Dialekt TensorFlow Lite.
Ten dialekt odwzorowuje operacje TensorFlow Lite.
niezmienniki:
- Wszystkie wartości są typu Tensor (w szczególności skalary są reprezentowane za pomocą tensorów zerowymiarowych);
Definicja operacji
tfl.abs (TFL::AbsOp)
Operator wartości bezwzględnej
Biorąc pod uwagę tensor x , ta operacja zwraca tensor zawierający wartość bezwzględną każdego elementu w x . Na przykład, jeśli x jest elementem wejściowym, a y jest elementem wyjściowym, ta operacja oblicza \(y = |x|\).
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult, SameOperandsAndResultShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
x | tensor 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby zmiennoprzecinkowej lub wartości typu QI8 lub QI16 |
Wyniki:
| Wynik | Opis |
|---|---|
y | tensor 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby zmiennoprzecinkowej lub wartości typu QI8 lub QI16 |
tfl.add (TFL::AddOp)
Operator dodawania
Operacja dodawania elementów.
Cechy: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, Commutative, QuantizableResult, ResultsBroadcastableShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
Argumenty:
| Operand | Opis |
|---|---|
lhs | tensor 32-bitowej liczby zmiennoprzecinkowej lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub wartości typu QI16 |
rhs | tensor 32-bitowej liczby zmiennoprzecinkowej lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub wartości typu QI16 |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub wartości typu QI16 |
tfl.add_n (TFL::AddNOp)
_Dodaj operatora n
Dodaje wszystkie tensory wejściowe po elementach.
Cechy: AlwaysSpeculatableImplTrait, Commutative
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
inputs | tensor dowolnych wartości typu |
Wyniki:
| Wynik | Opis |
|---|---|
sum | tensor 32-bitowych wartości zmiennoprzecinkowych lub 32-bitowych liczb całkowitych bez znaku |
tfl.arg_max (TFL::ArgMaxOp)
operatora ArgMax
Zwraca indeks o największej wartości spośród wymiarów tensora.
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
output_type | ::mlir::Atrybut | atrybut pochodny |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 1-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub wartości typu QUI8 |
dim | tensor 32/64-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32/64-bitowych liczb całkowitych bez znaku |
tfl.arg_min (TFL::ArgMinOp)
Operator ArgMin
Zwraca indeks o najmniejszej wartości spośród wymiarów tensora. a = [1, 10, 26,9, 2,8, 166,32, 62,3] b = tf.math.argmin(input = a) c = tf.keras.backend.eval(b)
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
output_type | ::mlir::Atrybut | atrybut pochodny |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 1-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub wartości typu QUI8 |
dim | tensor 32/64-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32/64-bitowych liczb całkowitych bez znaku |
tfl.assign_variable (TFL::AssignVariableOp)
Przypisuje nową wartość do zmiennej.
Każda operacja ReadVariableOp z zależnością sterowania od tej operacji gwarantuje zwrócenie tej wartości lub późniejszej nowszej wartości zmiennej.
Interfejsy: TflRuntimeVerifyOpInterface
Argumenty:
| Operand | Opis |
|---|---|
resource_id | tensor wartości zasobów |
value | tensor 32-bitowej liczby zmiennoprzecinkowej lub 64-bitowej zmiennoprzecinkowej lub 1-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu QI16 lub typ złożony z 32-bitowymi elementami zmiennoprzecinkowymi lub typ złożony z 64-bitowymi wartościami elementów zmiennoprzecinkowych |
tfl.atan2 (TFL::Atan2Op)
Operacja Atan2
Operacja „atan2” oblicza arcus tangens y/x elementowo, uwzględniając znaki argumentów.
Cechy: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, SameOperandsAndResultShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
y | tensor 32-bitowych wartości zmiennoprzecinkowych lub 64-bitowych wartości zmiennoprzecinkowych |
x | tensor 32-bitowych wartości zmiennoprzecinkowych lub 64-bitowych wartości zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub 64-bitowych wartości zmiennoprzecinkowych |
tfl.average_pool_2d (TFL::AveragePool2DOp)
_Average_pool operator 2d
Wykonuje operację uśredniania puli na wejściu.
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
filter_height | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
filter_width | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
padding | ::mlir::StringAttr | atrybut ciągu, którego wartość to SAME lub VALID |
stride_h | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_w | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
tfl.basic_lstm (TFL::BasicLSTMop)
Podstawowy operator lstm
podstawowy operator komórki LSTM.
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
cell_clip | ::mlir::FloatAttr | 32-bitowy atrybut zmiennoprzecinkowy, którego wartość nie jest ujemna |
proj_clip | ::mlir::FloatAttr | 32-bitowy atrybut zmiennoprzecinkowy, którego wartość nie jest ujemna |
kernel_type | ::mlir::TFL::LSTMKernelTypeAttr | lstm_kernel_type, którego wartość to mlir::TFL::LSTMKernelType::BASIC |
Argumenty:
| Operand | Opis |
|---|---|
data_input | tensor 32-bitowych wartości typu float lub QUI8 |
prev_activ_input | tensor 32-bitowych wartości typu float lub QUI8 |
weights_input | tensor 32-bitowych wartości typu float lub QUI8 |
biases_input | tensor 32-bitowych wartości typu float lub QI32 |
prev_state_input | tensor 32-bitowych wartości typu float lub QI16 |
Wyniki:
| Wynik | Opis |
|---|---|
activ_output | Tensor 2D o dowolnych wartościach typu |
state_output | Tensor 2D o dowolnych wartościach typu |
concat_temp | Tensor 2D o dowolnych wartościach typu |
activ_temp | Tensor 2D o dowolnych wartościach typu |
tfl.batch_matmul (TFL::BatchMatMulOp)
Operator mnożenia macierzy wsadowej
Wykonuje wsadowe mnożenie macierzy na wejściach. Jest zgodny z konwencjami TensorFlow BatchMatMulV2, z obsługą nieznanych wymiarów w wymiarach wsadowych i rozgłaszaniu.
Inputs:
`inputs[0]`: required: input LHS
`inputs[1]`: required: input RHS
`adjoint_lhs`: optional: Transpose LHS (default false)
`adjoint_lhs`: optional: Transpose LHS (default false)
Cechy: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
adj_x | ::mlir::BoolAttr | atrybut logiczny |
adj_y | ::mlir::BoolAttr | atrybut logiczny |
asymmetric_quantize_inputs | ::mlir::BoolAttr | atrybut logiczny |
Argumenty:
| Operand | Opis |
|---|---|
x | tensor 32-bitowej liczby zmiennoprzecinkowej lub typu QI8 lub typu QI16 lub 8-bitowych liczb całkowitych bez znaku |
y | tensor 32-bitowej liczby zmiennoprzecinkowej lub typu QI8 lub typu QI16 lub 8-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych liczb zmiennoprzecinkowych lub typu QI8 lub typu QI16 lub 32-bitowych liczb całkowitych bez znaku |
tfl.batch_to_space_nd (TFL::BatchToSpaceNdOp)
Operator BatchToSpaceNd
Ta operacja przekształca wymiar „wsadowy” 0 w wymiary przestrzenne.
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowej liczby zmiennoprzecinkowej lub 8-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub wartości typu QI16 |
block_shape | tensor 32-bitowych liczb całkowitych bez znaku |
indices | tensor 32-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub wartości typu QI16 |
tfl.bidirectional_sequence_lstm (TFL::BidirectionalSequenceLSTMop)
Dwukierunkowy operator sekwencji lstm
Dwukierunkowy lstm to zasadniczo dwa lstm, jeden biegnący do przodu, a drugi do tyłu. A wyjściem jest konkatenacja dwóch lstms.
Cechy: Quantizowalny wynik
Interfejsy: DynamicRangeQuantizedOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
cell_clip | ::mlir::FloatAttr | 32-bitowy atrybut zmiennoprzecinkowy, którego wartość nie jest ujemna |
proj_clip | ::mlir::FloatAttr | 32-bitowy atrybut zmiennoprzecinkowy, którego wartość nie jest ujemna |
merge_outputs | ::mlir::BoolAttr | atrybut logiczny |
time_major | ::mlir::BoolAttr | atrybut logiczny |
asymmetric_quantize_inputs | ::mlir::BoolAttr | atrybut logiczny |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_input_to_input_weights | tensor dowolnych wartości typu lub brak typu |
fw_input_to_forget_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_input_to_cell_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_input_to_output_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_recurrent_to_input_weights | tensor dowolnych wartości typu lub brak typu |
fw_recurrent_to_forget_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_recurrent_to_cell_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_recurrent_to_output_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
fw_cell_to_input_weights | tensor dowolnych wartości typu lub brak typu |
fw_cell_to_forget_weights | tensor dowolnych wartości typu lub brak typu |
fw_cell_to_output_weights | tensor dowolnych wartości typu lub brak typu |
fw_input_gate_bias | tensor dowolnych wartości typu lub brak typu |
fw_forget_gate_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
fw_cell_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
fw_output_gate_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
fw_projection_weights | tensor dowolnych wartości typu lub brak typu |
fw_projection_bias | tensor dowolnych wartości typu lub brak typu |
bw_input_to_input_weights | tensor dowolnych wartości typu lub brak typu |
bw_input_to_forget_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_input_to_cell_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_input_to_output_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_recurrent_to_input_weights | tensor dowolnych wartości typu lub brak typu |
bw_recurrent_to_forget_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_recurrent_to_cell_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_recurrent_to_output_weights | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
bw_cell_to_input_weights | tensor dowolnych wartości typu lub brak typu |
bw_cell_to_forget_weights | tensor dowolnych wartości typu lub brak typu |
bw_cell_to_output_weights | tensor dowolnych wartości typu lub brak typu |
bw_input_gate_bias | tensor dowolnych wartości typu lub brak typu |
bw_forget_gate_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
bw_cell_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
bw_output_gate_bias | tensor 32-bitowych wartości zmiennoprzecinkowych |
bw_projection_weights | tensor dowolnych wartości typu lub brak typu |
bw_projection_bias | tensor dowolnych wartości typu lub brak typu |
fw_input_activation_state | stanowy tensor |
fw_input_cell_state | stanowy tensor |
bw_input_activation_state | stanowy tensor |
bw_input_cell_state | stanowy tensor |
aux_input | tensor dowolnych wartości typu lub brak typu |
fw_aux_input_to_input_weights | tensor dowolnych wartości typu lub brak typu |
fw_aux_input_to_forget_weights | tensor dowolnych wartości typu lub brak typu |
fw_aux_input_to_cell_weights | tensor dowolnych wartości typu lub brak typu |
fw_aux_input_to_output_weights | tensor dowolnych wartości typu lub brak typu |
bw_aux_input_to_input_weights | tensor dowolnych wartości typu lub brak typu |
bw_aux_input_to_forget_weights | tensor dowolnych wartości typu lub brak typu |
bw_aux_input_to_cell_weights | tensor dowolnych wartości typu lub brak typu |
bw_aux_input_to_output_weights | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
fw_output | tensor dowolnych wartości typu |
bw_output | tensor dowolnych wartości typu |
tfl.bitcast (TFL::BitcastOp)
Operator bitcastu
Bitcastuje tensor z jednego typu na inny.
Cechy: AlwaysSpeculatableImplTrait
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor dowolnych wartości typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor dowolnych wartości typu |
tfl.bitwise_xor (TFL::BitwiseXorOp)
Bitowy operator Xor
Elementwise oblicza bitowe XOR dla lhs i rhs .
Cechy: AlwaysSpeculatableImplTrait, Commutative, SameOperandsAndResultElementType
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
lhs | tensor 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku |
rhs | tensor 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 8-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku |
tfl.broadcast_args (TFL::BroadcastArgsOp)
Zwróć kształt s0 op s1 z rozgłoszeniem.
Biorąc pod uwagę s0 i s1 , tensory reprezentujące kształty, obliczamy r0 , rozgłaszany kształt. s0 , s1 i r0 są wektorami całkowitymi.
Cechy: AlwaysSpeculatableImplTrait
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
s0 | tensor 32/64-bitowych liczb całkowitych bez znaku |
s1 | tensor 32/64-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
r0 | tensor 32/64-bitowych liczb całkowitych bez znaku |
tfl.broadcast_to (TFL::BroadcastToOp)
Rozgłaszaj tablicę dla zgodnego kształtu.
Broadcasting to proces tworzenia tablic, które mają kompatybilne kształty dla operacji arytmetycznych. Dwa kształty są zgodne, jeśli dla każdej pary wymiarów są one równe lub jeden z nich jest jeden. Próbując rozesłać tensor do kształtu, zaczyna się on od końcowych wymiarów i posuwa się naprzód.
Na przykład,
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]], kształt=(3, 3), dtype=int32)
W powyższym przykładzie wejściowy Tensor o kształcie [1, 3] jest rozgłaszany na wyjściowy Tensor o kształcie [3, 3] .
Podczas wykonywania rozgłaszanych operacji, takich jak mnożenie tensora przez skalar, rozgłaszanie (zwykle) przynosi pewne korzyści w czasie lub przestrzeni, ponieważ rozgłaszany tensor nigdy się nie materializuje.
Jednak broadcast_to nie niesie ze sobą żadnych takich korzyści. Nowo utworzony tensor przejmuje pełną pamięć transmitowanego kształtu. (W kontekście wykresu broadcast_to może zostać połączone z kolejną operacją, a następnie zoptymalizowane).
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 1-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub 8-bitowej liczby całkowitej bez znaku lub typu QUI8 lub 16-bitowej liczby całkowitej bez znaku lub typu QI16 lub 64-bitowego bez znaku typ całkowity lub złożony z 32-bitowymi wartościami elementów zmiennoprzecinkowych |
shape | tensor 32/64-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 1-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub 8-bitowej liczby całkowitej bez znaku lub typu QUI8 lub 16-bitowej liczby całkowitej bez znaku lub typu QI16 lub 64-bitowego bez znaku typ całkowity lub złożony z 32-bitowymi wartościami elementów zmiennoprzecinkowych |
tfl.bucketize (TFL::BucketizeOp)
Sortuje „dane wejściowe” na podstawie „granic”.
Przykład:
Jeśli wejściami są boundaries = [0, 10, 100] i input = [[-5, 10000][150, 10][5, 100]] , to output = [[0, 3][3, 2][1, 3]] .
Cechy: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
boundaries | ::mlir::ArrayAttr | 32-bitowy atrybut tablicy zmiennoprzecinkowej |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowej liczby zmiennoprzecinkowej lub 64-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych liczb całkowitych bez znaku |
tfl.call_once (TFL::CallOnceOp)
Wywołuje funkcję inicjalizacji
Ta operacja wywołuje podaną funkcję inicjalizacji dla inicjatora sesji w dialekcie zapisanego modelu.
Interfejsy: TflRuntimeVerifyOpInterface
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
session_init_function | ::mlir::StringAttr | atrybut ciągu |
tfl.cast (TFL::CastOp)
Operator rzutu
Rzutuje dane wejściowe z typu wejściowego na typ wyjściowy.
Cechy: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 16-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby zmiennoprzecinkowej lub 64-bitowej liczby zmiennoprzecinkowej lub 1-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczba całkowita bez znaku lub typ TFLite quint8 lub 8-bitowa liczba całkowita bez znaku lub 8-bitowa liczba całkowita bez znaku lub typ złożony z 32-bitowymi wartościami elementów zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 16-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby zmiennoprzecinkowej lub 64-bitowej liczby zmiennoprzecinkowej lub 1-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczba całkowita bez znaku lub typ TFLite quint8 lub 8-bitowa liczba całkowita bez znaku lub 8-bitowa liczba całkowita bez znaku lub typ złożony z 32-bitowymi wartościami elementów zmiennoprzecinkowych |
tfl.ceil (TFL::CeilOp)
Operator sufitu
Zwraca elementarną wartość ceil wejścia.
Cechy: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
Interfejsy: ConditionallySpeculatable, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
x | tensor 32-bitowych wartości zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
y | tensor 32-bitowych wartości zmiennoprzecinkowych |
tfl.complex_abs (TFL::ComplexAbsOp)
Oblicza złożoną wartość bezwzględną tensora.
Biorąc pod uwagę tensor x liczb zespolonych, ta operacja zwraca tensor typu float lub double , który jest wartością bezwzględną każdego elementu w x . Wszystkie elementy w x muszą być liczbami zespolonymi postaci \(a + bj\). Wartość bezwzględna jest obliczana jako \( \sqrt{a^2 + b^2}\).
Cechy: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor typu złożonego z 32-bitowymi elementami zmiennoprzecinkowymi lub typu złożonego z 64-bitowymi wartościami elementów zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub 64-bitowych wartości zmiennoprzecinkowych |
tfl.concatenation (TFL::ConcatenationOp)
Operator łączenia
Łączy tensory wzdłuż jednego wymiaru
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
axis | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
Argumenty:
| Operand | Opis |
|---|---|
values | tensor dowolnych wartości typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 64-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 16-bitowej liczby całkowitej bez znaku lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub typu QUI8 lub 8-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 1 -bitowe wartości całkowite bez znaku |
tfl.control_node (TFL::ControlNodeOp)
_ Operacja TFL.control_node zawija operacje jednoblokowe w celu dołączenia krawędzi sterujących. _
Służy do zawijania regionów i dołączania do nich zależności kontrolnych. Zwykle dzieje się to na jednym z ostatnich kroków przed emisją modelu flatbuffer, aby umożliwić optymalizacje, które opierają się na ustalonej kolejności operacji (takich jak rematerializacja). Eksporter flatbuffer rozpakuje opakowany region i doda adnotację do wygenerowanego modelu za pomocą metadanych tak, że wszelkie zmiany kolejności w czasie wykonywania będą zgodne z kolejnością nadaną przez zależności kontrolne.
Cechy: HasParent mlir::func::FuncOp , RecursiveMemoryEffects, SingleBlockImplicitTerminator
Argumenty:
| Operand | Opis |
|---|---|
controlInputs | kontrola |
Wyniki:
| Wynik | Opis |
|---|---|
outputs | tensor dowolnych wartości typu |
control | kontrola |
tfl.conv_2d (TFL::Conv2DOp)
Operator splotu
Wykonuje operację splotu na wejściach.
Wejścia: inputs[0] : wymagane: wejścia tensora aktywacji inputs[1] : wymagane: inputs[2] : opcjonalne: tensor polaryzacji
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<0, 1>
Interfejsy: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
dilation_h_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_w_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
padding | ::mlir::StringAttr | atrybut ciągu, którego wartość to SAME lub VALID |
stride_h | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_w | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
filter | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI4 lub typu QI8 lub typu QUI8 |
bias | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
tfl.conv_3d (TFL::Conv3DOp)
Operator splotu 3D
Wykonuje operację splotu na wejściach 3D. Wejścia: inputs[0] : wymagane: wejścia tensora aktywacji inputs[1] : wymagane: inputs[2] : opcjonalne: tensor polaryzacji
Cechy: AlwaysSpeculatableImplTrait, quant::AccumulatorUniformScale<2, 0, 1>
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
dilation_d_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_h_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_w_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
padding | ::mlir::StringAttr | atrybut ciągu, którego wartość to SAME lub VALID |
stride_d | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_h | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_w | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych |
filter | tensor 32-bitowych wartości zmiennoprzecinkowych |
bias | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych |
tfl.conv_3d_transpose (TFL::Conv3DTransposeOp)
Transponowany operator splotu 3D
Wykonuje transponowaną operację splotu na wejściach 3D. Wejścia: inputs[0] : wymagane: kształt wyjściowy tensora inputs[1] : wymagane: inputs[2] : wymagane: wejście tensora aktywacji inputs[3] : opcjonalne: tensor polaryzacji
Cechy: AlwaysSpeculatableImplTrait, quant::AccumulatorUniformScale<2, 0, 1>
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
dilation_d_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_h_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_w_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
padding | ::mlir::StringAttr | atrybut ciągu, którego wartość to SAME lub VALID |
stride_d | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_h | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_w | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
Argumenty:
| Operand | Opis |
|---|---|
output_shape | tensor 32-bitowych liczb całkowitych bez znaku |
filter | tensor 32-bitowych wartości zmiennoprzecinkowych |
input | tensor 32-bitowych wartości zmiennoprzecinkowych |
bias | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych |
tfl.cos (TFL::CosOp)
Operator kosinusowy
Oblicza elementarny cosinus danych wejściowych
Cechy: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
Interfejsy: ConditionallySpeculatable, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
x | tensor 32-bitowych wartości zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
y | tensor 32-bitowych wartości zmiennoprzecinkowych |
tfl.cumsum (TFL::CumsumOp)
Operator sumy
Oblicz skumulowaną sumę tensora x wzdłuż osi.
Cechy: AlwaysSpeculatableImplTrait
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
exclusive | ::mlir::BoolAttr | atrybut logiczny |
reverse | ::mlir::BoolAttr | atrybut logiczny |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku |
axis | tensor 32-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku |
tfl.custom (TFL::CustomOp)
Niestandardowy op
Ogólna operacja dla dowolnej niestandardowej operacji TFLite.
input: Lista wejść w oryginalnym op. custom_code: Ciąg używany do określenia, czym dokładnie jest ta operacja, co odpowiada operator_codes.custom_code w buforze płaskim. custom_option: uchwyt do zapisywania atrybutów op w sposób bajtowy. wyjście: Lista wyjść w oryginalnym op.
Interfejsy: TflRuntimeVerifyOpInterface
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
custom_code | ::mlir::StringAttr | atrybut ciągu |
custom_option | ::mlir::TFL::ConstBytesAttr | Reprezentacja atrybutu ciągu skompilowanych bajtów |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor dowolnych wartości typu |
tfl.custom_tf (TFL::CustomTfOp)
Operacja Wrapper dla niestandardowych operacji TF.
Operacja opakowania wokół dowolnej operacji Custom TF. Obejmuje to operacje zdefiniowane przy użyciu custom_opdefs lub połączone, które nie są zdefiniowane w dialekcie TF. Ta operacja po prostu otacza niestandardową operację w regionie. Uwaga nr 1, ta operacja nie będzie zawierała niestandardowych operacji TF Lite zdefiniowanych za pomocą funkcji CustomOp. Uwaga nr 2, ta operacja jest tylko wewnętrzną reprezentacją wewnątrz konwertera i nie jest eksponowana/eksportowana, gdy model jest eksportowany do Flatbuffer.
Cechy: IsolatedFromAbove, RecursiveMemoryEffects, SingleBlockImplicitTerminator
Interfejsy: InferTypeOpInterface, TflRuntimeVerifyOpInterface
Argumenty:
| Operand | Opis |
|---|---|
input | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor dowolnych wartości typu |
tfl.densify (TFL::densifyOp)
Operator zagęszczania
Konwertuje tensor rzadki na format gęsty.
Cechy: AlwaysSpeculatableImplTrait
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub 8-bitowych liczb całkowitych bez znaku |
tfl.depth_to_space (TFL::DepthToSpaceOp)
operator DepthToSpace
Przestawia dane z głębi na bloki danych przestrzennych. To jest odwrotna transformacja SpaceToDepth. Mówiąc dokładniej, ta operacja generuje kopię tensora wejściowego, w którym wartości z wymiaru depth są przenoszone w blokach przestrzennych do wymiarów height i width . Attr block_size wskazuje rozmiar bloku wejściowego i sposób przenoszenia danych.
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
block_size | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku, którego wartość jest dodatnia |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowej liczby zmiennoprzecinkowej lub 8-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu TFLite quint8 lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub wartości typu QUI8 |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowej liczby zmiennoprzecinkowej lub 8-bitowej liczby całkowitej bez znaku lub 32-bitowej liczby całkowitej bez znaku lub 64-bitowej liczby całkowitej bez znaku lub typu TFLite quint8 lub 8-bitowej liczby całkowitej bez znaku lub typu QI8 lub wartości typu QUI8 |
tfl.depthwise_conv_2d (TFL::DepthwiseConv2DOp)
Operator splotu separowalny w głąb
Wykonuje operację splotu na wejściach.
Wejścia: inputs[0] : wymagane: wejścia tensora aktywacji inputs[1] : wymagane: inputs[2] : opcjonalne: tensor polaryzacji
Cechy: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<3, 1>
Interfejsy: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
dilation_h_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
dilation_w_factor | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
padding | ::mlir::StringAttr | atrybut ciągu, którego wartość to SAME lub VALID |
stride_h | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
stride_w | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
depth_multiplier | ::mlir::IntegerAttr | 32-bitowy atrybut liczby całkowitej bez znaku |
Argumenty:
| Operand | Opis |
|---|---|
input | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
filter | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI4 lub typu QI8 lub typu QUI8 |
bias | tensor dowolnych wartości typu lub brak typu |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych lub typu QI8 lub typu QUI8 lub typu QI16 |
tfl.dequantize (TFL::DekwantyzacjaOp)
Operator dekwantyzacji
Konwertuje skwantyzowaną tablicę liczb całkowitych na liczby zmiennoprzecinkowe zgodnie z parametrami kwantyzacji.
Interfejsy: NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Argumenty:
| Operand | Opis |
|---|---|
input | tensor typu QI4 lub typu QI8 lub typu QUI8 lub typu QI16 lub 16-bitowych wartości zmiennoprzecinkowych |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych wartości zmiennoprzecinkowych |
tfl.div (TFL::DivOp)
Operator dywizji
Operacja dzielenia elementarnego.
Cechy: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, ResultsBroadcastableShape
Interfejsy: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Efekty: PamięćEffects::Effect{}
Atrybuty:
| Atrybut | Typ MLIR | Opis |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | atrybut ciągu, którego wartością jest NONE lub RELU lub RELU_N1_TO_1 lub RELU6 lub TANH lub SIGN_BIT |
Argumenty:
| Operand | Opis |
|---|---|
lhs | tensor 32-bitowych liczb zmiennoprzecinkowych lub 32-bitowych liczb całkowitych bez znaku lub wartości typu QUI8 |
rhs | tensor 32-bitowych liczb zmiennoprzecinkowych lub 32-bitowych liczb całkowitych bez znaku lub wartości typu QUI8 |
Wyniki:
| Wynik | Opis |
|---|---|
output | tensor 32-bitowych liczb zmiennoprzecinkowych lub 32-bitowych liczb całkowitych bez znaku lub wartości typu QUI8 |
tfl.dynamic_update_slice (TFL::DynamicUpdateSliceOp)
DynamicUpdateSlice.
DynamicUpdateSlice op, które mają taką samą semantykę jak XLA DynamicUpdateSlice. Generates a result which is the value of the input array operand, with a slice update overwritten at start_indices.
See https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
operand | tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
update | tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
start_indices | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or 8-bit signless integer values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or 8-bit signless integer values |
tfl.embedding_lookup (TFL::EmbeddingLookupOp)
Embedding lookup operator
Looks up ids in a list of embedding tensors.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
lookup | tensor of 32-bit signless integer values |
value | tensor of 32-bit float or 8-bit signless integer or 8-bit unsigned integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 8-bit unsigned integer values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 1-bit signless integer or 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or TFLite string type values |
y | tensor of 1-bit signless integer or 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or TFLite string type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QI8 type or QI16 type values |
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] .
Other examples:
# '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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
dim | tensor of 32/64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
tfl.external_const (TFL::ExternalConstOp)
External const op.
External const op holds a buffer_index which points to a constant in the flatbuffer.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
buffer_index | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
min | ::mlir::FloatAttr | 32-bit float attribute |
max | ::mlir::FloatAttr | 32-bit float attribute |
num_bits | ::mlir::IntegerAttr | 32-bit signless integer attribute whose minimum value is 2 whose maximum value is 16 |
narrow_range | ::mlir::BoolAttr | bool attribute whose value is false |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
dims | tensor of 32/64-bit signless integer values |
input | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values |
Results:
| Result | Description |
|---|---|
result | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values |
rhs | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
rhs | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
tfl.fully_connected (TFL::FullyConnectedOp)
Fully connected op
Traits: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<-1, 1>
Interfaces: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
weights_format | ::mlir::StringAttr | string attribute whose value is DEFAULT, or SHUFFLED4x16INT8 |
keep_num_dims | ::mlir::BoolAttr | bool attribute |
asymmetric_quantize_inputs | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or QUI16 type values |
filter | tensor of 32-bit float or QI4 type or QI8 type or QUI8 type or QI16 type values |
bias | tensor of any type values or none type |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
batch_dims | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
params | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
indices | tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
params | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or TFLite string type values |
indices | tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or TFLite string type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
approximate | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type or QUI8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
rhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values |
rhs | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QUI8 type or QI8 type values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
table_id | ::mlir::IntegerAttr | 32-bit signless integer attribute |
key_dtype | ::mlir::TypeAttr | any type attribute |
value_dtype | ::mlir::TypeAttr | any type attribute |
Results:
| Result | Description |
|---|---|
out | tensor of resource values |
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
Operands:
| Operand | Description |
|---|---|
hash_table | tensor of resource values |
keys | tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values |
default_value | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
out | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values |
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
Operands:
| Operand | Description |
|---|---|
hash_table | tensor of resource values |
keys | tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values |
values | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values |
tfl.hashtable_size (TFL::HashtableSizeOp)
Computes the number of elements in the given table.
Interfaces: TflRuntimeVerifyOpInterface
Operands:
| Operand | Description |
|---|---|
hash_table | tensor of resource values |
Results:
| Result | Description |
|---|---|
out | tensor of 64-bit signless integer values |
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.
Example:
%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.
Example:
tfl.if %b {
...
}
Traits: NoRegionArguments, RecursiveMemoryEffects, SingleBlockImplicitTerminator
Interfaces: RegionBranchOpInterface, TflRuntimeVerifyOpInterface
Operands:
| Operand | Description |
|---|---|
cond | tensor of 1-bit signless integer values |
Results:
| Result | Description |
|---|---|
results | tensor of any type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 64-bit float values |
tfl.l2_normalization (TFL::L2NormalizationOp)
L2 Normalize Operator
L2Normalization Op
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, FixedOutputRangeInterface, NoMemoryEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
rhs | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values |
rhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
radius | ::mlir::IntegerAttr | 32-bit signless integer attribute |
bias | ::mlir::FloatAttr | 32-bit float attribute |
alpha | ::mlir::FloatAttr | 32-bit float attribute |
beta | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float values |
tfl.log (TFL::LogOp)
Natural logarithm operator
Performs element-wise natural logarithm operation on input.
Traits: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values |
tfl.logical_and (TFL::LogicalAndOp)
Logical AND operator
Element-wise logical AND operation.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 1-bit signless integer values |
rhs | tensor of 1-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
tfl.logical_not (TFL::LogicalNotOp)
Logical NOT operator
Element-wise logical NOT operation.
Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 1-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
tfl.logical_or (TFL::LogicalOrOp)
Logical OR operator
Element-wise logical OR operation.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 1-bit signless integer values |
rhs | tensor of 1-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
cell_clip | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative |
proj_clip | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative |
kernel_type | ::mlir::TFL::LSTMKernelTypeAttr | lstm_kernel_type whose value is mlir::TFL::LSTMKernelType::FULL |
asymmetric_quantize_inputs | ::mlir::BoolAttr | bool attribute |
input_to_input_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_forget_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_cell_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_output_intermediate | ::mlir::TypeAttr | any type attribute |
effective_hidden_scale_intermediate | ::mlir::TypeAttr | any type attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QI16 type values |
input_to_input_weights | tensor of any type values or none type |
input_to_forget_weights | tensor of 32-bit float or QI8 type values |
input_to_cell_weights | tensor of 32-bit float or QI8 type values |
input_to_output_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_input_weights | tensor of any type values or none type |
recurrent_to_forget_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_cell_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_output_weights | tensor of 32-bit float or QI8 type values |
cell_to_input_weights | tensor of any type values or none type |
cell_to_forget_weights | tensor of any type values or none type |
cell_to_output_weights | tensor of any type values or none type |
input_gate_bias | tensor of any type values or none type |
forget_gate_bias | tensor of 32-bit float or QI32 type values |
cell_bias | tensor of 32-bit float or QI32 type values |
output_gate_bias | tensor of 32-bit float or QI32 type values |
projection_weights | tensor of any type values or none type |
projection_bias | tensor of any type values or none type |
input_activation_state | stateful tensor |
input_cell_state | stateful tensor |
input_layer_norm_coefficients | tensor of any type values or none type |
forget_layer_norm_coefficients | tensor of any type values or none type |
cell_layer_norm_coefficients | tensor of any type values or none type |
output_layer_norm_coefficients | tensor of any type values or none type |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Operands:
| Operand | Description |
|---|---|
diagonal | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values |
diagonal | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
result | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
padding | ::mlir::StringAttr | string attribute whose value is SAME, or VALID |
stride_w | ::mlir::IntegerAttr | 32-bit signless integer attribute |
stride_h | ::mlir::IntegerAttr | 32-bit signless integer attribute |
filter_width | ::mlir::IntegerAttr | 32-bit signless integer attribute |
filter_height | ::mlir::IntegerAttr | 32-bit signless integer attribute |
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
rhs | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
max | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values |
axis | tensor of 32-bit signless integer or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
rhs | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
min | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
mode | ::mlir::TFL::MirrorPaddingTypeAttr | mirror_pad_enum |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
pad | tensor of 32-bit signless integer or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
tfl.mul (TFL::MulOp)
Multiplication operator
Element-wise multiplication operation.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, Commutative, QuantizableResult, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
rhs | tensor of 32-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
logits | tensor of 32-bit float values |
num_samples | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
out | tensor of 32-bit signless integer or 64-bit signless integer values |
tfl.neg (TFL::NegOp)
Negation operator
Computes element-wise negation of input
Traits: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values |
tfl.no_value (TFL::NoValueOp)
Constant representing no value.
No value constant op.
Traits: AlwaysSpeculatableImplTrait, ConstantLike
Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
value | ::mlir::UnitAttr | unit attribute |
Results:
| Result | Description |
|---|---|
none_val | none type |
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{}
Operands:
| Operand | Description |
|---|---|
boxes | tensor of 32-bit float values |
scores | tensor of 32-bit float values |
max_output_size | tensor of 32-bit signless integer values |
iou_threshold | tensor of 32-bit float values |
score_threshold | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
selected_indices | tensor of 32-bit signless integer values |
valid_outputs | tensor of 32-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
boxes | tensor of 32-bit float values |
scores | tensor of 32-bit float values |
max_output_size | tensor of 32-bit signless integer values |
iou_threshold | tensor of 32-bit float values |
score_threshold | tensor of 32-bit float values |
soft_nms_sigma | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
selected_indices | tensor of 32-bit signless integer values |
selected_scores | tensor of 32-bit float values |
valid_outputs | tensor of 32-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type or TFLite string type values |
rhs | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type or TFLite string type values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
tolerance | ::mlir::FloatAttr | 32-bit float attribute |
log_if_failed | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of QI8 type or QUI8 type or QI16 type or 16-bit float or TFLite quint8 type values |
ref | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
indices | tensor of 32-bit signless integer or 64-bit signless integer values |
depth | tensor of 32-bit signless integer values |
on_value | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values |
off_value | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values |
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.
For example:
# '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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
values_count | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
values | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
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)
For example:
# '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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
padding | tensor of 32/64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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)
For example:
# '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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
padding | tensor of 32/64-bit signless integer values |
constant_values | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
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
Interfaces: RegionBranchOpInterface
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
tfl.pow (TFL::PowOp)
Power operator
Element-wise power operation.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer values |
rhs | tensor of 32-bit float or 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
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, quant::AffineOpCoefficient<-1, 1>
Interfaces: AffineQuantizedOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values |
alpha | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
qtype | ::mlir::TypeAttr | Tensor type attribute |
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
| Result | Description |
|---|---|
output | tensor of QUI8 type or QI8 type or QI16 type or QUI16 type or TFLite quint8 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
s_param | ::mlir::TFL::SparsityParameterAttr | Sparsity parameter. |
compressed_data | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
qtype | ::mlir::TypeAttr | Tensor type attribute |
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
s_param | ::mlir::TFL::SparsityParameterAttr | Sparsity parameter. |
compressed_data | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
| Result | Description |
|---|---|
output | tensor of QUI8 type or QI8 type or QI16 type or QUI16 type or TFLite quint8 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
qtype | ::mlir::TypeAttr | Tensor type attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of QI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
shape | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
out | tensor of 32-bit float values |
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
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
shape | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
out | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
start | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values |
limit | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values |
delta | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values |
Results:
| Result | Description |
|---|---|
result | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values |
tfl.rank (TFL::RankOp)
Rank operator.
Returns the rank of a tensor.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of any integer type |
tfl.read_variable (TFL::ReadVariableOp)
Reads variable value.
Read variable data identified by 'resource_id'.
Interfaces: TflRuntimeVerifyOpInterface
Operands:
| Operand | Description |
|---|---|
resource_id | tensor of resource values |
Results:
| Result | Description |
|---|---|
result | tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 64-bit float values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 1-bit signless integer values |
reduction_indices | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 1-bit signless integer values |
reduction_indices | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 1-bit signless integer values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
axes | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
axes | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
axes | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QUI8 type or QI8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QUI8 type or QI8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QUI8 type or QI8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
shape | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
align_corners | ::mlir::BoolAttr | bool attribute |
half_pixel_centers | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values |
size | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
align_corners | ::mlir::BoolAttr | bool attribute |
half_pixel_centers | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values |
size | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
seq_dim | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative |
batch_dim | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values |
seq_lengths | tensor of 32/64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values |
axis | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float values |
fft_length | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of complex type with 32-bit float elements values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values |
rhs | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or QI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float or QI8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
indices | tensor of 32-bit signless integer values |
updates | tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or 1-bit signless integer values |
shape | 1D tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or 1-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
tfl.select (TFL::SelectOp)
Select 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:
- Either the same shape (in which case the select is elementwise), or
- condition must be Rank 1 and match over the first dimension.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
condition | tensor of 1-bit signless integer values |
x | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
y | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
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:
- Either the same shape (in which case the select is elementwise), or
- Broadcastable shapes between 'condition', 'x' and 'y'.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, QuantizableResult, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
condition | tensor of 1-bit signless integer values |
x | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
y | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
tfl.shape (TFL::ShapeOp)
Shape operator
Returns the shape of a tensor.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
out_type | ::mlir::Attribute | derived attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit signless integer or 64-bit signless integer values |
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, SameOperandsAndResultElementType, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float or 64-bit float or 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 64-bit float or 32-bit signless integer values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
begin | tensor of 32/64-bit signless integer values |
size | tensor of 32/64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
tfl.softmax (TFL::SoftmaxOp)
Softmax operator
Computes element-wise softmax activations with the following formula
exp(input) / tf.reduce_sum(exp(input * beta), dim)
Traits: AlwaysSpeculatableImplTrait, QuantizableResult, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable, FixedOutputRangeInterface, NoMemoryEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
beta | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
block_shape | tensor of 32-bit signless integer values |
paddings | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
block_size | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
sparse_indices | tensor of 32/64-bit signless integer values |
output_shape | tensor of 32/64-bit signless integer values |
sparse_values | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values |
default_value | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values |
Results:
| Result | Description |
|---|---|
dense | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
num_splits | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
| Operand | Description |
|---|---|
split_dim | tensor of 32-bit signless integer values |
value | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
outputs | tensor of any type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
num_splits | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
| Operand | Description |
|---|---|
value | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values |
size_splits | 1D tensor of 32-bit signless integer values |
split_dim | 0D tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
outputs | tensor of any type values |
tfl.sqrt (TFL::SqrtOp)
Square root operator
Computes element-wise Square root of input
Traits: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
x | tensor of 32-bit float values |
Results:
| Result | Description |
|---|---|
y | tensor of 32-bit float values |
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{}
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer or QI8 type values |
rhs | tensor of 32-bit float or 32-bit signless integer or QI8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or QI8 type values |
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 .
For example:
# '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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
squeeze_dims | ::mlir::ArrayAttr | 64-bit integer array attribute whose size is at most 8 |
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
tfl.strided_slice (TFL::StridedSliceOp)
StridedSlice Op
Return a strided slice from input .
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
begin_mask | ::mlir::IntegerAttr | 32-bit signless integer attribute |
end_mask | ::mlir::IntegerAttr | 32-bit signless integer attribute |
ellipsis_mask | ::mlir::IntegerAttr | 32-bit signless integer attribute |
new_axis_mask | ::mlir::IntegerAttr | 32-bit signless integer attribute |
shrink_axis_mask | ::mlir::IntegerAttr | 32-bit signless integer attribute |
offset | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or 1-bit signless integer or 16-bit signless integer or QI16 type or TFLite quint8 type or TFLite string type values |
begin | tensor of 32-bit signless integer values |
end | tensor of 32-bit signless integer values |
strides | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or 1-bit signless integer or 16-bit signless integer or QI16 type or TFLite quint8 type or TFLite string type values |
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{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
Operands:
| Operand | Description |
|---|---|
lhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
rhs | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
tfl.sum (TFL::SumOp)
Sum operator
Computes the sum reduction along the specified axes
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
axes | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
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, quant::AccumulatorUniformScale<3, 2, 4>
Interfaces: DynamicRangeQuantizedOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
rank | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
asymmetric_quantize_inputs | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type values |
feature_weights | tensor of 32-bit float or QI8 type or QUI8 type values |
time_weights | tensor of 32-bit float or QI16 type values |
input_gate_bias | tensor of any type values or none type |
activation_state | stateful tensor |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values |
multiples | tensor of 32/64-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values |
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{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values |
k | tensor of 16-bit signless integer or 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
values | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values |
indices | tensor of 16-bit signless integer or 32-bit signless integer values |
tfl.transpose (TFL::TransposeOp)
Transpose operator
Returns the Transpose of x
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit signless integer or 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values |
perm | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit signless integer or 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values |
tfl.transpose_conv (TFL::TransposeConvOp)
Transpose convolution operator
Performs transpose convolution operation on input.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<3, 1, 2>, quant::AffineOpCoefficient<0, 1>
Interfaces: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
padding | ::mlir::StringAttr | string attribute whose value is SAME, or VALID |
stride_h | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
stride_w | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
Operands:
| Operand | Description |
|---|---|
output_shape | tensor of 32-bit signless integer values |
weights | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values |
input | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values |
bias | tensor of any type values or none type |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values |
tfl.unidirectional_sequence_lstm (TFL::UnidirectionalSequenceLSTMOp)
Unidirectional sequence lstm operator
A recurrent neural network specified by an LSTM cell. This Op supports unrolling the input along the time or batch dimensions, and implements the following operation for each element in the sequence s = 1...sequence_length: outputs[s] = state = activation(LSTMOp(inputs[s]))
where LSTMOp is LSTM TF Lite Op and the “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).
Traits: QuantizableResult
Interfaces: DynamicRangeQuantizedOpInterface, InferTypeOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
cell_clip | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative |
proj_clip | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative |
time_major | ::mlir::BoolAttr | bool attribute |
asymmetric_quantize_inputs | ::mlir::BoolAttr | bool attribute |
diagonal_recurrent_tensors | ::mlir::BoolAttr | bool attribute |
input_to_input_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_forget_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_cell_intermediate | ::mlir::TypeAttr | any type attribute |
input_to_output_intermediate | ::mlir::TypeAttr | any type attribute |
effective_hidden_scale_intermediate | ::mlir::TypeAttr | any type attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float values |
input_to_input_weights | tensor of any type values or none type |
input_to_forget_weights | tensor of 32-bit float or QI8 type values |
input_to_cell_weights | tensor of 32-bit float or QI8 type values |
input_to_output_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_input_weights | tensor of any type values or none type |
recurrent_to_forget_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_cell_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_output_weights | tensor of 32-bit float or QI8 type values |
cell_to_input_weights | tensor of any type values or none type |
cell_to_forget_weights | tensor of any type values or none type |
cell_to_output_weights | tensor of any type values or none type |
input_gate_bias | tensor of any type values or none type |
forget_gate_bias | tensor of 32-bit float values |
cell_bias | tensor of 32-bit float values |
output_gate_bias | tensor of 32-bit float values |
projection_weights | tensor of any type values or none type |
projection_bias | tensor of any type values or none type |
input_activation_state | stateful tensor |
input_cell_state | stateful tensor |
input_layer_norm_coefficients | tensor of any type values or none type |
forget_layer_norm_coefficients | tensor of any type values or none type |
cell_layer_norm_coefficients | tensor of any type values or none type |
output_layer_norm_coefficients | tensor of any type values or none type |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or QI8 type values |
tfl.unidirectional_sequence_rnn (TFL::UnidirectionalSequenceRNNOp)
Unidirectional sequence rnn operator
A recurrent neural network specified by an RNN cell. This Op takes in input in a format {batch_size, seq_len, input_size} or {seq_len, batch_size, input_size} if it's time-majored.
It implements the following operation for each element in the sequence s = 1...sequence_length: outputs[s] = state = activation(RNNOp(inputs[s]))
where RNNOp is RNNOp TF Lite Op and the “activation” is the function passed as the “fused_activation_function” argument (if not “NONE”).
Traits: QuantizableResult
Interfaces: DynamicRangeQuantizedOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
time_major | ::mlir::BoolAttr | bool attribute |
fused_activation_function | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT |
asymmetric_quantize_inputs | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float values |
input_to_input_weights | tensor of 32-bit float or QI8 type values |
recurrent_to_input_weights | tensor of 32-bit float or QI8 type values |
input_gate_bias | tensor of 32-bit float values |
hidden_state | stateful tensor |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float values |
tfl.unique (TFL::UniqueOp)
Unique Op.
This operation returns a tensor output containing all of the unique elements of input sorted in the same order that they occur in input . This operation also returns a tensor idx the same size as x that contains the index of each value of input in the unique output output . In other words:
Traits: AlwaysSpeculatableImplTrait, QuantizableResult
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
idx_out_type | ::mlir::Attribute | derived attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
idx | tensor of 32/64-bit signless integer values |
tfl.unpack (TFL::UnpackOp)
Unpacks a tensor along a dimension into multiple tensors
Unpacks a given dimension of a rank- R tensor into num rank- (R-1) tensors.
Unpacks num tensors from value by chipping it along the axis dimension. For example, given a tensor of shape (A, B, C, D) ;
If axis == 0 then the i'th tensor in output is the slice value[i, :, :, :] and each tensor in output will have shape (B, C, D) . (Note that the dimension unpacked along is gone, unlike split ).
If axis == 1 then the i'th tensor in output is the slice value[:, i, :, :] and each tensor in output will have shape (A, C, D) . Etc.
This is the opposite of pack .
Traits: AlwaysSpeculatableImplTrait, QuantizableResult, SameOperandsAndResultElementType
Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
num | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative |
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit signless integer or QI8 type or QUI8 type or 16-bit signless integer or QI16 type values |
Results:
| Result | Description |
|---|---|
outputs | tensor of any type values |
tfl.unsorted_segment_max (TFL::UnsortedSegmentMaxOp)
UnsortedSegmentMax operator
Computes the maximum value along segments of a tensor such that output[i] = max(data[j....]) where segment_ids[j...] = i if the maximum is empty for a given segment ID i, it outputs the smallest possible value for the specific numeric type, output[i] = numeric_limits::lowest(). Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
num_segments | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
tfl.unsorted_segment_min (TFL::UnsortedSegmentMinOp)
UnsortedSegmentMin operator
Computes the minimum value along segments of a tensor such that output[i] = min(data[j....]) where segment_ids[j...] = i if the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output[i] = numeric_limits::max(). Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
num_segments | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
tfl.unsorted_segment_prod (TFL::UnsortedSegmentProdOp)
UnsortedSegmentProd operator
Computes the product along segments of a tensor.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
num_segments | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
tfl.unsorted_segment_sum (TFL::UnsortedSegmentSumOp)
UnsortedSegmentSum operator
From a tensor segmentation, computes the output resulting from summing together elements mapped to the same segment_id. Ie output[i] is equal to the tensor sum of all elements from the input tensor mapped to segment_id i . If no tensors are mapped to a particular included segment_id, the output at that indice will be a zero tensor with the appropriate shape. Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
num_segments | tensor of 32-bit signless integer values |
Results:
| Result | Description |
|---|---|
output | tensor of 32-bit float or 32-bit signless integer values |
tfl.var_handle (TFL::VarHandleOp)
Returns a handle to a variable resource from its name.
Returns a handle for a variable resource from its name. container: the container this variable is placed in. shared_name: the name by which this variable is referred to.
Interfaces: TflRuntimeVerifyOpInterface
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
container | ::mlir::StringAttr | string attribute |
shared_name | ::mlir::StringAttr | string attribute |
Results:
| Result | Description |
|---|---|
resource_handle | tensor of resource values |
tfl.where (TFL::WhereOp)
Returns locations of nonzero / true values in a tensor.
This operation returns the coordinates of true elements in condition . The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in condition . Indices are output in row-major order.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
condition | tensor of 1-bit signless integer or 32-bit float or 32/64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer values |
Results:
| Result | Description |
|---|---|
index | tensor of 64-bit signless integer values |
tfl.while (TFL::WhileOp)
While loop
output = input; while (cond(output)) { output = body(output) }
While loop where all values are passes through arguments with implicit capture.
input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. cond: A region that takes 'input' and returns a boolean scalar tensor. body: A region that takes a list of tensors and returns another list of tensors. Both lists have the same types.
Traits: SingleBlockImplicitTerminator
Interfaces: LoopLikeOpInterface, TflRuntimeVerifyOpInterface
Attributes:
| Attribute | MLIR Type | Description |
|---|---|---|
is_stateless | ::mlir::BoolAttr | bool attribute |
Operands:
| Operand | Description |
|---|---|
input | tensor of any type values |
Results:
| Result | Description |
|---|---|
output | tensor of any type values |
tfl.yield (TFL::YieldOp)
Yield operation
The "yield" operation represents a return operation within the conditional and body of structured control flow (eg, while), and a terminator for ControlNodeOp. The operation takes a variable number of operands and produces no results. The operand number and types must match the signature of the region that contains the operation.
Traits: AlwaysSpeculatableImplTrait, QuantizableResult, Terminator
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
| «unnamed» | any type |
tfl.zeros_like (TFL::ZerosLikeOp)
ZerosLike operator
Returns a tensor of zeros with the same shape and type as the input tensor.
Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
| Operand | Description |
|---|---|
input | tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values |
Results:
| Result | Description |
|---|---|
output | tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values |
Attribute definition
DimensionMetadataAttr
Dimension metadata.
Syntax:
#tfl.dimension_metadata<
::mlir::TFL::DimensionTypeAttr, # format
int32_t, # dense_size
::llvm::ArrayRef<int32_t>, # segments
::llvm::ArrayRef<int32_t> # indices
>
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| format | ::mlir::TFL::DimensionTypeAttr | dimension_type |
| dense_size | int32_t | |
| segments | ::llvm::ArrayRef<int32_t> | |
| indices | ::llvm::ArrayRef<int32_t> |
SparsityParameterAttr
Sparsity parameter.
Syntax:
#tfl.sparsity_parameter<
::llvm::ArrayRef<int32_t>, # traversal_order
::llvm::ArrayRef<int32_t>, # block_map
::llvm::ArrayRef<DimensionMetadataAttr> # dim_metadata
>
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| traversal_order | ::llvm::ArrayRef<int32_t> | |
| block_map | ::llvm::ArrayRef<int32_t> | |
| dim_metadata | ::llvm::ArrayRef<DimensionMetadataAttr> |
ConstBytesAttr
A string attribute representation of compiled bytes
Syntax Examples:
#tfl<const_bytes : "0xDEADBEEF">
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| value | ::llvm::StringRef |
DimensionTypeAttr
dimension_type
Syntax:
#tfl.dimension_type_attr<
::mlir::TFL::DimensionType # value
>
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| value | ::mlir::TFL::DimensionType | an enum of type DimensionType |
LSTMKernelTypeAttr
lstm_kernel_type
Syntax:
#tfl.lstm_kernel_type_attr<
::mlir::TFL::LSTMKernelType # value
>
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| value | ::mlir::TFL::LSTMKernelType | an enum of type LSTMKernelType |
MirrorPaddingTypeAttr
mirror_pad_enum
Syntax:
#tfl.mirror_pad_attr<
::mlir::TFL::MirrorPaddingType # value
>
Parameters:
| Parameter | C++ type | Description |
|---|---|---|
| value | ::mlir::TFL::MirrorPaddingType | an enum of type MirrorPaddingType |