Add conversion for llvm.alloca, llvm.mul, llvm.sext, llvm.zext in LlvmToNeuraPass

include/NeuraDialect/NeuraOps.td

@@ -81,8 +81,8 @@ def Neura_FMulOp : Op<NeuraDialect, "fmul"> {
 
 def Neura_FDivOp : Op<NeuraDialect, "fdiv"> {
   let summary = "Floating division operation";
-  let arguments = (ins AnyFloat:$lhs, AnyFloat:$rhs, Optional<AnyType>:$predicate);
-  let results = (outs AnyFloat:$result);
+  let arguments = (ins AnyType:$lhs, AnyType:$rhs, Optional<AnyType>:$predicate);
+  let results = (outs AnyType:$result);
   // let assemblyFormat = "$lhs `,` $rhs `,` $predicate attr-dict `:` type($result)";
 }
 
@@ -211,6 +211,54 @@ def Neura_CastOp : Op<NeuraDialect, "cast">{
   // let assemblyFormat = "$input type($input) `->` type($output) `,` $predicate attr-dict";
 }
 
+// Defines an alloca operation for memory allocation.
+def Neura_AllocaOp : Op<NeuraDialect, "alloca"> {
+  let summary = "Memory allocation operation";
+  let description = [{
+    Allocates memory on the stack, similar to llvm.alloca.
+    Takes a predicated size value and returns a pointer to the allocated memory.
+
+    Example:
+      %ptr = neura.alloca %size : !neura.data<i32, i1> -> !llvm.ptr
+  }];
+
+  let arguments = (ins AnyType:$size);
+  let results = (outs AnyType:$result);
+  let assemblyFormat = "$size attr-dict `:` type($size) `->` type($result)";
+}
+
+// Defines a sign extension operation.
+def Neura_SExtOp : Op<NeuraDialect, "sext"> {
+  let summary = "Sign extension operation";
+  let description = [{
+    Sign extends a value from a smaller integer type to a larger integer type.
+    Similar to llvm.sext, but works with predicated values.
+
+    Example:
+      %extended = neura.sext %value : !neura.data<i8, i1> -> !neura.data<i32, i1>
+  }];
+
+  let arguments = (ins AnyType:$value);
+  let results = (outs AnyType:$result);
+  let assemblyFormat = "$value attr-dict `:` type($value) `->` type($result)";
+}
+
+// Defines a zero extension operation.
+def Neura_ZExtOp : Op<NeuraDialect, "zext"> {
+  let summary = "Zero extension operation";
+  let description = [{
+    Zero extends a value from a smaller integer type to a larger integer type.
+    Similar to llvm.zext, but works with predicated values.
+
+    Example:
+      %extended = neura.zext %value : !neura.data<i8, i1> -> !neura.data<i32, i1>
+  }];
+
+  let arguments = (ins AnyType:$value);
+  let results = (outs AnyType:$result);
+  let assemblyFormat = "$value attr-dict `:` type($value) `->` type($result)";
+}
+
 // ----------------------------------------------------
 // Defines vector operations.
 

lib/Conversion/LlvmToNeura/LlvmToNeuraPass.cpp

@@ -304,6 +304,63 @@ struct LlvmConstantToNeuraConstant : public OpRewritePattern<LLVM::ConstantOp> {
   }
 };
 
+struct LlvmAllocaToNeuraAlloca : public OpRewritePattern<LLVM::AllocaOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(LLVM::AllocaOp op,
+                                PatternRewriter &rewriter) const override {
+    Value size = op.getArraySize();
+    Type resultType = op.getType();
+
+    // Convert the size to neura.data<i32, i1> if it's not already
+    // For simplicity, we'll assume the size is already in the right format
+    // In practice, you might need to handle type conversion here
+
+    rewriter.replaceOpWithNewOp<neura::AllocaOp>(op, resultType, size);
+    return success();
+  }
+};
+
+struct LlvmSExtToNeuraSExt : public OpRewritePattern<LLVM::SExtOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(LLVM::SExtOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.getArg();
+    Type resultType = op.getType();
+
+    rewriter.replaceOpWithNewOp<neura::SExtOp>(op, resultType, input);
+    return success();
+  }
+};
+
+struct LlvmZExtToNeuraZExt : public OpRewritePattern<LLVM::ZExtOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(LLVM::ZExtOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.getArg();
+    Type resultType = op.getType();
+
+    rewriter.replaceOpWithNewOp<neura::ZExtOp>(op, resultType, input);
+    return success();
+  }
+};
+
+struct LlvmMulToNeuraMul : public OpRewritePattern<LLVM::MulOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(LLVM::MulOp op,
+                                PatternRewriter &rewriter) const override {
+    Value lhs = op.getLhs();
+    Value rhs = op.getRhs();
+    Type resultType = op.getType();
+
+    rewriter.replaceOpWithNewOp<neura::MulOp>(op, resultType, lhs, rhs);
+    return success();
+  }
+};
+
 struct LowerLlvmToNeuraPass
     : public PassWrapper<LowerLlvmToNeuraPass, OperationPass<ModuleOp>> {
 
@@ -338,6 +395,10 @@ struct LowerLlvmToNeuraPass
     patterns.add<LlvmReturnToNeuraReturn>(&getContext());
     patterns.add<FuncReturnToNeuraReturn>(&getContext());
     patterns.add<LlvmFSubToNeuraFSub>(&getContext());
+    patterns.add<LlvmAllocaToNeuraAlloca>(&getContext());
+    patterns.add<LlvmSExtToNeuraSExt>(&getContext());
+    patterns.add<LlvmZExtToNeuraZExt>(&getContext());
+    patterns.add<LlvmMulToNeuraMul>(&getContext());
 
     FrozenRewritePatternSet frozen(std::move(patterns));
 

test/c2llvm2mlir/kernel2.cpp

@@ -0,0 +1,46 @@
+// RUN: mlir-neura-opt %s | FileCheck %s
+
+#include <stdio.h>
+
+#define NTAPS 32
+
+int input[NTAPS] = {
+1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1,
+1, 1, 1, 1, 1, 1, 1, 1
+};
+int output[NTAPS];
+int coefficients[NTAPS] = {25, 150, 375, -225, 50, 75, -300, 125,
+25, 150, 375, -225, 50, 75, -300, 125,
+25, 150, 375, -225, 50, 75, -300, 125,
+25, 150, 375, -225, 50, 75, -300, 125};
+
+void kernel(int input[], int output[], int coefficient[]);
+
+int main()
+{
+
+//  input_dsp (input, NTAPS, 0);
+
+  kernel(input, output, coefficients);
+
+//  output_dsp (input, NTAPS, 0);
+//  output_dsp (coefficients, NTAPS, 0);
+//  output_dsp (output, NTAPS, 0);
+  printf("output: %d\n", output[0]);
+  return 0;
+}
+
+/*   input :           input sample array */
+/*   output:           output sample array */
+/*   coefficient:      coefficient array */
+void kernel(int input[], int output[], int coefficient[]) {
+  int i, j;
+
+   for (i = 0; i < NTAPS; ++i) {
+        for (j = 0; j < NTAPS; ++j) {
+            output[j] += input[i] * coefficient[i];
+        }
+    }
+}

test/c2llvm2mlir/test2.mlir

@@ -0,0 +1,16 @@
+// RUN: clang++ -S -emit-llvm kernel2.cpp -o kernel2.ll
+// RUN: mlir-translate --import-llvm kernel2.ll -o kernel2.mlir
+// RUN: mlir-neura-opt --assign-accelerator \
+// RUN:   --lower-llvm-to-neura \
+// RUN:   --canonicalize-live-in \
+// RUN:   --leverage-predicated-value \
+// RUN:   --transform-ctrl-to-data-flow \
+// RUN:   --fold-constant \
+// RUN:   --insert-data-mov \
+// RUN:   --map-to-accelerator="mapping-strategy=heuristic backtrack-config=simple" \
+// RUN:   --generate-code kernel2.mlir | FileCheck %s --check-prefix=CHECK-LLVM2NEURA
+
+// CHECK-LLVM2NEURA: %25 = neura.alloca %24 : !neura.data<i32, i1> -> !neura.data<!llvm.ptr, i1>
+// CHECK-LLVM2NEURA: %38 = "neura.phi"(%36, %37) : (!neura.data<i32, i1>, !neura.data<i32, i1>) -> !neura.data<i32, i1>
+// CHECK-LLVM2NEURA: %175 = neura.sext %174 : !neura.data<i32, i1> -> !neura.data<i64, i1>
+// CHECK-LLVM2NEURA: %194 = "neura.mul"(%192, %193) : (!neura.data<i32, i1>, !neura.data<i32, i1>) -> !neura.data<i32, i1>