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This change is to provide users with the option `canonicalizing_inf_a…
…s_min_max_float` to canonicalize boundary values in the IR replacing -Inf/Inf with MIN/MAX float value. An option is provided to the user to enable this option. PiperOrigin-RevId: 630372176
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91 changes: 91 additions & 0 deletions
91
tensorflow/compiler/mlir/lite/tests/canonicalize_boundary_value.mlir
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| // RUN: tf-opt %s --canonicalize-boundary-value --split-input-file | FileCheck %s | ||
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| // CHECK-LABEL: func.func @clamp_neg_inf_f32() -> tensor<f32> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<-3.40282347E+38> : tensor<f32> | ||
| // CHECK: return %[[CONST]] : tensor<f32> | ||
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| func.func @clamp_neg_inf_f32() -> tensor<f32> { | ||
| %ret = stablehlo.constant dense<0xFF800000> : tensor<f32> | ||
| return %ret : tensor<f32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f32() -> tensor<f32> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<3.40282347E+38> : tensor<f32> | ||
| // CHECK: return %[[CONST]] : tensor<f32> | ||
| func.func @clamp_pos_inf_f32() -> tensor<f32> { | ||
| %ret = stablehlo.constant dense<0x7F800000> : tensor<f32> | ||
| return %ret : tensor<f32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f32_tensor() -> tensor<1x4xf32> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<{{\[\[}}3.40282347E+38, 1.000000e+01, 2.000000e+01, -3.40282347E+38]]> : tensor<1x4xf32> | ||
| // CHECK: return %[[CONST]] : tensor<1x4xf32> | ||
| func.func @clamp_pos_inf_f32_tensor() -> tensor<1x4xf32> { | ||
| %ret = stablehlo.constant dense<[[0x7F800000, 10.0, 20.0, 0xFF800000]]> : tensor<1x4xf32> | ||
| return %ret : tensor<1x4xf32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_neg_inf_f16() -> tensor<f16> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<-6.550400e+04> : tensor<f16> | ||
| // CHECK: return %[[CONST]] : tensor<f16> | ||
| func.func @clamp_neg_inf_f16() -> tensor<f16> { | ||
| %ret = stablehlo.constant dense<0xFC00> : tensor<f16> | ||
| return %ret : tensor<f16> | ||
| } | ||
| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_neg_inf_bf16() -> tensor<bf16> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<-1.038460e+34> : tensor<bf16> | ||
| // CHECK: return %[[CONST]] : tensor<bf16> | ||
| func.func @clamp_neg_inf_bf16() -> tensor<bf16> { | ||
| %ret = stablehlo.constant dense<0xF800> : tensor<bf16> | ||
| return %ret : tensor<bf16> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f16() -> tensor<f16> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<6.550400e+04> : tensor<f16> | ||
| // CHECK: return %[[CONST]] : tensor<f16> | ||
| func.func @clamp_pos_inf_f16() -> tensor<f16> { | ||
| %ret = stablehlo.constant dense<0x7C00> : tensor<f16> | ||
| return %ret : tensor<f16> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f16_tensor() -> tensor<1x4xf16> { | ||
| // CHECK: %[[CONST:.*]] = stablehlo.constant dense<{{\[\[}}6.550400e+04, 1.000000e+01, 2.000000e+01, -6.550400e+04]]> : tensor<1x4xf16> | ||
| // CHECK: return %[[CONST]] : tensor<1x4xf16> | ||
| func.func @clamp_pos_inf_f16_tensor() -> tensor<1x4xf16> { | ||
| %ret = stablehlo.constant dense<[[0x7C00, 10.0, 20.0, 0xFC00]]> : tensor<1x4xf16> | ||
| return %ret : tensor<1x4xf16> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f16_tensor_tf_const() -> tensor<3xf16> { | ||
| // CHECK: %[[CONST:.*]] = "tf.Const"() <{value = dense<6.550400e+04> : tensor<3xf16>}> : () -> tensor<3xf16> | ||
| // CHECK: return %[[CONST]] : tensor<3xf16> | ||
| func.func @clamp_pos_inf_f16_tensor_tf_const() -> tensor<3xf16> { | ||
| %ret = "tf.Const"() <{value = dense<0x7C00> : tensor<3xf16>}> : () -> tensor<3xf16> | ||
| return %ret : tensor<3xf16> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: func.func @clamp_pos_inf_f16_arith_op() -> f16 { | ||
| // CHECK: %[[CONST:.*]] = arith.constant 6.550400e+04 : f16 | ||
| // CHECK: return %[[CONST]] : f16 | ||
| func.func @clamp_pos_inf_f16_arith_op() -> f16 { | ||
| %ret = arith.constant 0x7C00 : f16 | ||
| return %ret : f16 | ||
| } | ||
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121 changes: 121 additions & 0 deletions
121
tensorflow/compiler/mlir/lite/transforms/canonicalize_boundary_value_pass.cc
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| /* Copyright 2024 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #include <memory> | ||
| #include <utility> | ||
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| #include "llvm/ADT/STLExtras.h" | ||
| #include "llvm/Support/Casting.h" | ||
| #include "mlir/Dialect/Arith/IR/Arith.h" // from @llvm-project | ||
| #include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project | ||
| #include "mlir/IR/Builders.h" // from @llvm-project | ||
| #include "mlir/IR/BuiltinAttributeInterfaces.h" // from @llvm-project | ||
| #include "mlir/IR/BuiltinAttributes.h" // from @llvm-project | ||
| #include "mlir/IR/BuiltinOps.h" // from @llvm-project | ||
| #include "mlir/IR/BuiltinTypeInterfaces.h" // from @llvm-project | ||
| #include "mlir/IR/BuiltinTypes.h" // from @llvm-project | ||
| #include "mlir/IR/Diagnostics.h" // from @llvm-project | ||
| #include "mlir/IR/PatternMatch.h" // from @llvm-project | ||
| #include "mlir/Pass/Pass.h" // from @llvm-project | ||
| #include "mlir/Support/LLVM.h" // from @llvm-project | ||
| #include "mlir/Support/LogicalResult.h" // from @llvm-project | ||
| #include "mlir/Transforms/DialectConversion.h" // from @llvm-project | ||
| #include "mlir/Transforms/GreedyPatternRewriteDriver.h" // from @llvm-project | ||
| #include "stablehlo/dialect/StablehloOps.h" // from @stablehlo | ||
| #include "tensorflow/compiler/mlir/lite/transforms/passes.h" | ||
| #include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h" | ||
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| namespace mlir { | ||
| namespace TFL { | ||
| namespace { | ||
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| #define DEBUG_TYPE "canonicalize-boundary-value" | ||
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| #define GEN_PASS_DEF_CANONICALIZEBOUNDARYVALUEPASS | ||
| #include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc" | ||
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| class CanonicalizeBoundaryValuePass | ||
| : public impl::CanonicalizeBoundaryValuePassBase< | ||
| CanonicalizeBoundaryValuePass> { | ||
| void runOnOperation() override; | ||
| }; | ||
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| // Clamp constant -Inf/Inf to MIN/MAX float value. | ||
| template <typename OpTy> | ||
| struct ClampInfToMinMaxFloat : public OpRewritePattern<OpTy> { | ||
| using OpRewritePattern<OpTy>::OpRewritePattern; | ||
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| LogicalResult matchAndRewrite(OpTy const_op, | ||
| PatternRewriter& rewriter) const override { | ||
| Attribute attr = const_op.getValueAttr(); | ||
| if (auto float_attr = llvm::dyn_cast<FloatAttr>(attr)) { | ||
| if (float_attr.getValue().isInfinity()) { | ||
| FloatType float_type = llvm::dyn_cast<FloatType>(const_op.getType()); | ||
| if (!float_type) return failure(); | ||
| rewriter.replaceOpWithNewOp<OpTy>( | ||
| const_op, rewriter.getFloatAttr( | ||
| float_type, APFloat::getLargest( | ||
| float_type.getFloatSemantics(), | ||
| float_attr.getValue().isNegative()))); | ||
| return success(); | ||
| } | ||
| } | ||
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| ElementsAttr tensor_attr = llvm::dyn_cast<ElementsAttr>(attr); | ||
| if (!tensor_attr) return failure(); | ||
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| Type type = tensor_attr.getType(); | ||
| ShapedType tensor_type = llvm::cast<ShapedType>(type); | ||
| auto float_type = dyn_cast<FloatType>(tensor_type.getElementType()); | ||
| if (!float_type) return failure(); | ||
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| auto vals_orig = tensor_attr.getValues<APFloat>(); | ||
| // If all values are finite, no need to rewrite. | ||
| if (llvm::all_of(vals_orig, [&](APFloat val) { return !val.isInfinity(); })) | ||
| return failure(); | ||
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| SmallVector<APFloat> vals_new(llvm::map_range(vals_orig, [&](APFloat val) { | ||
| return val.isInfinity() | ||
| ? APFloat::getLargest(float_type.getFloatSemantics(), | ||
| val.isNegative()) | ||
| : val; | ||
| })); | ||
| rewriter.replaceOpWithNewOp<OpTy>( | ||
| const_op, DenseElementsAttr::get(tensor_type, vals_new)); | ||
| return success(); | ||
| } | ||
| }; | ||
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| void CanonicalizeBoundaryValuePass::runOnOperation() { | ||
| auto* ctx = &getContext(); | ||
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| RewritePatternSet patterns(ctx); | ||
| patterns.add<ClampInfToMinMaxFloat<stablehlo::ConstantOp>, | ||
| ClampInfToMinMaxFloat<TF::ConstOp>, | ||
| ClampInfToMinMaxFloat<arith::ConstantOp>>(ctx); | ||
| if (failed( | ||
| applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)))) { | ||
| return signalPassFailure(); | ||
| } | ||
| } | ||
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| } // end namespace | ||
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| std::unique_ptr<OperationPass<ModuleOp>> CreateCanonicalizeBoundaryValuePass() { | ||
| return std::make_unique<CanonicalizeBoundaryValuePass>(); | ||
| } | ||
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| } // end namespace TFL | ||
| } // end namespace mlir |
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