodingAttr
+  // this is a heuristic to accommodate fused attention
   RankedTensorType targetType = convertOp.getType();
-  if (!shouldPropagateConversion(convertOp)) {
+  if (isa<DotOperandEncodingAttr>(targetType.getEncoding()))
     return;
-  }
-
   Value oldV = convertOp.getSrc();
   LDBG("check backward remat with source " << oldV << " encoding "
                                            << targetType.getEncoding());
@@ -1120,10 +1083,11 @@ void LayoutRematerialization::backwardRematerialization(
 // of the convert.
 void LayoutRematerialization::hoistConvertOnTopOfExtOrBroadcast(
     ConvertLayoutOp convertOp) {
+  // we don't handle conversions to DotOperandEncodingAttr
+  // this is a heuristics to accommodate fused attention
   RankedTensorType targetType = convertOp.getType();
-  if (!shouldPropagateConversion(convertOp)) {
+  if (isa<DotOperandEncodingAttr>(targetType.getEncoding()))
     return;
-  }
 
   auto isExtOrBroadcastOp = [](Operation *op) {
     if (isa<arith::ExtSIOp, arith::ExtUIOp, arith::ExtFOp, BroadcastOp,

lib/Dialect/TritonGPU/Transforms/Utility.cpp

@@ -1057,54 +1057,6 @@ MMALoadType getMMALoadType(Operation *loadOp) {
   }
 }
 
-static Type getNewType(Type type, Attribute encoding) {
-  RankedTensorType tensorType = cast<RankedTensorType>(type);
-  return RankedTensorType::get(tensorType.getShape(),
-                               tensorType.getElementType(), encoding);
-}
-
-void convertOpEncoding(Attribute encoding, Operation *op) {
-  OpBuilder builder(op);
-  // Convert operands
-  // For load/store with tensor pointers, we don't have to change the
-  // operands' type, we do this by changing the outputs' type of
-  // `make_tensor_ptr`
-  SmallVector<Value, 4> newArgs;
-  for (auto operand : op->getOperands()) {
-    auto tensorType = dyn_cast<RankedTensorType>(operand.getType());
-    if (tensorType &&
-        !isa<triton::gpu::SharedEncodingAttr>(tensorType.getEncoding())) {
-      Type newType = getNewType(tensorType, encoding);
-      newArgs.push_back(builder.create<triton::gpu::ConvertLayoutOp>(
-          op->getLoc(), newType, operand));
-    } else {
-      newArgs.push_back(operand);
-    }
-  }
-
-  // Convert output types
-  SmallVector<Type, 4> newTypes;
-  for (auto t : op->getResultTypes()) {
-    bool isAsync = isa<triton::gpu::AsyncCopyGlobalToLocalOp>(op);
-    newTypes.push_back(isAsync ? t : getNewType(t, encoding));
-  }
-
-  // Construct new op with the new encoding
-  Operation *newOp = builder.create(op->getLoc(), op->getName().getIdentifier(),
-                                    newArgs, newTypes, op->getAttrs());
-
-  // Cast the results back to the original layout
-  for (size_t i = 0; i < op->getNumResults(); i++) {
-    Value newResult = newOp->getResult(i);
-    if (newTypes[i] != op->getResultTypes()[i]) {
-      newResult = builder.create<triton::gpu::ConvertLayoutOp>(
-          op->getLoc(), op->getResult(i).getType(), newResult);
-    }
-    op->getResult(i).replaceAllUsesWith(newResult);
-  }
-  op->erase();
-}
-
 namespace {
 
 /// Detect dead arguments in scf.for op by assuming all the values are dead and