Fuse relu6 & relu1 to fc.

PiperOrigin-RevId: 306566180
Change-Id: I23c2e7c4301c0478ad16c26d46a7ad0f0cecef70

tensorflow/compiler/mlir/lite/tests/optimize.mlir

@@ -475,6 +475,28 @@ func @FuseFullyConnectedRelu(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32
   // CHECK: return %[[RES]]
 }
 
+// CHECK-LABEL: @FuseFullyConnectedRelu6
+func @FuseFullyConnectedRelu6(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32>, %arg2: tensor<128xf32>) -> tensor<1x128xf32> {
+  %0 = "tfl.fully_connected" (%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x256xf32>, tensor<128x256xf32>, tensor<128xf32>) -> tensor<1x128xf32>
+  %1 = "tfl.relu6"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
+  return %1 : tensor<1x128xf32>
+
+  // CHECK: %[[RES:[0-9].*]] = "tfl.fully_connected"
+  // CHECK-SAME: fused_activation_function = "RELU6"
+  // CHECK: return %[[RES]]
+}
+
+// CHECK-LABEL: @FuseFullyConnectedRelu1
+func @FuseFullyConnectedRelu1(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32>, %arg2: tensor<128xf32>) -> tensor<1x128xf32> {
+  %0 = "tfl.fully_connected" (%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x256xf32>, tensor<128x256xf32>, tensor<128xf32>) -> tensor<1x128xf32>
+  %1 = "tfl.relu_n1_to_1"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
+  return %1 : tensor<1x128xf32>
+
+  // CHECK: %[[RES:[0-9].*]] = "tfl.fully_connected"
+  // CHECK-SAME: fused_activation_function = "RELU_N1_TO_1"
+  // CHECK: return %[[RES]]
+}
+
 // CHECK-LABEL: @HardSwishPattern
 func @HardSwishPattern(%arg0: tensor<1xf32>) -> tensor<1xf32> {
   %three = constant dense<3.> : tensor<f32>

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

@@ -51,6 +51,9 @@ namespace TFL {
 //===----------------------------------------------------------------------===//
 // The actual Optimize Pass.
 namespace {
+const char kRelu[] = "RELU";
+const char kRelu6[] = "RELU6";
+const char kRelu1[] = "RELU_N1_TO_1";
 
 bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) {
   if (sq_op.getType().cast<ShapedType>().getRank() - 1 ==
@@ -300,10 +303,11 @@ struct FuseFullyConnectedAndAdd : public OpRewritePattern<TFL::AddOp> {
 };
 
 // TODO(b/136285429): Move to tablegen when variadic is supported.
-struct FuseFullyConnectedAndRelu : public OpRewritePattern<TFL::ReluOp> {
-  using OpRewritePattern<TFL::ReluOp>::OpRewritePattern;
+template <typename ReluXOp, char const *Act>
+struct FuseFullyConnectedAndReluX : public OpRewritePattern<ReluXOp> {
+  using OpRewritePattern<ReluXOp>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(TFL::ReluOp relu_op,
+  LogicalResult matchAndRewrite(ReluXOp relu_op,
                                 PatternRewriter &rewriter) const override {
     Operation *input = relu_op.getOperand().getDefiningOp();
     if (!isa_and_nonnull<FullyConnectedOp>(input)) return failure();
@@ -311,7 +315,7 @@ struct FuseFullyConnectedAndRelu : public OpRewritePattern<TFL::ReluOp> {
     if (fully_connected_op.fused_activation_function() != "NONE")
       return failure();
 
-    auto new_activation_func = rewriter.getStringAttr("RELU");
+    auto new_activation_func = rewriter.getStringAttr(Act);
     auto new_weights_format =
         rewriter.getStringAttr(fully_connected_op.weights_format());
     auto new_keep_num_dims =
@@ -708,7 +712,10 @@ void Optimize::runOnFunction() {
   // we explore these potentially first and then fuse the binary ops with the
   // following ops in a second pattern match.
   TFL::populateWithGenerated(ctx, &patterns);
-  patterns.insert<FuseFullyConnectedAndAdd, FuseFullyConnectedAndRelu,
+  patterns.insert<FuseFullyConnectedAndAdd,
+                  FuseFullyConnectedAndReluX<TFL::ReluOp, kRelu>,
+                  FuseFullyConnectedAndReluX<TFL::Relu6Op, kRelu6>,
+                  FuseFullyConnectedAndReluX<TFL::Relu1Op, kRelu1>,
                   FuseFullyConnectedAndMul>(ctx);
   applyPatternsAndFoldGreedily(func, patterns);