Reuse input buffer in lowering to krnl (#2939)

* first step Signed-off-by: chentong319 <[email protected]> * cpu Signed-off-by: chentong319 <[email protected]> * options Signed-off-by: chentong319 <[email protected]> * unify Signed-off-by: chentong319 <[email protected]> * simd Signed-off-by: chentong319 <[email protected]> * comments Signed-off-by: chentong319 <[email protected]> * lit test Signed-off-by: chentong319 <[email protected]> * fix test Signed-off-by: chentong319 <[email protected]> * format Signed-off-by: chentong319 <[email protected]> * response Signed-off-by: chentong319 <[email protected]> --------- Signed-off-by: chentong319 <[email protected]>
onnx · Sep 13, 2024 · 97d497f · 97d497f
1 parent 02f45b0
commit 97d497f
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Showing 4 changed files with 109 additions and 9 deletions.
diff --git a/src/Compiler/CompilerOptions.cpp b/src/Compiler/CompilerOptions.cpp
@@ -42,6 +42,7 @@ bool enableONNXHybridPass;                             // common for both
 std::vector<std::string> functionsToDecompose;         // common for both
 std::string opsForCall;                                // common for both
 bool disableKrnlOpFusion;                              // common for both
+bool enableKrnlBufferReuse;                            // common for both
 bool disableMemRefPrefetch;                            // common for both
 EmissionTargetType emissionTarget;                     // onnx-mlir only
 bool invokeOnnxVersionConverter;                       // onnx-mlir only
@@ -212,6 +213,14 @@ static llvm::cl::opt<bool, true> disableKrnlOpFusionOpt(
     llvm::cl::location(disableKrnlOpFusion), llvm::cl::init(false),
     llvm::cl::cat(OnnxMlirCommonOptions));
 
+static llvm::cl::opt<bool, true> enableKrnlBufferReuseOpt(
+    "enable-krnl-buffer-reuse",
+    llvm::cl::desc("enable buffer reuse within an op in onnx-to-krnl pass"
+                   "(default=false)\n"
+                   "Set to 'true' if you want to enable buffer reuse."),
+    llvm::cl::location(enableKrnlBufferReuse), llvm::cl::init(false),
+    llvm::cl::cat(OnnxMlirCommonOptions));
+
 static llvm::cl::opt<bool, true> disableMemRefPrefetchOpt(
     "disable-memref-prefetch",
     llvm::cl::desc("disable generation of memref.prefetch (default=false)\n"

diff --git a/src/Compiler/CompilerOptions.hpp b/src/Compiler/CompilerOptions.hpp
@@ -87,6 +87,7 @@ extern bool enableONNXHybridPass;                             // common for both
 extern std::vector<std::string> functionsToDecompose;         // common for both
 extern std::string opsForCall;                                // common for both
 extern bool disableKrnlOpFusion;                              // common for both
+extern bool enableKrnlBufferReuse;                            // common for both
 extern bool disableMemRefPrefetch;                            // common for both
 extern EmissionTargetType emissionTarget;                     // onnx-mlir only
 extern bool invokeOnnxVersionConverter;                       // onnx-mlir only

diff --git a/src/Conversion/ONNXToKrnl/Math/Elementwise.cpp b/src/Conversion/ONNXToKrnl/Math/Elementwise.cpp
@@ -28,6 +28,82 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
+// Check the input, x, can be reused as the output buffer
+bool isBufferReusable(Value x, MemRefType outputType) {
+  if (!x.hasOneUse())
+    return false;
+
+  Type xType = x.getType();
+  auto inputType = dyn_cast<ShapedType>(xType);
+  if (!inputType)
+    return false;
+  // Currently, only static shape could be reused.
+  // ToFix: use DimAnalysis to handle dynamic shape.
+  if (!hasStaticShape(inputType))
+    return false;
+  if (!hasStaticShape(outputType))
+    return false;
+
+  // Currently reuse requires that the shape has to be the same.
+  // ToFix: If the shape is not the same, memref.cast can be used.
+  if (getRank(inputType) != getRank(outputType))
+    return false;
+  for (int64_t i = 0; i < getRank(inputType); i++) {
+    if (inputType.getShape()[i] != outputType.getShape()[i])
+      return false;
+  }
+
+  // ToFix: The simd padding is not checked
+  // We did not record whether the memref is padded or not.
+  // The padding added to the memref the as an attribute, or not needed.
+  return true;
+}
+
+// Traverse the operands to find the candidate for buffer reuse.
+// Return -1, if no candidate is found.
+int whichBufferToReuse(ValueRange values, MemRefType outputType) {
+  for (size_t i = 0; i < values.size(); i++) {
+    if (isBufferReusable(values[i], outputType))
+      return i;
+  }
+  return -1;
+}
+
+// Allocate memref (as before) if no input buffer can be reused.
+// Default VL=0 is used for non SIMD allocation
+Value allocOrReuse(MemRefBuilder &create, Operation *op,
+    ValueRange generatedOperands, MemRefType outputMemRefType, DimsExprRef dims,
+    int64_t alignment, int64_t VL = 0);
+
+Value allocOrReuse(MemRefBuilder &create, Operation *op,
+    ValueRange generatedOperands, MemRefType outputMemRefType, DimsExprRef dims,
+    int64_t alignment, int64_t VL) {
+
+  int indexToReuse = -1;
+  // By default, enableKrnlBufferReuse is false. Simply allocate a memref.
+  if (enableKrnlBufferReuse) {
+    // Be aware to use the op->getOperands() to check the number of uses.
+    // After buffer reuse, the number of uses of the transformed Value,
+    // generatedOperands, will increase.
+    indexToReuse = whichBufferToReuse(op->getOperands(), outputMemRefType);
+  }
+
+  if (indexToReuse != -1) {
+    int size = getSizeInBytes(outputMemRefType);
+    LLVM_DEBUG({
+      llvm::dbgs() << "  malloc_size " << size << "\n";
+      op->dump();
+    });
+    return generatedOperands[indexToReuse];
+  } else {
+    if (VL == 0)
+      return create.alignedAlloc(outputMemRefType, dims, alignment);
+    else
+      return create.alignedAllocWithSimdPadding(
+          outputMemRefType, dims, VL, alignment);
+  }
+}
+
 // =============================================================================
 
 /// Emit post-processing for variadic element-wise ops.
@@ -1323,14 +1399,14 @@ static LogicalResult getPartiallyFlattenedSimdCode(
   IndexExprScope allocScope(create.vec, shapeHelper->getScope());
   DimsExpr outputDims;
   getIndexExprList<SymbolIndexExpr>(shapeHelper->getOutputDims(), outputDims);
-  // Alloc memory with padding for SIMD.
+  // Reuse the buffer from the input, or Alloc memory with padding for SIMD.
   // For the moment, its ok to go here; if we truly have partial flattening of
   // the simd code, then we only do it with static memref size that are
   // multiples of VL * unrollVL, so there should be no padding anyway. This
   // will change if we do partial flattening with non-multiple of VL *
   // unrollVL.
-  Value alloc = create.mem.alignedAllocWithSimdPadding(
-      outputMemRefType, outputDims, VL, alignment);
+  Value alloc = allocOrReuse(
+      create.mem, op, operands, outputMemRefType, outputDims, alignment, VL);
   // Create flat inputs in the last innerDinNum dims.
   llvm::SmallVector<Value, 4> flatOperands;
   for (Value oper : operands) {
@@ -1975,8 +2051,9 @@ struct ONNXElementwiseUnaryOpLowering
     outputMemRefType = opFusionHelper.getOutputType(outputMemRefType);
 
     // Insert an allocation for the result of this operation.
-    Value alloc = create.mem.alignedAlloc(
-        outputMemRefType, shapeHelper.getOutputDims(), alignment);
+    Value alloc = allocOrReuse(create.mem, op, operands, outputMemRefType,
+        shapeHelper.getOutputDims(), alignment);
+    ;
 
     // Only create krnl.iterate if one of the operands is not scalar tensor.
     if (!isScalar) {
@@ -2156,8 +2233,9 @@ struct ONNXElementwiseBinaryOpLowering
     outputMemRefType = opFusionHelper.getOutputType(outputMemRefType);
 
     // Insert an allocation and deallocation for the result of this operation.
-    Value alloc = create.mem.alignedAlloc(
-        outputMemRefType, shapeHelper.getOutputDims(), alignment);
+    Value alloc = allocOrReuse(create.mem, op, operands, outputMemRefType,
+        shapeHelper.getOutputDims(), alignment);
+    ;
 
     // Only create krnl.iterate if one of the operands is not scalar tensor.
     if (!isScalar) {
@@ -2331,8 +2409,9 @@ struct ONNXElementwiseVariadicOpLowering
     outputMemRefType = opFusionHelper.getOutputType(outputMemRefType);
 
     // Insert an allocation and deallocation for the result of this operation.
-    Value alloc = create.mem.alignedAlloc(
-        outputMemRefType, shapeHelper.getOutputDims(), alignment);
+    Value alloc = allocOrReuse(create.mem, op, operands, outputMemRefType,
+        shapeHelper.getOutputDims(), alignment);
+    ;
 
     // Only create krnl.iterate if one of the operands is not scalar tensor.
     if (!isScalar) {

diff --git a/test/mlir/conversion/onnx_to_krnl/onnx_lowering_reuse.mlir b/test/mlir/conversion/onnx_to_krnl/onnx_lowering_reuse.mlir
@@ -0,0 +1,11 @@
+// RUN: onnx-mlir-opt --disable-krnl-op-fusion=true --enable-krnl-buffer-reuse=true --shape-inference --convert-onnx-to-krnl --canonicalize %s -split-input-file | FileCheck %s
+
+// -----
+func.func @test_reuse(%arg0: tensor<1024xf32>, %arg1: tensor<1024xf32>) -> tensor<1024xf32> {
+    %0 = "onnx.Add"(%arg0, %arg1) : (tensor<1024xf32>, tensor<1024xf32>) -> tensor<1024xf32>
+    %1 = "onnx.Sqrt"(%0) : (tensor<1024xf32>) -> tensor<1024xf32>
+    %2 = "onnx.Sqrt"(%1) : (tensor<1024xf32>) -> tensor<1024xf32>
+    return %2 : tensor<1024xf32>
+}
+// CHECK-LABEL: func.func @test_reuse
+// CHECK-NOT: memref.alloc