wehub-resource-sync
127 lines
5.0 KiB
C++
127 lines
5.0 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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// This header file defines common validators used by TFLite transformation
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// passes to validate op attributes or values.
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#ifndef TENSORFLOW_COMPILER_MLIR_UTILS_VALIDATORS_H_
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#define TENSORFLOW_COMPILER_MLIR_UTILS_VALIDATORS_H_
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#include <cstdint>
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#include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project
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#include "mlir/IR/Attributes.h" // from @llvm-project
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#include "mlir/IR/BuiltinAttributeInterfaces.h" // from @llvm-project
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#include "mlir/IR/BuiltinAttributes.h" // from @llvm-project
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#include "mlir/IR/BuiltinTypes.h" // from @llvm-project
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#include "mlir/IR/Operation.h" // from @llvm-project
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#include "mlir/IR/Value.h" // from @llvm-project
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#include "mlir/Support/LLVM.h" // from @llvm-project
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namespace mlir {
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namespace TF {
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// TODO(jpienaar): Change these to being one of these variants and/or generate
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// these predicates.
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// Returns true if the given TensorFlow op does not have a `data_format`
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// attribute (then default to "NHWC"), or its `data_format` attribute is "NHWC".
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inline bool TFDataFormatIsNHWC(Operation *op) {
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auto attr = op->getAttrOfType<StringAttr>("data_format");
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return !attr || attr.getValue() == "NHWC";
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}
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// Returns true if the given TensorFlow op does not have a `data_format`
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// attribute (then default to "NDHWC"), or its `data_format` attribute is
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// "NDHWC".
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inline bool TFDataFormatIsNDHWC(Operation *op) {
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auto attr = op->getAttrOfType<StringAttr>("data_format");
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return !attr || attr.getValue() == "NDHWC";
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}
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// Returns true if the given `op`
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// * has an attribute with the given `name`,
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// * and the attribute is an integer list of the form [1, X, Y, 1],
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// and writes X, Y as 32-bit integer attribute to `x`, `y`.
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bool TFIntListIs1XY1(Operation *op, StringRef name, IntegerAttr *x,
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IntegerAttr *y);
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// Returns true if the attribute is an integer list of the form [1, X, Y, 1].
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bool TFIntListIs1XY1(Attribute attr);
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// Returns true if the attribute is an integer list of the form [1, 1, X, Y].
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bool TFIntListIs11XY(Attribute attr);
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// Returns true if the given `op`
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// * has an attribute with the given `name`,
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// * and the attribute is an integer list of the form [1, X, Y, Z, 1],
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// and writes X, Y as 32-bit integer attribute to `x`, `y`, z.
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bool TFIntListIs1XYZ1(Operation *op, StringRef name, IntegerAttr *x,
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IntegerAttr *y, IntegerAttr *z);
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// Returns true if every element of the attribute is 1. All elements of `attr`
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// must be `IntegerAttr`.
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bool TFIntListIsAllOnes(Attribute attr);
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// Returns true iff the given value is a float32 tensor.
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// is "DT_FLOAT".
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inline bool TFTypeIsFloat32Tensor(Value value) {
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auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
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if (!tensorType) return false;
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return tensorType.getElementType().isF32();
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}
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// Returns true iff the given value is a bf16 tensor.
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inline bool TFTypeIsBFloat16Tensor(Value value) {
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auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
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if (!tensorType) return false;
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return tensorType.getElementType().isBF16();
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}
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// Returns true iff the given value is a f16 tensor.
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inline bool TFTypeIsHalfTensor(Value value) {
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auto tensorType = mlir::dyn_cast<TensorType>(value.getType());
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if (!tensorType) return false;
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return tensorType.getElementType().isF16();
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}
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// Returns true iff the given value is a f16 or bf16 tensor.
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inline bool TFTypeIsBFloat16OrHalfTensor(Value value) {
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return TFTypeIsBFloat16Tensor(value) || TFTypeIsHalfTensor(value);
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}
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// Returns true iff the given TensorFlow op has a `padding` attribute whose
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// value is "SAME" or "VALID", and writes the attribute to `padding`.
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inline bool TFPaddingIsSameOrValid(Operation *op, StringAttr *padding) {
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auto padding_attr = op->getAttrOfType<StringAttr>("padding");
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if (padding_attr.getValue() != "SAME" && padding_attr.getValue() != "VALID")
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return false;
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*padding = padding_attr;
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return true;
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}
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/// Returns whether the given `a` and `b` have broadcast-compatible
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/// types.
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bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b);
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// Returns true if every dimension of the attribute is 1 except the last one.
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bool IsDimensionsDegenerateExceptLastOne(mlir::TypedAttr val);
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// Returns true if every element is 1 except the last one.
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bool IsDimensionsDegenerateExceptLastOne(ArrayRef<int64_t> elements_shape);
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} // end namespace TF
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} // end namespace mlir
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#endif // TENSORFLOW_COMPILER_MLIR_UTILS_VALIDATORS_H_
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