「tfl」方言

TensorFlow Liteの方言。

この方言はTensorFlow Liteの演算に対応します。

不変量:

• すべての値はテンソル型です（特に、スカラーはゼロ次元テンソルを使用して表現されます）。

業務

tfl.abs (TFL::AbsOp)

絶対値演算子

テンソルxが与えられた場合、この操作はxの各要素の絶対値を含むテンソルを返します。たとえば、x が入力要素で y が出力要素の場合、この操作は以下を計算します。 \(y = |x|\)。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult 、 SameOperandsAndResultShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.add (TFL::AddOp)

加算演算子

要素ごとの加算演算。

特性: ::mlir::OpTrait::TFLRuntimeOpTrait 、 AlwaysSpeculatableImplTrait 、 Commutative 、 QuantizableResult 、 ResultsBroadcastableShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.add_n (TFL::AddNOp)

_n演算子を追加する

入力されたすべてのテンソルを要素ごとに加算します。

特性: AlwaysSpeculatableImplTrait 、 Commutative

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.arg_max (TFL::ArgMaxOp)

ArgMax演算子

テンソルの各次元において、最も大きな値を持つインデックスを返します。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.arg_min (TFL::ArgMinOp)

ArgMin オペレーター

テンソルの各次元の中で最小値を持つインデックスを返します。 a = [1, 10, 26.9, 2.8, 166.32, 62.3] b = tf.math.argmin(input = a) c = tf.keras.backend.eval(b)

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.assign_variable (TFL::AssignVariableOp)

変数に新しい値を代入します。

この操作に制御依存するすべての ReadVariableOp は、この値、または変数のそれ以降の新しい値を返すことが保証されます。

インターフェース: TflRuntimeVerifyOpInterface

オペランド:

tfl.atan2 (TFL::Atan2Op)

Atan2 操作

「atan2」演算は、引数の符号を考慮しながら、y/xの逆正接を要素ごとに計算します。

特性: AlwaysSpeculatableImplTrait 、 SameOperandsAndResultType

インターフェース: ConditionallySpeculatable 、 InferTypeOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.average_pool_2d (TFL::AveragePool2DOp)

_平均プール2D オペレーター

入力に対して平均プーリング演算を実行します。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.basic_lstm (TFL::BasicLSTMOp)

基本的なLSTM演算子

基本的なLSTMセル演算子。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.batch_matmul (TFL::BatchMatMulOp)

バッチ行列乗算演算子

入力に対してバッチ処理による行列乗算を実行します。TensorFlow BatchMatMulV2 の規則に従い、バッチ次元における未知の次元とブロードキャストをサポートします。

Inputs:
  `inputs[0]`: required: input LHS
  `inputs[1]`: required: input RHS
  `adjoint_lhs`: optional: Transpose LHS (default false)
  `adjoint_rhs`: optional: Transpose RHS (default false)

特性: ::mlir::OpTrait::TFLRuntimeOpTrait 、 AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェース: ConditionallySpeculatable 、 DynamicRangeQuantizedOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.batch_to_space_nd (TFL::BatchToSpaceNdOp)

BatchToSpaceNd オペレーター

この操作は、「バッチ」次元0を空間次元に再構成します。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.bidirectional_sequence_lstm (TFL::BidirectionalSequenceLSTMOp)

双方向シーケンスLSTM演算子

双方向LSTMは、基本的に2つのLSTMから構成され、一方は順方向に、もう一方は逆方向に動作します。そして、出力は2つのLSTMを連結したものです。

特性: QuantizableResult

インターフェース: DynamicRangeQuantizedOpInterface 、 TFL_StatefulOp 、 TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.bitcast (TFL::BitcastOp)

ビットキャスト演算子

テンソルをある型から別の型にビットキャストします。

特性: AlwaysSpeculatableImplTrait

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.bitwise_xor (TFL::BitwiseXorOp)

ビットごとの XOR 演算子

要素ごとに、 lhsとrhsのビットごとの XOR を計算します。

特性: AlwaysSpeculatableImplTrait 、 Commutative 、 ResultsBroadcastableShape 、 SameOperandsAndResultElementType

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.broadcast_args (TFL::BroadcastArgsOp)

ブロードキャストを使用して、s0 op s1 の形状を返します。

形状を表すテンソルs0とs1が与えられたとき、ブロードキャストされた形状r0を計算します。s0 s0 s1 、 r0はすべて整数ベクトルです。

特性: AlwaysSpeculatableImplTrait

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.broadcast_to (TFL::BroadcastToOp)

互換性のある形状の配列をブロードキャストします。

ブロードキャストとは、配列の形状を算術演算に適したものにするプロセスです。2つの形状は、各次元ペアについて、それらが等しいか、どちらか一方が1である場合に互換性があるとみなされます。テンソルを形状にブロードキャストしようとすると、まず末尾の次元から始まり、順に処理が進められます。

例えば、

x = tf.constant([1, 2, 3]) y = tf.broadcast_to(x, [3, 3]) print(y) tf.Tensor( [[1 2 3] [1 2 3] [1 2 3]], shape=(3, 3), dtype=int32)

上記の例では、形状が[1, 3]の入力テンソルが、形状が[3, 3]の出力テンソルにブロードキャストされます。

テンソルとスカラーの乗算など、ブロードキャスト演算を行う場合、ブロードキャストされたテンソルは実体化されないため、（通常）時間的または空間的な利点が得られます。

しかし、 broadcast_toにはそのような利点はありません。新しく作成されたテンソルは、ブロードキャストされた形状のメモリをすべて占有します。（ただし、グラフのコンテキストでは、 broadcast_to後続の操作と融合され、最適化によって削除される可能性があります。）

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.bucketize (TFL::BucketizeOp)

「境界」に基づいて「入力」をバケットに分類します。

例：

入力がboundaries = [0, 10, 100]でinput = [[-5, 10000][150, 10][5, 100]]の場合、出力はoutput = [[0, 3][3, 2][1, 3]]になります。

特性: AlwaysSpeculatableImplTrait 、 SameOperandsAndResultShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.call_once (TFL::CallOnceOp)

初期化関数を呼び出す

この操作は、tf の保存済みモデル方言におけるセッション初期化子に対して、指定された初期化関数を呼び出します。

インターフェース: TflRuntimeVerifyOpInterface

属性:

tfl.cast (TFL::CastOp)

キャストオペレーター

入力型を出力型にキャストします。

特性: AlwaysSpeculatableImplTrait 、 SameOperandsAndResultShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.ceil (TFL::CeilOp)

天井オペレーター

入力された要素の要素ごとの切り上げ値を返します。

特性: AlwaysSpeculatableImplTrait 、 InferTensorType 、 TF::SameOperandsAndResultTypeResolveRef

インターフェース: ConditionallySpeculatable 、 InferShapedTypeOpInterface 、 InferTypeOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.complex_abs (TFL::ComplexAbsOp)

テンソルの複素絶対値を計算します。

複素数のテンソルxが与えられた場合、この操作は、 xの各要素の絶対値であるfloat型またはdoubleのテンソルを返します。x のすべての要素は、 xの形式の複素数である必要があります。 \(a + bj\)絶対値は次のように計算されます。 \( \sqrt{a^2 + b^2}\)。

特性: AlwaysSpeculatableImplTrait 、 SameOperandsAndResultShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.concatenation (TFL::ConcatenationOp)

連結演算子

1次元に沿ってテンソルを連結する

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.control_node (TFL::ControlNodeOp)

TFL.control_node操作は、制御エッジを接続するために、単一ブロック操作をラップします。

これは、領域をラップし、制御依存関係をそれらにアタッチするために使用されます。通常、これはフラットバッファモデルを出力する前の最後のステップのいずれかで行われ、操作の固定順序に依存する最適化（再マテリアライズなど）を有効にするために使用されます。フラットバッファエクスポーターは、ラップされた領域をアンラップし、生成されたモデルにメタデータを注釈付けすることで、実行時の順序変更が制御依存関係によって指定された順序を尊重するようにします。

特性: HasParent<mlir::func::FuncOp> 、 RecursiveMemoryEffects 、 SingleBlockImplicitTerminator<YieldOp> 、 SingleBlock

オペランド:

結果：

tfl.conv_2d (TFL::Conv2DOp)

畳み込み演算子

入力に対して畳み込み演算を実行します。

入力: inputs[0] : 必須: 入力活性化テンソルinputs[1] : 必須: フィルタ重みテンソルinputs[2] : オプション: バイアステンソル

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult 、 TFL::AccumulatorUniformScale<2, 0, 1> 、 TFL::AffineOpCoefficient<0, 1>

インターフェース: AffineQuantizedOpInterface 、 ConditionallySpeculatable 、 DynamicRangeQuantizedOpInterface 、 InferTypeOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 RequiresQuantizedBiasInterface 、 TFL_SparseOp 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.conv_3d (TFL::Conv3DOp)

畳み込み3D演算子

3D入力に対して畳み込み演算を実行します。入力: inputs[0] : 必須: 入力活性化テンソルinputs[1] : 必須: フィルタ重みテンソルinputs[2] : オプション: バイアステンソル

特性: AlwaysSpeculatableImplTrait 、 TFL::AccumulatorUniformScale<2, 0, 1>

インターフェース: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 RequiresQuantizedBiasInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.conv_3d_transpose (TFL::Conv3DTransposeOp)

転置畳み込み3D演算子

3D入力に対して転置畳み込み演算を実行します。入力: inputs[0] : 必須: 出力テンソルの形状inputs[1] : 必須: フィルタ重みテンソルinputs[2] : 必須: 入力活性化テンソルinputs[3] : オプション: バイアステンソル

特性: AlwaysSpeculatableImplTrait 、 TFL::AccumulatorUniformScale<3, 1, 2>

インターフェース: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 RequiresQuantizedBiasInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.cos (TFL::CosOp)

コサイン演算子

入力の要素ごとのコサインを計算します

特性: AlwaysSpeculatableImplTrait 、 InferTensorType 、 TF::SameOperandsAndResultTypeResolveRef

インターフェース: ConditionallySpeculatable 、 InferShapedTypeOpInterface 、 InferTypeOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.cumsum (TFL::CumsumOp)

カムサムオペレーター

軸に沿ったテンソルxの累積和を計算します。

特性: AlwaysSpeculatableImplTrait

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.custom (TFL::CustomOp)

カスタムオペレーション

TFLiteのカスタムオペレーションに使用できる汎用的なオペレーションです。

input: 元の操作の入力のリスト。 custom_code: この操作が正確にどれであるかを識別するために使用される文字列。flatbuffer の operator_codes.custom_code に対応します。 custom_option: 操作の属性をバイト形式で保存するためのホルダー。 output: 元の操作の出力のリスト。

インターフェース: TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.custom_tf (TFL::CustomTfOp)

TFカスタムオペレーション用のラッパーオペレーション。

任意のカスタム TF オペレーションをラップするオペレーションです。これには、カスタム オペレーション定義を使用して定義されたオペレーション、または TF 方言で定義されていないリンクされたオペレーションが含まれます。このオペレーションは、カスタムオペレーションをリージョン内にラップするだけです。注 #1、このオペレーションには CustomOp を使用して定義された TF Lite カスタムオペレーションは含まれません。注 #2、このオペレーションはコンバータ内部の表現であり、モデルが Flatbuffer にエクスポートされるときに公開/エクスポートされません。

特性: IsolatedFromAbove 、 RecursiveMemoryEffects 、 SingleBlockImplicitTerminator<YieldOp> 、 SingleBlock

インターフェース: InferTypeOpInterface 、 TflRuntimeVerifyOpInterface

オペランド:

結果：

tfl.densify (TFL::DensifyOp)

密化演算子

疎テンソルを密テンソル形式に変換します。

特性: AlwaysSpeculatableImplTrait

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.depth_to_space (TFL::DepthToSpaceOp)

DepthToSpace オペレーター

深度データを空間データのブロックに再配置します。これは SpaceToDepth の逆変換です。具体的には、この操作では入力テンソルのコピーを出力します。 depth次元の値は空間ブロック単位でheightとwidth次元に移動されます。属性block_size入力ブロックのサイズとデータの移動方法を示します。

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 SameOperandsAndResultsScale 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.depthwise_conv_2d (TFL::DepthwiseConv2DOp)

深層分離畳み込み演算子

入力に対して畳み込み演算を実行します。

入力: inputs[0] : 必須: 入力活性化テンソルinputs[1] : 必須: フィルタ重みテンソルinputs[2] : オプション: バイアステンソル

特性: AlwaysSpeculatableImplTrait 、 QuantizableResult 、 TFL::AccumulatorUniformScale<2, 0, 1> 、 TFL::AffineOpCoefficient<3, 1>

インターフェース: AffineQuantizedOpInterface 、 ConditionallySpeculatable 、 DynamicRangeQuantizedOpInterface 、 NoMemoryEffect (MemoryEffectOpInterface) 、 RequiresQuantizedBiasInterface 、 TFL_SparseOp 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.dequantize (TFL::DequantizeOp)

逆量子化演算子

量子化された整数配列を、量子化パラメータに従って浮動小数点数に変換します。

インターフェース: NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.dilate (TFL::DilateOp)

膨張演算子

既存の要素の間に新しい要素を追加することで、テンソルを拡張します。

特性: AlwaysSpeculatableImplTrait

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

オペランド:

結果：

tfl.div (TFL::DivOp)

分割オペレーター

要素ごとの除算演算。

特性: ::mlir::OpTrait::TFLRuntimeOpTrait 、 AlwaysSpeculatableImplTrait 、 QuantizableResult 、 ResultsBroadcastableShape

インターフェイス: ConditionallySpeculatable 、 NoMemoryEffect (MemoryEffectOpInterface) 、 TflArithmeticCountOpInterface 、 TflRuntimeVerifyOpInterface

効果: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.dynamic_update_slice (TFL::DynamicUpdateSliceOp)

DynamicUpdateSlice。

XLA DynamicUpdateSlice と同じセマンティクスを持つ DynamicUpdateSlice 操作。入力配列オペランドの値である結果を生成します。この結果には、start_indices で上書きされたスライス更新が含まれます。

See https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.elu (TFL::EluOp)

Exponential Linear Unit operator

Computes the exponential linear f(x) -> exp(x) - 1 for x < 0, x for x >= 0. element-wise.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.embedding_lookup (TFL::EmbeddingLookupOp)

Embedding lookup operator

Looks up ids in a list of embedding tensors.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: AffineQuantizedOpInterface , ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.equal (TFL::EqualOp)

Equal operator

Returns the truth element of x == y element-wise

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , Commutative , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.exp (TFL::ExpOp)

Natural exponentiation operator

Performs element-wise natural exponentiation operation on input.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.expand_dims (TFL::ExpandDimsOp)

Inserts a dimension of 1 into a tensor's shape.

Given a tensor input , this operation inserts a dimension of 1 at the dimension index axis of input 's shape. The dimension index axis starts at zero; if you specify a negative number for axis it is counted backward from the end.

This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape [height, width, channels] , you can make it a batch of 1 image with expand_dims(image, 0) , which will make the shape [1, height, width, channels] .

その他の例：

# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]

# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]

This operation requires that:

-1-input.dims() <= dim <= input.dims()

This operation is related to squeeze() , which removes dimensions of size 1.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.external_const (TFL::ExternalConstOp)

External const op.

External const op that can hold :

• buffer_index which points to a constant in the flatbuffer.
• external_buffer which contains metadata for external buffer outside flatbuffer.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.fake_quant (TFL::FakeQuantOp)

FakeQuant operator

Fake-quantize the 'inputs' tensor of type float via float scalars min and max to 'outputs' tensor of same shape as inputs.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.fill (TFL::FillOp)

Fill the tensor with given value.

Fill the tensor with given value.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.floor (TFL::FloorOp)

Floor operator

Returns element-wise floor value of the input.

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.floor_div (TFL::FloorDivOp)

Floor div operator

Element-wise floor div operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.floor_mod (TFL::FloorModOp)

Division reminder

Element-wise division reminder operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.fully_connected (TFL::FullyConnectedOp)

Fully connected op

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , TFL::AccumulatorUniformScale<2, 0, 1> , TFL::AffineOpCoefficient<0, 1>

Interfaces: AffineQuantizedOpInterface , ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , RequiresQuantizedBiasInterface , TFL_SparseOp , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.gather (TFL::GatherOp)

Gather operator

Gather slices from params axis axis according to indices .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.gather_nd (TFL::GatherNdOp)

_Gather nd operator

Gather slices from params into a Tensor with shape specified by indices .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.gelu (TFL::GeluOp)

GELU activation function.

Computes GELU activation function element-wise.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.greater (TFL::GreaterOp)

Greater operator

Element-wise greater operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.greater_equal (TFL::GreaterEqualOp)

_Greater equal operator

Element-wise greater_equal operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.hard_swish (TFL::HardSwishOp)

Hardswish activation function.

Computes hard-swish activation function f(x) -> (x * relu6(x+3))/6 element-wise.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.hashtable (TFL::HashtableOp)

Creates a non-initialized hash table.

This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.

Interfaces: TflRuntimeVerifyOpInterface

属性:

結果：

tfl.hashtable_find (TFL::HashtableFindOp)

Looks up keys in a table, outputs the corresponding values.

The tensor keys must of the same type as the keys of the table. The output values is of the type of the table values.

The scalar default_value is the value output for keys not present in the table. It must also be of the same type as the table values.

Interfaces: TflRuntimeVerifyOpInterface

オペランド:

結果：

tfl.hashtable_import (TFL::HashtableImportOp)

Replaces the contents of the table with the specified keys and values.

The tensor keys must be of the same type as the keys of the table. The tensor values must be of the type of the table values.

Interfaces: TflRuntimeVerifyOpInterface

オペランド:

tfl.hashtable_size (TFL::HashtableSizeOp)

Computes the number of elements in the given table.

Interfaces: TflRuntimeVerifyOpInterface

オペランド:

結果：

tfl.if (TFL::IfOp)

If-then-else operation

The tfl.if operation represents an if-then-else construct for conditionally executing two regions of code. The operand to an if operation is a boolean value. For example:

tfl.if %b  {
  ...
} else {
  ...
}

tfl.if may also return results that are defined in its regions. The values defined are determined by which execution path is taken.

例：

%x, %y = tfl.if %b -> (tensor<f32>, tensor<f32>) {
  %x_true = ...
  %y_true = ...
  tfl.yield %x_true, %y_true : tensor<f32>, tensor<f32>
} else {
  %x_false = ...
  %y_false = ...
  tfl.yield %x_false, %y_false : tensor<f32>, tensor<f32>
}

tfl.if regions are always terminated with "tfl.yield". If "tfl.if" defines no values, the "tfl.yield" can be left out, and will be inserted implicitly. Otherwise, it must be explicit. Also, if "tfl.if" defines one or more values, the 'else' block cannot be omitted.

例：

tfl.if %b  {
  ...
}

Traits: NoRegionArguments , RecursiveMemoryEffects , SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Interfaces: RegionBranchOpInterface , TflRuntimeVerifyOpInterface

オペランド:

結果：

tfl.imag (TFL::ImagOp)

Returns the imaginary part of a complex number.

Given a tensor input of complex numbers, this operation returns a tensor of type float that is the imaginary part of each element in input . All elements in input must be complex numbers of the form \(a + bj\), where a is the real part and b is the imaginary part returned by this operation.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.l2_normalization (TFL::L2NormalizationOp)

L2 Normalize Operator

L2Normalization Op

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , FixedOutputRangeInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.leaky_relu (TFL::LeakyReluOp)

Leaky Relu operator

Element-wise Leaky ReLU operator x -> x >= 0 ? x : (alpha * x)

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.less (TFL::LessOp)

Less operator

Element-wise less operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.less_equal (TFL::LessEqualOp)

_Less equal operator

Element-wise less_equal operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.local_response_normalization (TFL::LocalResponseNormalizationOp)

Local Response Normalization.

The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius . In detail,

sqr_sum[a, b, c, d] =
    sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta

For details, see Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012) .

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.log (TFL::LogOp)

Natural logarithm operator

Performs element-wise natural logarithm operation on input.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.log_softmax (TFL::LogSoftmaxOp)

Log softmax operator

Computes element-wise log softmax activations with the following formula

input - log(reduce_sum(exp(input), dim))

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , FixedOutputRangeInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.logical_and (TFL::LogicalAndOp)

論理AND演算子

Element-wise logical AND operation.

Traits: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.logical_not (TFL::LogicalNotOp)

論理否定演算子

Element-wise logical NOT operation.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.logical_or (TFL::LogicalOrOp)

論理OR演算子

Element-wise logical OR operation.

Traits: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.logistic (TFL::LogisticOp)

Logistic operator

Computes element-wise Sigmoid of input

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , FixedOutputRangeInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.lstm (TFL::LSTMOp)

The full lstm operator

Long short-term memory unit (LSTM) recurrent network layer. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. 'Long Short-Term Memory'. Neural Computation, 9(8):1735-1780, 1997. The peephole implementation is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. 'Long short-term memory recurrent neural network architectures for large scale acoustic modeling.' INTERSPEECH, 2014. The coupling of input and forget gate (CIFG) is based on: http://arxiv.org/pdf/1503.04069.pdf Greff et al. 'LSTM: A Search Space Odyssey' The layer normalization is based on: https://arxiv.org/pdf/1607.06450.pdf Ba et al. 'Layer Normalization'

Traits: QuantizableResult

Interfaces: DynamicRangeQuantizedOpInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.matrix_diag (TFL::MatrixDiagOp)

Returns a tensor with the provided diagonal and everything else padded with zeros.

Given a diagonal, returns a tensor with the diagonal and everything else padded with zeros. Assume diagonal has k dimensions [I, J, K, ..., N] , then the output is a tensor of rank k+1 with dimensions [I, J, K, ..., N, N] where: output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n].

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.matrix_set_diag (TFL::MatrixSetDiagOp)

Returns a batched matrix tensor with new batched diagonal values.

Given input and diagonal , this operation returns a tensor with the same shape and values as input , except for the main diagonal of the innermost matrices. These will be overwritten by the values in diagonal .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.max_pool_2d (TFL::MaxPool2DOp)

Max Pool 2D op

Performs max pool 2D on input.

Inputs: inputs[0] : required: the input tensor

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.maximum (TFL::MaximumOp)

Max operator

Element-wise max operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , Commutative , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.mean (TFL::MeanOp)

Mean operator

Computes the mean of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.minimum (TFL::MinimumOp)

Min operator

Element-wise min operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , Commutative , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.mirror_pad (TFL::MirrorPadOp)

MirrorPad Operator. Pads a tensor with mirrored values.

This operation pads a input with mirrored values according to the paddings you specify. paddings is an integer tensor with shape [n, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many values to add before the contents of input in that dimension, and paddings[D, 1] indicates how many values to add after the contents of input in that dimension.

Both paddings[D, 0] and paddings[D, 1] must be no greater than input.dim_size(D) (or input.dim_size(D) - 1) if copy_border is true (if false, respectively).

The padded size of each dimension D of the output is:

paddings(D, 0) + input.dim_size(D) + paddings(D, 1)

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.mul (TFL::MulOp)

乗算演算子

Element-wise multiplication operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , Commutative , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.multinomial (TFL::MultinomialOp)

Draws samples from a categorical distribution.

The generated values will have a categorical distribution based on the logits or unnormalized log-probabilities provided for all classes.

Interfaces: TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.neg (TFL::NegOp)

否定演算子

Computes element-wise negation of input

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.no_value (TFL::NoValueOp)

Constant representing no value.

No value constant op.

Traits: AlwaysSpeculatableImplTrait , ConstantLike

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.non_max_suppression_v4 (TFL::NonMaxSuppressionV4Op)

Greedily selects a subset of bounding boxes in descending order of score,

pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (ie, lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation . For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.non_max_suppression_v5 (TFL::NonMaxSuppressionV5Op)

Greedily selects a subset of bounding boxes in descending order of score,

pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (ie, lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation . For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices) This op also supports a Soft-NMS (with Gaussian weighting) mode (cf Bodla et al, https://arxiv.org/abs/1704.04503 ) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. To enable this Soft-NMS mode, set the soft_nms_sigma parameter to be larger than 0.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.not_equal (TFL::NotEqualOp)

_Not equal operator

Element-wise not_equal operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , Commutative , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.NumericVerify (TFL::NumericVerifyOp)

Verifies the numericals of the two operands

The NumericVerify op is a debugging op to verify the numericals of the two activations. It is a custom op in TFLite. If log_if_failed is true, the NumericVerify op calculates statistics on differences between float and quantized activations, output logs, set differences to the output tensors, and throws an error if errors above tolerance exist. If log_if_failed = false, then it doesn't care about errors.

Traits: QuantizableResult , SameOperandsShape

Interfaces: TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.one_hot (TFL::OneHotOp)

OneHot operator

Returns a one-hot tensor.The locations represented by indices in indices take value on_value , while all other locations take value off_value .

If the input indices is rank N , the output will have rank N+1 , The new axis is created at dimension axis (default: the new axis is appended at the end).

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.pack (TFL::PackOp)

Packs a list of tensors along a dimension into one tensor

Packs a list of values_count rank- R tensors into one rank- (R+1) tensor.

Packs the values_count tensors in values into a tensor with rank one higher than each tensor in values , by packing them along the axis dimension.

Given a list of tensors of shape (A, B, C) ;

if axis == 0 then the output tensor will have the shape (N, A, B, C) . if axis == 1 then the output tensor will have the shape (A, N, B, C) . Etc.

例えば：

# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]

This is the opposite of unpack .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.pad (TFL::PadOp)

Padding operator

This operation pads a input with zeros according to the paddings you specify. paddings is an integer tensor with shape [Dn, 2] , where n is the rank of input . For each dimension D of input , paddings[D, 0] indicates how many zeros to add before the contents of input in that dimension, and paddings[D, 1] indicates how many zeros to add after the contents of input in that dimension.

The padded size of each dimension D of the output is:

paddings(D, 0) + input.dim_size(D) + paddings(D, 1)

例えば：

# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.padv2 (TFL::PadV2Op)

Padding operator v2

This operation pads a input according to the paddings and constant_values you specify. paddings is an integer tensor with shape [Dn, 2] , where n is the rank of input . For each dimension D of input , paddings[D, 0] indicates how many zeros to add before the contents of input in that dimension, and paddings[D, 1] indicates how many zeros to add after the contents of input in that dimension. constant_values is a scalar tensor of the same type as input that indicates the value to use for padding input .

The padded size of each dimension D of the output is:

paddings(D, 0) + input.dim_size(D) + paddings(D, 1)

例えば：

# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.poly_call (TFL::PolyCallOp)

Poly call

Have multiple function bodies for the same computation. This allows a program compiler/interpreter to choose one of the available options to execute the program based on which one is most suitable for the target backend.

input: A list of input tensors whose types are T. output: A list of output tensors whose types are T.

call: Multiple regions, each of which encapsulates the same semantic computation but in different forms.

Traits: SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Interfaces: RegionBranchOpInterface

オペランド:

結果：

tfl.pow (TFL::PowOp)

Power operator

Element-wise power operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.prelu (TFL::PReluOp)

Parameterized Relu operator

Parameterized Relu operator x -> x >= 0 ? x : (alpha * x) where alpha is a trainable tensor. input and alpha should be the same size as input or be broadcastable.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape , TFL::AffineOpCoefficient<-1, 1>

Interfaces: AffineQuantizedOpInterface , ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.pseudo_const (TFL::ConstOp)

Constant pseudo op.

Represents a constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead.

The op is allowed to have all the same type of attributes as tf.Const does (eg, opaque TF attributes are allowed).

Traits: AlwaysSpeculatableImplTrait , ConstantLike , FirstAttrDerivedResultType , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.pseudo_qconst (TFL::QConstOp)

Quantized constant pseudo op

Represents a quantized constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. The quantization parameters are stored as a type attribute in this constant.

Traits: AlwaysSpeculatableImplTrait , FirstAttrDerivedResultType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.pseudo_sparse_const (TFL::SparseConstOp)

Sparse constant pseudo op.

Represents a sparse constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead.

Traits: AlwaysSpeculatableImplTrait , FirstAttrDerivedResultType , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.pseudo_sparse_qconst (TFL::SparseQConstOp)

Sparse quantized constant pseudo op

Represents a sparse quantized constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. The quantization parameters are stored as a type attribute in this constant.

Traits: AlwaysSpeculatableImplTrait , FirstAttrDerivedResultType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

結果：

tfl.quantize (TFL::QuantizeOp)

Quantize operator

Converts floating point tensors to quantized integer tensors according to the quantization parameters defined in the type attribute.

Traits: FirstAttrDerivedResultType , SameOperandsAndResultShape

Interfaces: NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.random_standard_normal (TFL::RandomStandardNormalOp)

Outputs random values from a normal distribution.

The generated values will have mean 0 and standard deviation 1.

Interfaces: TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.random_uniform (TFL::RandomUniformOp)

Outputs random values from a uniform distribution.

The generated values follow a uniform distribution in the range [0, 1) . The lower bound 0 is included in the range, while the upper bound 1 is excluded.

Interfaces: TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.range (TFL::RangeOp)

Range operator

Returns a 1D tensor defined by a sequence from start to limit with a given delta .

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.rank (TFL::RankOp)

Rank operator.

Returns the rank of a tensor.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.read_variable (TFL::ReadVariableOp)

Reads variable value.

Read variable data identified by 'resource_id'.

Interfaces: TflRuntimeVerifyOpInterface

オペランド:

結果：

tfl.real (TFL::RealOp)

Returns the real part of a complex number.

Given a tensor input of complex numbers, this operation returns a tensor of type float that is the real part of each element in input . All elements in input must be complex numbers of the form \(a + bj\), where a is the real part returned by this operation and b is the imaginary part.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.reduce_all (TFL::ReduceAllOp)

Computes the "logical and" of elements across dimensions of a tensor.

Reduces input along the dimensions given in axis . Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis . If keep_dims is true, the reduced dimensions are retained with length 1.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reduce_any (TFL::ReduceAnyOp)

Computes the "logical or" of elements across dimensions of a tensor.

Reduces input along the dimensions given in axis . Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis . If keep_dims is true, the reduced dimensions are retained with length 1.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reduce_max (TFL::ReduceMaxOp)

Max-reduction operator

Computes the max reduction along the specified axes

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reduce_min (TFL::ReduceMinOp)

Min-reduction operator

Computes the min reduction along the specified axes

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reduce_prod (TFL::ReduceProdOp)

Prod-reduction operator

Computes the product along the specified axes

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.relu (TFL::ReluOp)

Relu operator

Element-wise Relu operator x -> max(0, x)

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.relu6 (TFL::Relu6Op)

Relu6 operator

Element-wise Relu6 operator x -> max(0, min(6, x))

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.relu_0_to_1 (TFL::Relu0To1Op)

Relu0To1 operator

Element-wise Relu0To1 operator x -> max(0, min(1, x))

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.relu_n1_to_1 (TFL::Relu1Op)

Relu1 operator

Element-wise Relu1 operator x -> max(-1, min(1, x))

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.reshape (TFL::ReshapeOp)

Reshape operator

Produces a tensor with the same values but different static shape defined by the output type.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.resize_bilinear (TFL::ResizeBilinearOp)

ResizeBilinear Op

Resize images to size using bilinear interpolation.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.resize_nearest_neighbor (TFL::ResizeNearestNeighborOp)

ResizeNearestNeighbor Op

Resize images to size using nearest neighbor interpolation.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reverse_sequence (TFL::ReverseSequenceOp)

Reverses variable length slices.

This op first slices input along the dimension batch_dim , and for each slice i , reverses the first seq_lengths[i] elements along the dimension seq_dim .

The elements of seq_lengths must obey seq_lengths[i] <= input.dims[seq_dim] , and seq_lengths must be a vector of length input.dims[batch_dim] .

The output slice i along dimension batch_dim is then given by input slice i , with the first seq_lengths[i] slices along dimension seq_dim reversed.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.reverse_v2 (TFL::ReverseV2Op)

ReverseV2 Operator

Reverses specific dimensions of a tensor.

Given a tensor, and a int32/int64 tensor axis representing the set of dimensions of tensor to reverse. This operation reverses each dimension i for which there exists j st axis[j] == i.

Args: tensor: A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, float32, bool Up to 8-D.

axis: A Tensor. Must be one of the following types: int32, int64. with only 1 element which is the axis index. TODO: Add support for multiple elements.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.rfft2d (TFL::RFFT2dOp)

2D real-valued fast Fourier transform.

Computes the 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of input .

Since the DFT of a real signal is Hermitian-symmetric, RFFT2D only returns the fft_length / 2 + 1 unique components of the FFT for the inner-most dimension of output : the zero-frequency term, followed by the fft_length / 2 positive-frequency terms.

Along each axis RFFT2D is computed on, if fft_length is smaller than the corresponding dimension of input , the dimension is cropped. If it is larger, the dimension is padded with zeros.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.right_shift (TFL::RightShiftOp)

Right Shift operator

Elementwise computes the bitwise right-shift of lhs by rhs .

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.round (TFL::RoundOp)

Round operator

Rounds the values of a tensor to the nearest integer, element-wise.

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.rsqrt (TFL::RsqrtOp)

Reciprocal of square root operator

Computes element-wise reverse square root of input

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.scatter_nd (TFL::ScatterNdOp)

_Scatter nd operator

Scatter updates into a new tensor according to indices

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.segment_sum (TFL::SegmentSumOp)

SegmentSum operator

Computes the sum along segments of a tensor.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.select (TFL::SelectOp)

演算子を選択

Select values of 'x' if the corresponding value of 'condition' is true or the value of 'y' if false. There are valid condition input sizes:

1. Either the same shape (in which case the select is elementwise), or
2. condition must be Rank 1 and match over the first dimension.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.select_v2 (TFL::SelectV2Op)

SelectV2 operator

Select values of 'x' if the corresponding value of 'condition' is true or the value of 'y' if false. There are valid condition input sizes:

1. Either the same shape (in which case the select is elementwise), or
2. Broadcastable shapes between 'condition', 'x' and 'y'.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.shape (TFL::ShapeOp)

Shape operator

Returns the shape of a tensor.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.sign (TFL::SignOp)

Sign operation

Returns NaN if x is NaN, 0 if x is 0, -1 if x < 0 and 1 if x > 0.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.sin (TFL::SinOp)

Sine operator

Computes element-wise Sine of input

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.slice (TFL::SliceOp)

Return a slice from 'input'.

The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'.

begin is zero-based; size is one-based. If size[i] is -1, all remaining elements in dimension i are included in the slice. In other words, this is equivalent to setting: size[i] = input.dim_size(i) - begin[i]

Requirements : 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.softmax (TFL::SoftmaxOp)

Softmax operator

Computes element-wise softmax activations with the following formula

exp(input * beta) / tf.reduce_sum(exp(input * beta), dim)

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , FixedOutputRangeInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.space_to_batch_nd (TFL::SpaceToBatchNdOp)

SpaceToBatchNd operator

This operation reshapes space dimensions into the "batch" dimension 0

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.space_to_depth (TFL::SpaceToDepthOp)

SpaceToDepth operator

Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. block_size indicates the input block size.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.sparse_to_dense (TFL::SparseToDenseOp)

Converts a sparse representation into a dense tensor.

Builds an array dense with shape output_shape such that

# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)

# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]

# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]

All other values in dense are set to default_value . If sparse_values is a scalar, all sparse indices are set to this single value.

Indices should be sorted in lexicographic order, and indices must not contain any repeats. If validate_indices is true, these properties are checked during execution.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.split (TFL::SplitOp)

Splits a tensor into num_split tensors along one dimension.

Splits the value tensor along split_dim into a number of sub-tensors with same shape as the original one, except for split_dim . Same as tf.Split.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.split_v (TFL::SplitVOp)

Splits a tensor into num_split tensors along one dimension.

Splits the value tensor along split_dim into a number of sub-tensors with same shape as the original one, except for split_dim . The grouping of the resultant sub-tensors is decided by size-splits . Same as tf.SplitV.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.sqrt (TFL::SqrtOp)

Square root operator

Computes element-wise Square root of input

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.square (TFL::SquareOp)

Square operator

Computes element-wise Square of input

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.squared_difference (TFL::SquaredDifferenceOp)

Squared difference operator

Element-wise squared difference operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.squeeze (TFL::SqueezeOp)

Removes dimensions of size 1 from the shape of a tensor.

Given a tensor input , this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying squeeze_dims .

例えば：

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]

Or, to remove specific size 1 dimensions:

# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.strided_slice (TFL::StridedSliceOp)

StridedSlice Op

Return a strided slice from input .

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.sub (TFL::SubOp)

Subtraction operator

Element-wise subtraction operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.sum (TFL::SumOp)

和演算子

Computes the sum reduction along the specified axes

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

属性:

オペランド:

結果：

tfl.svdf (TFL::SVDFOp)

Single value decomposition filter operator

The SVDF op is a decomposition of a densely connected op into low rank filters. For details: https://research.google.com/pubs/pub43813.html https://arxiv.org/abs/1812.02802

Traits: QuantizableResult , TFL::AccumulatorUniformScale<3, 2, 4>

Interfaces: DynamicRangeQuantizedOpInterface , RequiresQuantizedBiasInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

属性:

オペランド:

結果：

tfl.tanh (TFL::TanhOp)

Hyperbolic tangent operator

Computes element-wise Hyperbolic tangent of input

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , FixedOutputRangeInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.tile (TFL::TileOp)

Tile operator.

Constructs a tensor by tiling a given tensor.

This operation creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) * multiples[i] elements, and the values of input are replicated multiples[i] times along the 'i'th dimension. For example, tiling [abcd] by [2] produces [abcdabcd].

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.topk_v2 (TFL::TopKV2Op)

TopK operator

Returns the top k largest element along each last dimensional slice of input and the indices of values within the last dimension of the input tensor.

Results are always sorted in the descending order.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.transpose (TFL::TransposeOp)

Transpose operator

Returns the Transpose of x

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , SameOperandsAndResultsScale , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

オペランド:

結果：

tfl.transpose_conv (TFL::TransposeConvOp)

Transpose convolution operator

Performs transpose convolution operation on input.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , TFL::AccumulatorUniformScale<3, 1, 2> , TFL::AffineOpCoefficient<0, 1>

Interfaces: AffineQuantizedOpInterface , ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , RequiresQuantizedBiasInterface , TFL_SparseOp , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

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