Add type and shape constraints to TFLite Add op

PiperOrigin-RevId: 305591811
Change-Id: I12a9b832fc9ee385c77dc1a19f24aa9debcca641

tensorflow/compiler/mlir/lite/BUILD

@@ -307,7 +307,7 @@ cc_library(
         "transforms/optimize_functional_ops.cc",
         "transforms/prepare_composite_functions_tf.cc",
         "transforms/prepare_tf.cc",
-        "transforms/runtime_verify.cc",
+        "transforms/runtime_type_verify.cc",
         "transforms/split_merged_operands.cc",
         "transforms/trim_functions_tf.cc",
         "transforms/while_loop_outline.cc",

tensorflow/compiler/mlir/lite/common/tfl_pass_config.h

@@ -36,8 +36,7 @@ struct PassConfig {
         form_clusters(false),
         unfold_batch_matmul(true),
         legalize_tf_while(true),
-        shape_inference(true),
-        runtime_verification(true) {}
+        shape_inference(true) {}
 
   // If `emit_builtin_tflite_ops` is true, TF Lite legalization passes will be
   // added, which produces TF Lite ops.
@@ -66,8 +65,6 @@ struct PassConfig {
   bool legalize_tf_while;
   // Whether to do shape inference.
   bool shape_inference;
-  // Whether to do TFLite runtime verification.
-  bool runtime_verification;
 };
 
 }  // namespace TFL

tensorflow/compiler/mlir/lite/converter_gen.cc

@@ -441,7 +441,7 @@ static bool RuntimeVerifierWriterMain(raw_ostream &os, RecordKeeper &records) {
 
     mlir::tblgen::FmtContext verify_ctx;
     os << "::mlir::LogicalResult " << op.getCppClassName()
-       << "::VerifyTflRuntimeConstraints(::mlir::Operation *op, bool "
+       << "::VerifyTflRuntimeTypes(::mlir::Operation *op, bool "
           "failure_on_operand_type_mismatch) {\n";
     os << "  auto top = cast<" << op.getCppClassName() << ">(op); (void)top;\n";
     verify_ctx.withOp("top");
@@ -466,25 +466,6 @@ static bool RuntimeVerifierWriterMain(raw_ostream &os, RecordKeeper &records) {
                              "operand");
     GenOperandResultVerifier(os, def->getValueAsDag("results")->getArgs(),
                              "result");
-
-    for (auto &trait : op.getTraits()) {
-      if (!trait.getDef().isSubClassOf("GenInternalOpTrait")) {
-        continue;
-      }
-      if (trait.getDef().getValueAsString("trait") !=
-          "OpTrait::TFLRuntimeOpTrait") {
-        continue;
-      }
-
-      auto *val = trait.getDef().getValue("tflRuntimePredicate");
-      if (!val) continue;
-
-      mlir::tblgen::Pred pred(dyn_cast<llvm::DefInit>(val->getValue()));
-      os << tgfmt(
-          "  if (!($0)) {\n    "
-          "    return ::mlir::LogicalResult::Failure;\n  }\n",
-          &verify_ctx, tgfmt(pred.getCondition(), &verify_ctx));
-    }
     os << "  return top.verify();\n}\n";
   }
 

tensorflow/compiler/mlir/lite/ir/tfl_op_interfaces.td

@@ -86,7 +86,7 @@ def TFL_RuntimeVerification : OpInterface<"TflRuntimeVerifyOpInterface"> {
   let methods = [
     StaticInterfaceMethod<
       [{Returns whether the op's operands/results are supported by runtime.}],
-      "LogicalResult", "VerifyTflRuntimeConstraints",
+      "LogicalResult", "VerifyTflRuntimeTypes",
       (ins "Operation*":$op, "bool":$failure_on_operand_type_mismatch)
     >,
   ];

tensorflow/compiler/mlir/lite/ir/tfl_ops.cc

@@ -46,30 +46,6 @@ namespace mlir {
 #include "tensorflow/compiler/mlir/lite/ir/tfl_structs.cc.inc"
 namespace TFL {
 
-// Returns true when the given two types have the same shape or broadcastable
-// shape within the given rank. If any given shapes are non-static, this method
-// returns true.
-bool IsBinaryOperandsHaveSameShapesOrBroadcastableShape(Type lhs, Type rhs,
-                                                        int max_bcast_rank) {
-  // Ignore shape checking on the non-static shapes for model compatibility.
-  auto lhs_shaped_type = lhs.dyn_cast<ShapedType>();
-  if (!lhs_shaped_type || !lhs_shaped_type.hasStaticShape()) return true;
-  auto rhs_shaped_type = rhs.dyn_cast<ShapedType>();
-  if (!rhs_shaped_type || !rhs_shaped_type.hasStaticShape()) return true;
-
-  if (lhs_shaped_type.getShape().equals(rhs_shaped_type.getShape()))
-    return true;
-
-  SmallVector<int64_t, 4> result_shape;
-  if (!OpTrait::util::getBroadcastedShape(lhs_shaped_type.getShape(),
-                                          rhs_shaped_type.getShape(),
-                                          result_shape)) {
-    return false;
-  }
-  return lhs_shaped_type.getRank() <= max_bcast_rank &&
-         rhs_shaped_type.getRank() <= max_bcast_rank;
-}
-
 //===----------------------------------------------------------------------===//
 // TensorFlowLiteDialect
 //===----------------------------------------------------------------------===//

tensorflow/compiler/mlir/lite/ir/tfl_ops.td

@@ -106,22 +106,6 @@ class DerivedShapeAttr<code body> : DerivedAttr<"ArrayRef<int64_t>", body>;
 class DerivedTFLiteTypeAttr<code body> :
   DerivedAttr<"tflite::TensorType", body>;
 
-// TFL Runtime op trait predicate.
-class TFL_RuntimePredOpTrait<string desc, Pred pred> :
-    GenInternalOpTrait<"TFLRuntimeOpTrait"> {
-  Pred tflRuntimePredicate = pred;
-  string tflRuntimeDescription = desc;
-}
-
-class TFL_BinaryOperandsHaveSameShapesOrBroadcastableShape<
-    int i, int j, int max_bcast_rank> :
-  TFL_RuntimePredOpTrait<"operand #" # i # " and operand #" # j #
-      " have the same shape or broadcastable shapes within the rank " #
-      max_bcast_rank,
-    CPred<"TFL::IsBinaryOperandsHaveSameShapesOrBroadcastableShape("
-              "$_op.getOperand(" # i # ").getType(), $_op.getOperand(" # j #
-              ").getType(), " # max_bcast_rank # ")">>;
-
 // These additional types/type constraints here are used to decouple the ops
 // from runtime support for the ops. Prefer to use these types when defining
 // new TF_Ops for uniformity.
@@ -376,9 +360,10 @@ an output element, this operation computes \\(y = |x|\\).
   let hasFolder = 1;
 }
 
-def TFL_AddOp : TFL_Op<"add", [
-    TFL_BinaryOperandsHaveSameShapesOrBroadcastableShape<0, 1, 4>,
-    ResultsBroadcastableShape, NoSideEffect, Commutative, TFL_GpuTargetOp]> {
+def TFL_AddOp : TFL_Op<"add", [ResultsBroadcastableShape,
+                               NoSideEffect,
+                               Commutative,
+                               TFL_GpuTargetOp]> {
   let summary = "Addition operator";
 
   let description = [{
@@ -386,11 +371,11 @@ def TFL_AddOp : TFL_Op<"add", [
   }];
 
   let arguments = (
-    ins TFL_TensorOf<[F32, I32, QI8, QUI8, QI16]>:$lhs,
-    TFL_TensorOf<[F32, I32, QI8, QUI8, QI16]>:$rhs,
+    ins AnyTensor:$lhs,
+    AnyTensor:$rhs,
     TFL_AFAttr:$fused_activation_function);
 
-  let results = (outs TFL_TensorOf<[F32, I32, QI8, QUI8, QI16]>:$output);
+  let results = (outs AnyTensor:$output);
 
   let hasFolder = 1;
 

tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc

@@ -285,7 +285,7 @@ Status ConvertMLIRToTFLiteFlatBuffer(const toco::TocoFlags& toco_flags,
   if (pass_config.legalize_tf_while) {
     pm.addPass(mlir::TFL::CreateWhileOutlinePass());
   }
-  pm.addPass(mlir::TFL::CreateRuntimeVerifyPass());
+  pm.addPass(mlir::TFL::CreateRuntimeTypeVerifyPass());
 
   auto status = ConvertTFExecutorToTFLOrFlatbuffer(
       module.get(), /*export_to_mlir=*/false, emit_builtin_tflite_ops,

tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir

@@ -9,20 +9,6 @@ func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
 // CHECK:  return
 }
 
-// CHECK-LABEL: testAddHighDimsHaveSameShape
-func @testAddHighDimsHaveSameShape(%arg0: tensor<1x2x3x4x5x6x7x8xi32>, %arg1: tensor<1x2x3x4x5x6x7x8xi32>) -> tensor<1x2x3x4x5x6x7x8xi32> {
-  // CHECK: tfl.add %arg0, %arg1 {fused_activation_function = "NONE"}
-  %0 = "tf.Add"(%arg0, %arg1) : (tensor<1x2x3x4x5x6x7x8xi32>, tensor<1x2x3x4x5x6x7x8xi32>) -> tensor<1x2x3x4x5x6x7x8xi32>
-  return %0 : tensor<1x2x3x4x5x6x7x8xi32>
-}
-
-// CHECK-LABEL: testAddTooHighBroadcastableDims
-func @testAddTooHighBroadcastableDims(%arg0: tensor<1x2x3x4x5x6xi32>, %arg1: tensor<1x2x3x4x5x1xi32>) -> tensor<1x2x3x4x5x6xi32> {
-  // expected-error @+1 {{'tfl.add' op failed to verify that operand #0 and operand #1 have the same shape or broadcastable shapes within the rank 4}}
-  %0 = "tf.Add"(%arg0, %arg1) : (tensor<1x2x3x4x5x6xi32>, tensor<1x2x3x4x5x1xi32>) -> tensor<1x2x3x4x5x6xi32>
-  return %0 : tensor<1x2x3x4x5x6xi32>
-}
-
 func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> {
   %2 = "tf.LeakyRelu"(%arg0) {alpha = 0.1 : f32} : (tensor<1xf32>) -> tensor<1xf32>
   return %2: tensor<1xf32>

tensorflow/compiler/mlir/lite/tf_tfl_passes.cc

@@ -173,8 +173,7 @@ void AddTFToTFLConversionPasses(const mlir::TFL::PassConfig& pass_config,
     pass_manager->addPass(
         mlir::TFL::CreatePrepareTFPass(pass_config.unfold_batch_matmul));
     pass_manager->addNestedPass<mlir::FuncOp>(mlir::createCanonicalizerPass());
-    pass_manager->addPass(
-        mlir::TFL::CreateLegalizeTFPass(pass_config.runtime_verification));
+    pass_manager->addPass(mlir::TFL::CreateLegalizeTFPass());
     pass_manager->addPass(mlir::TFL::CreateOptimizePass());
     // This pass operates on TensorFlow ops but is triggered after legalization
     // so that it can target constants introduced once TensorFlow Identity ops
@@ -256,8 +255,7 @@ void CreateTFLStandardPipeline(OpPassManager& pm,
   // TFLite dialect passes.
   pm.addPass(mlir::TFL::CreatePrepareTFPass(true));
   pm.addNestedPass<mlir::FuncOp>(mlir::createCanonicalizerPass());
-  pm.addPass(
-      mlir::TFL::CreateLegalizeTFPass(/*run_tfl_runtime_verification=*/true));
+  pm.addPass(mlir::TFL::CreateLegalizeTFPass());
   pm.addPass(mlir::TFL::CreateOptimizePass());
   pm.addPass(mlir::TFL::CreateOptimizeFunctionalOpsPass());
 
@@ -270,7 +268,7 @@ void CreateTFLStandardPipeline(OpPassManager& pm,
 
   pm.addPass(mlir::TFL::CreateWhileOutlinePass());
 
-  pm.addPass(mlir::TFL::CreateRuntimeVerifyPass());
+  pm.addPass(mlir::TFL::CreateRuntimeTypeVerifyPass());
 }
 
 // Registers a pass pipeline for the standard TFL passes.

tensorflow/compiler/mlir/lite/tf_tfl_translate.cc

@@ -214,7 +214,7 @@ int main(int argc, char **argv) {
   if (pass_config.legalize_tf_while) {
     pm.addPass(mlir::TFL::CreateWhileOutlinePass());
   }
-  pm.addPass(mlir::TFL::CreateRuntimeVerifyPass());
+  pm.addPass(mlir::TFL::CreateRuntimeTypeVerifyPass());
 
   std::string result;
   auto status = tensorflow::ConvertTFExecutorToTFLOrFlatbuffer(

tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc

@@ -70,21 +70,8 @@ constexpr char kUnidirectionalSequenceRnn[] = "tf.UnidirectionalSequenceRnn";
 constexpr char kTfLiteInputIndices[] = "_tflite_input_indices";
 
 // Legalize operations in functions.
-class LegalizeTF : public PassWrapper<LegalizeTF, FunctionPass> {
- public:
-  LegalizeTF() = default;
-  LegalizeTF(const LegalizeTF&) {}
-  explicit LegalizeTF(bool run_tfl_runtime_verification) {
-    run_tfl_runtime_verification_ = run_tfl_runtime_verification;
-  }
-
-  /// Performs the lowering to TFLite dialect.
+struct LegalizeTF : public PassWrapper<LegalizeTF, FunctionPass> {
   void runOnFunction() override;
-
- private:
-  Option<bool> run_tfl_runtime_verification_{
-      *this, "run-tfl-runtime-verification",
-      llvm::cl::desc("Allow tfl runtime verification."), llvm::cl::init(true)};
 };
 
 // Returns true if all tensor value in `values` has static shape and same shape.
@@ -754,19 +741,13 @@ void LegalizeTF::runOnFunction() {
   // graph.
   target.addLegalOp<mlir::ConstantOp>();
   target.addLegalOp<ConstOp>();
-  if (run_tfl_runtime_verification_) {
   target.addDynamicallyLegalDialect<TensorFlowLiteDialect>(
-        Optional<ConversionTarget::DynamicLegalityCallbackFn>(
-            [](Operation* op) {
+      Optional<ConversionTarget::DynamicLegalityCallbackFn>([](Operation* op) {
         auto tfl_op = dyn_cast_or_null<TflRuntimeVerifyOpInterface>(op);
         if (!tfl_op) return false;
-              return succeeded(tfl_op.VerifyTflRuntimeConstraints(
-                  tfl_op.getOperation(),
-                  /*failure_on_operand_type_mismatch=*/false));
+        return succeeded(tfl_op.VerifyTflRuntimeTypes(
+            tfl_op.getOperation(), /*failure_on_operand_type_mismatch=*/false));
       }));
-  } else {
-    target.addLegalDialect<TensorFlowLiteDialect>();
-  }
   // Keep trying to convert.
   // TODO(karimnosseir): This is similar to what apply greedy patterns does.
   // Look if there is a function that tries until it converge.
@@ -782,9 +763,8 @@ void LegalizeTF::runOnFunction() {
 }  // namespace
 
 // Creates an instance of the TensorFlow Lite dialect LegalizeTF pass.
-std::unique_ptr<OperationPass<FuncOp>> CreateLegalizeTFPass(
-    bool run_tfl_runtime_verification) {
-  return std::make_unique<LegalizeTF>(run_tfl_runtime_verification);
+std::unique_ptr<OperationPass<FuncOp>> CreateLegalizeTFPass() {
+  return std::make_unique<LegalizeTF>();
 }
 
 static PassRegistration<LegalizeTF> pass(

tensorflow/compiler/mlir/lite/transforms/passes.h

@@ -30,11 +30,7 @@ namespace TFL {
 class QuantizationSpecs;
 
 // Creates an instance of the TensorFlow Lite dialect LegalizeTF pass.
-// When the given run_tfl_runtime_verification value is true, it will check each
-// TFL builtin op towards the TFL runtime capability and the incompatible TF ops
-// will be left in the graph without getting legalized.
-std::unique_ptr<OperationPass<FuncOp>> CreateLegalizeTFPass(
-    bool run_tfl_runtime_verification);
+std::unique_ptr<OperationPass<FuncOp>> CreateLegalizeTFPass();
 
 // Creates an instance of the TensorFlow Lite dialect Optimize pass.
 std::unique_ptr<OperationPass<FuncOp>> CreateOptimizePass();
@@ -95,8 +91,8 @@ std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeTFWhilePass();
 // Creates an instance of the TensorFlow Lite dialect WhileOp outline pass.
 std::unique_ptr<OperationPass<ModuleOp>> CreateWhileOutlinePass();
 
-// Verifies runtime constraints.
-std::unique_ptr<OperationPass<FuncOp>> CreateRuntimeVerifyPass();
+// Verifies runtime supports types used.
+std::unique_ptr<OperationPass<FuncOp>> CreateRuntimeTypeVerifyPass();
 
 }  // namespace TFL
 

tensorflow/compiler/mlir/lite/transforms/runtime_type_verify.cc

@@ -22,32 +22,34 @@ namespace mlir {
 namespace TFL {
 namespace {
 
-// This pass verifies that the TFL ops meet the TFL runtime constraints.
-class RuntimeVerifyPass
-    : public mlir::PassWrapper<RuntimeVerifyPass, FunctionPass> {
+// This pass verifies that the operands and results types are supported by
+// TFLite runtime.
+class RuntimeTypeVerifyPass
+    : public mlir::PassWrapper<RuntimeTypeVerifyPass, FunctionPass> {
  public:
-  explicit RuntimeVerifyPass() {}
+  explicit RuntimeTypeVerifyPass() {}
 
  private:
   void runOnFunction() override;
 };
 
-void RuntimeVerifyPass::runOnFunction() {
+void RuntimeTypeVerifyPass::runOnFunction() {
   getFunction().walk([&](TflRuntimeVerifyOpInterface op) {
-    if (failed(op.VerifyTflRuntimeConstraints(
-            op.getOperation(), /*failure_on_operand_type_mismatch=*/true)))
+    if (failed(op.VerifyTflRuntimeTypes(
+            op.getOperation(),
+            /*failure_on_operand_type_mismatch=*/true)))
       signalPassFailure();
   });
 }
 }  // namespace
 
-// Verifies TFL runtime constraints.
-std::unique_ptr<OperationPass<FuncOp>> CreateRuntimeVerifyPass() {
-  return std::make_unique<RuntimeVerifyPass>();
+// Verifies runtime supports types used.
+std::unique_ptr<OperationPass<FuncOp>> CreateRuntimeTypeVerifyPass() {
+  return std::make_unique<RuntimeTypeVerifyPass>();
 }
 
-static PassRegistration<RuntimeVerifyPass> pass("tfl-runtime-verify",
-                                                "TFLite runtime verification");
+static PassRegistration<RuntimeTypeVerifyPass> pass(
+    "tfl-runtime-verify", "TFLite runtime verification");
 
 }  // namespace TFL
 }  // namespace mlir

tensorflow/lite/testing/op_tests/hardswish.py

@@ -48,17 +48,10 @@ def make_hardswish_tests(options):
   """Make a set of tests to do hardswish."""
 
   # Chose a set of parameters
-  if options.run_with_flex:
-    # Only Flex is able to execute on the data bigger than four dimension.
   test_parameters = [{
       "input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
                       [3, 15, 14, 3], [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
   }]
-  else:
-    test_parameters = [{
-        "input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
-                        [3, 15, 14, 3]],
-    }]
 
   def build_graph(parameters):
     inp = tf.compat.v1.placeholder(