Fuse relu6 & relu1 to fc.
PiperOrigin-RevId: 306566180 Change-Id: I23c2e7c4301c0478ad16c26d46a7ad0f0cecef70
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@ -475,6 +475,28 @@ func @FuseFullyConnectedRelu(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32
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// CHECK: return %[[RES]]
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}
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// CHECK-LABEL: @FuseFullyConnectedRelu6
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func @FuseFullyConnectedRelu6(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32>, %arg2: tensor<128xf32>) -> tensor<1x128xf32> {
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%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>
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%1 = "tfl.relu6"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
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return %1 : tensor<1x128xf32>
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// CHECK: %[[RES:[0-9].*]] = "tfl.fully_connected"
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// CHECK-SAME: fused_activation_function = "RELU6"
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// CHECK: return %[[RES]]
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}
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// CHECK-LABEL: @FuseFullyConnectedRelu1
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func @FuseFullyConnectedRelu1(%arg0: tensor<1x256xf32>, %arg1: tensor<128x256xf32>, %arg2: tensor<128xf32>) -> tensor<1x128xf32> {
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%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>
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%1 = "tfl.relu_n1_to_1"(%0) : (tensor<1x128xf32>) -> tensor<1x128xf32>
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return %1 : tensor<1x128xf32>
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// CHECK: %[[RES:[0-9].*]] = "tfl.fully_connected"
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// CHECK-SAME: fused_activation_function = "RELU_N1_TO_1"
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// CHECK: return %[[RES]]
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}
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// CHECK-LABEL: @HardSwishPattern
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func @HardSwishPattern(%arg0: tensor<1xf32>) -> tensor<1xf32> {
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%three = constant dense<3.> : tensor<f32>
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@ -51,6 +51,9 @@ namespace TFL {
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//===----------------------------------------------------------------------===//
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// The actual Optimize Pass.
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namespace {
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const char kRelu[] = "RELU";
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const char kRelu6[] = "RELU6";
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const char kRelu1[] = "RELU_N1_TO_1";
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bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) {
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if (sq_op.getType().cast<ShapedType>().getRank() - 1 ==
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@ -300,10 +303,11 @@ struct FuseFullyConnectedAndAdd : public OpRewritePattern<TFL::AddOp> {
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};
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// TODO(b/136285429): Move to tablegen when variadic is supported.
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struct FuseFullyConnectedAndRelu : public OpRewritePattern<TFL::ReluOp> {
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using OpRewritePattern<TFL::ReluOp>::OpRewritePattern;
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template <typename ReluXOp, char const *Act>
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struct FuseFullyConnectedAndReluX : public OpRewritePattern<ReluXOp> {
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using OpRewritePattern<ReluXOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(TFL::ReluOp relu_op,
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LogicalResult matchAndRewrite(ReluXOp relu_op,
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PatternRewriter &rewriter) const override {
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Operation *input = relu_op.getOperand().getDefiningOp();
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if (!isa_and_nonnull<FullyConnectedOp>(input)) return failure();
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@ -311,7 +315,7 @@ struct FuseFullyConnectedAndRelu : public OpRewritePattern<TFL::ReluOp> {
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if (fully_connected_op.fused_activation_function() != "NONE")
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return failure();
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auto new_activation_func = rewriter.getStringAttr("RELU");
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auto new_activation_func = rewriter.getStringAttr(Act);
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auto new_weights_format =
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rewriter.getStringAttr(fully_connected_op.weights_format());
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auto new_keep_num_dims =
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@ -708,7 +712,10 @@ void Optimize::runOnFunction() {
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// we explore these potentially first and then fuse the binary ops with the
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// following ops in a second pattern match.
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TFL::populateWithGenerated(ctx, &patterns);
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patterns.insert<FuseFullyConnectedAndAdd, FuseFullyConnectedAndRelu,
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patterns.insert<FuseFullyConnectedAndAdd,
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FuseFullyConnectedAndReluX<TFL::ReluOp, kRelu>,
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FuseFullyConnectedAndReluX<TFL::Relu6Op, kRelu6>,
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FuseFullyConnectedAndReluX<TFL::Relu1Op, kRelu1>,
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FuseFullyConnectedAndMul>(ctx);
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applyPatternsAndFoldGreedily(func, patterns);
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