[mlir][tosa] Add tosa.clamp as no-op canonicalization

[not-jenni]

Summary

When the min/max are the total range of the value, it is a no-op as the values are already restricted to that range.

mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

let arguments = (ins
     I64Attr:$max_int,
     F32Attr:$min_fp,
     F32Attr:$max_fp
   );
 
   let results = (outs
     Tosa_Tensor:$output
   );
+
+  let hasCanonicalizer = 1;
 }
 
 //===----------------------------------------------------------------------===//
 // Operator: reluN
 //===----------------------------------------------------------------------===//
 def Tosa_ReluNOp : Tosa_Op<"reluN", [
     DeclareOpInterfaceMethods<InferShapedTypeOpInterface,
                               ["inferReturnTypeComponents"]>,

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

struct MaxPool2dIsNoOp : public OpRewritePattern<tosa::MaxPool2dOp> {
   }
 };
 
 void MaxPool2dOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
                                               MLIRContext *context) {
   results.insert<MaxPool2dIsNoOp>(context);
 }
 
+struct ClampIsNoOp : public OpRewritePattern<tosa::ClampOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::ClampOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input();
+    auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
+    auto inputElementType = inputType.getElementType();
+
+    if (!inputType.hasStaticShape()) {
+      return failure();
+    }
+
+    if (inputElementType.isF32()) {
+      auto minClamp = op.min_fp();
+      auto maxClamp = op.max_fp();
+      bool isMin = (minClamp.isLargest() || minClamp.isInfinity()) &&
+                   minClamp.isNegative();
+      bool isMax = (maxClamp.isLargest() || maxClamp.isInfinity()) &&
+                   !maxClamp.isNegative();
+
+      if (isMin && isMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    if (inputElementType.isUnsignedInteger()) {
+      auto minClamp = op.min_int();
+      auto maxClamp = op.max_int();
+
+      int64_t intMin =
+          APInt::getMinValue(inputElementType.getIntOrFloatBitWidth())
+              .getZExtValue();
+      int64_t intMax =
+          APInt::getMaxValue(inputElementType.getIntOrFloatBitWidth())
+              .getZExtValue();
+
+      if (minClamp <= intMin && maxClamp >= intMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    if (inputElementType.isa<IntegerType>()) {
+      auto minClamp = op.min_int();
+      auto maxClamp = op.max_int();
+
+      int64_t intMin =
+          APInt::getSignedMinValue(inputElementType.getIntOrFloatBitWidth())
+              .getSExtValue();
+      int64_t intMax =
+          APInt::getSignedMaxValue(inputElementType.getIntOrFloatBitWidth())
+              .getSExtValue();
+
+      if (minClamp <= intMin && maxClamp >= intMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    return failure();
+  }
+};
+
+void ClampOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
+                                          MLIRContext *context) {
+  results.insert<ClampIsNoOp>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // Operator Folders.
 //===----------------------------------------------------------------------===//
 
 OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
   if (input().getType() == getType())
     return input();
   return {};
if (llvm::equal(llvm::seq<int64_t>(0, perms.size()), perms) &&
     return input1();
   return {};
 }
 
 //===----------------------------------------------------------------------===//
 // TOSA Operator Verifiers.
 //===----------------------------------------------------------------------===//
 
-template <typename T>
-static LogicalResult verifyConvOp(T op) {
+template <typename T> static LogicalResult verifyConvOp(T op) {
   // All TOSA conv ops have an input() and weight().
   auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
   auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>();
 
   // Must be ranked tensor types
   if (!inputType) {
     op.emitOpError("expect a ranked tensor for input, got ") << op.input();
     return failure();

mlir/test/Dialect/Tosa/canonicalize.mlir

 func @cast_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xi32> {
   // CHECK: "tosa.cast"
   %0 = "tosa.cast"(%arg0) : (tensor<?x1xf32>) -> tensor<?x1xi32>
   return %0 : tensor<?x1xi32>
 }
 
 // -----
 
+// CHECK-LABEL: @clamp_not_noop
+func @clamp_not_noop(%arg0: tensor<4xi32>) -> tensor<4xi32> {
+  // CHECK: "tosa.clamp"
+  %0 = "tosa.clamp"(%arg0) {min_int = 1 : i64, max_int = 4 : i64, min_fp = 1.0 : f32, max_fp = 4.0 : f32} : (tensor<4xi32>) -> tensor<4xi32>
+  return %0 : tensor<4xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @clamp_float_is_noop
+func @clamp_float_is_noop(%arg0: tensor<4xf32>) -> tensor<4xf32> {
+  // CHECK: return %arg0
+  // CHECK-NOT: "tosa.clamp"
+  %0 = "tosa.clamp"(%arg0) {min_int = -128 : i64, max_int = 127 : i64, min_fp = -3.40282347E+38 : f32, max_fp = 3.40282347E+38 : f32} :  (tensor<4xf32>) -> tensor<4xf32>
+  return %0 : tensor<4xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @clamp_int8_is_noop
+func @clamp_int8_is_noop(%arg0: tensor<4xi8>) -> tensor<4xi8> {
+  // CHECK: return %arg0
+  // CHECK-NOT: "tosa.clamp"
+  %0 = "tosa.clamp"(%arg0) {min_int = -128 : i64, max_int = 127 : i64, min_fp = -3.40282347E+38 : f32, max_fp = 3.40282347E+38 : f32} :  (tensor<4xi8>) -> tensor<4xi8>
+  return %0 : tensor<4xi8>
+}
+
+// -----
+
+// CHECK-LABEL: @clamp_int16_is_noop
+func @clamp_int16_is_noop(%arg0: tensor<4xi16>) -> tensor<4xi16> {
+  // CHECK: return %arg0
+  // CHECK-NOT: "tosa.clamp"
+  %0 = "tosa.clamp"(%arg0) {min_int = -32768 : i64, max_int = 32767 : i64, min_fp = -3.40282347E+38 : f32, max_fp = 3.40282347E+38 : f32} :  (tensor<4xi16>) -> tensor<4xi16>
+  return %0 : tensor<4xi16>
+}
+
+// -----
+
+// CHECK-LABEL: @clamp_uint8_is_noop
+func @clamp_uint8_is_noop(%arg0: tensor<4xui8>) -> tensor<4xui8> {
+  // CHECK: return %arg0
+  // CHECK-NOT: "tosa.clamp"
+  %0 = "tosa.clamp"(%arg0) {min_int = 0 : i64, max_int = 255 : i64, min_fp = -3.40282347E+38 : f32, max_fp = 3.40282347E+38 : f32} :  (tensor<4xui8>) -> tensor<4xui8>
+  return %0 : tensor<4xui8>
+}
+
+// -----
+
 // CHECK-LABEL: @concat_fold
 func @concat_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
   // CHECK: return %arg0
   %0 = "tosa.concat"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32>
   return %0 : tensor<?x1xf32>
 }
 
 // -----