TensorFlow Lite 방언.
이 방언은 TensorFlow Lite 작업에 매핑됩니다.
불변:
- 모든 값은 Tensor 유형입니다(특히 스칼라는 0차원 텐서를 사용하여 표시됨).
작업 정의
tfl.abs
(TFL::AbsOp)
절대값 연산자
텐서 x
주어지면 이 작업은 x
에 있는 각 요소의 절대값을 포함하는 텐서를 반환합니다. 예를 들어 x가 입력 요소이고 y가 출력 요소인 경우 이 연산은 \(y = |x|\)계산합니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult, SameOperandsAndResultShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
x | 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 32비트 부동 소수점 또는 QI8 유형 또는 QI16 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
y | 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 32비트 부동 소수점 또는 QI8 유형 또는 QI16 유형 값의 텐서 |
tfl.add
(TFL::AddOp)
더하기 연산자
요소별 덧셈 연산.
특성: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, Commutative, QuantizableResult, ResultsBroadcastableShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
피연산자:
피연산자 | 설명 |
---|---|
lhs | 32비트 부동 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
rhs | 32비트 부동 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
tfl.add_n
(TFL::AddNOp)
_n 연산자 추가
모든 입력 텐서를 요소별로 추가합니다.
특성: AlwaysSpeculatableImplTrait, Commutative
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
inputs | 모든 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
sum | 32비트 부동 소수점 또는 32비트 무부호 정수 값의 텐서 |
tfl.arg_max
(TFL::ArgMaxOp)
ArgMax 연산자
텐서 차원에서 가장 큰 값을 가진 인덱스를 반환합니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
output_type | ::mlir::속성 | 파생 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 1비트 부호 없는 정수 또는 32비트 부동 소수점 또는 32비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
dim | 32/64비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32/64비트 무부호 정수 값의 텐서 |
tfl.arg_min
(TFL::ArgMinOp)
ArgMin 연산자
텐서 차원에서 가장 작은 값을 가진 인덱스를 반환합니다. a = [1, 10, 26.9, 2.8, 166.32, 62.3] b = tf.math.argmin(입력 = a) c = tf.keras.backend.eval(b)
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
output_type | ::mlir::속성 | 파생 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 1비트 부호 없는 정수 또는 32비트 부동 소수점 또는 32비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
dim | 32/64비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32/64비트 무부호 정수 값의 텐서 |
tfl.assign_variable
(TFL::AssignVariableOp)
변수에 새 값을 할당합니다.
이 작업에 대한 컨트롤 종속성이 있는 모든 ReadVariableOp는 이 값 또는 변수의 후속 새 값을 반환하도록 보장됩니다.
인터페이스: TflRuntimeVerifyOpInterface
피연산자:
피연산자 | 설명 |
---|---|
resource_id | 자원 값의 텐서 |
value | 32비트 부동 소수점 또는 64비트 부동 소수점 또는 1비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 QI16 유형의 텐서 또는 32비트 부동 요소가 있는 복합 유형 또는 64비트 부동 요소 값이 있는 복합 유형 |
tfl.atan2
(TFL::Atan2Op)
Atan2 작업
"atan2" 연산은 인수의 부호를 고려하여 요소별로 y/x의 아크탄젠트를 계산합니다.
특성: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, SameOperandsAndResultShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
y | 32비트 부동 소수점 또는 64비트 부동 소수점 값의 텐서 |
x | 32비트 부동 소수점 또는 64비트 부동 소수점 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 64비트 부동 소수점 값의 텐서 |
tfl.average_pool_2d
(TFL::AveragePool2DOp)
_Average_pool 2d 연산자
입력에 대해 평균 풀링 작업을 수행합니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
filter_height | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
filter_width | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
padding | ::mlir::문자열 속성 | 값이 SAME 또는 VALID인 문자열 속성 |
stride_h | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_w | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
tfl.basic_lstm
(TFL::BasicLSTMOp)
기본 lstm 연산자
기본 LSTM 셀 오퍼레이터.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
cell_clip | ::mlir::FloatAttr | 값이 음수가 아닌 32비트 float 속성 |
proj_clip | ::mlir::FloatAttr | 값이 음수가 아닌 32비트 float 속성 |
kernel_type | ::mlir::TFL::LSTMKernelTypeAttr | 값이 mlir::TFL::LSTMKernelType::BASIC인 lstm_kernel_type |
피연산자:
피연산자 | 설명 |
---|---|
data_input | 32비트 float 또는 QUI8 유형 값의 텐서 |
prev_activ_input | 32비트 float 또는 QUI8 유형 값의 텐서 |
weights_input | 32비트 float 또는 QUI8 유형 값의 텐서 |
biases_input | 32비트 float 또는 QI32 유형 값의 텐서 |
prev_state_input | 32비트 float 또는 QI16 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
activ_output | 모든 유형 값의 2D 텐서 |
state_output | 모든 유형 값의 2D 텐서 |
concat_temp | 모든 유형 값의 2D 텐서 |
activ_temp | 모든 유형 값의 2D 텐서 |
tfl.batch_matmul
(TFL::BatchMatMulOp)
배치 행렬 곱하기 연산자
입력에 대해 일괄 처리된 행렬 곱셈을 수행합니다. TensorFlow BatchMatMulV2의 규칙을 따르고 배치 차원 및 브로드캐스팅에서 알 수 없는 차원을 지원합니다.
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)
특성: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, DynamicRangeQuantizedOpInterface, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
adj_x | ::mlir::부울 속성 | 부울 속성 |
adj_y | ::mlir::부울 속성 | 부울 속성 |
asymmetric_quantize_inputs | ::mlir::부울 속성 | 부울 속성 |
피연산자:
피연산자 | 설명 |
---|---|
x | 32비트 부동 소수점 또는 QI8 유형 또는 QI16 유형 또는 8비트 무부호 정수 값의 텐서 |
y | 32비트 부동 소수점 또는 QI8 유형 또는 QI16 유형 또는 8비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 QI8 유형 또는 QI16 유형 또는 32비트 무부호 정수 값의 텐서 |
tfl.batch_to_space_nd
(TFL::BatchToSpaceNdOp)
BatchToSpaceNd 연산자
이 작업은 "배치" 차원 0을 공간 차원으로 재구성합니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 또는 8비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
block_shape | 32비트 부호 없는 정수 값의 텐서 |
indices | 32비트 부호 없는 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
tfl.bidirectional_sequence_lstm
(TFL::BidirectionalSequenceLSTMOp)
양방향 시퀀스 lstm 연산자
양방향 lstm은 기본적으로 두 개의 lstm으로, 하나는 앞으로 실행되고 다른 하나는 뒤로 실행됩니다. 그리고 출력은 두 lstms의 연결입니다.
특성: QuantizableResult
인터페이스: DynamicRangeQuantizedOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
cell_clip | ::mlir::FloatAttr | 값이 음수가 아닌 32비트 float 속성 |
proj_clip | ::mlir::FloatAttr | 값이 음수가 아닌 32비트 float 속성 |
merge_outputs | ::mlir::부울 속성 | 부울 속성 |
time_major | ::mlir::부울 속성 | 부울 속성 |
asymmetric_quantize_inputs | ::mlir::부울 속성 | 부울 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_input_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_input_to_forget_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_input_to_cell_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_input_to_output_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_recurrent_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_recurrent_to_forget_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_recurrent_to_cell_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_recurrent_to_output_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
fw_cell_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_cell_to_forget_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_cell_to_output_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_input_gate_bias | 모든 유형 값 또는 없음 유형의 텐서 |
fw_forget_gate_bias | 32비트 부동 소수점 값의 텐서 |
fw_cell_bias | 32비트 부동 소수점 값의 텐서 |
fw_output_gate_bias | 32비트 부동 소수점 값의 텐서 |
fw_projection_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_projection_bias | 모든 유형 값 또는 없음 유형의 텐서 |
bw_input_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_input_to_forget_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_input_to_cell_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_input_to_output_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_recurrent_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_recurrent_to_forget_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_recurrent_to_cell_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_recurrent_to_output_weights | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
bw_cell_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_cell_to_forget_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_cell_to_output_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_input_gate_bias | 모든 유형 값 또는 없음 유형의 텐서 |
bw_forget_gate_bias | 32비트 부동 소수점 값의 텐서 |
bw_cell_bias | 32비트 부동 소수점 값의 텐서 |
bw_output_gate_bias | 32비트 부동 소수점 값의 텐서 |
bw_projection_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_projection_bias | 모든 유형 값 또는 없음 유형의 텐서 |
fw_input_activation_state | 상태 저장 텐서 |
fw_input_cell_state | 상태 저장 텐서 |
bw_input_activation_state | 상태 저장 텐서 |
bw_input_cell_state | 상태 저장 텐서 |
aux_input | 모든 유형 값 또는 없음 유형의 텐서 |
fw_aux_input_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_aux_input_to_forget_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_aux_input_to_cell_weights | 모든 유형 값 또는 없음 유형의 텐서 |
fw_aux_input_to_output_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_aux_input_to_input_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_aux_input_to_forget_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_aux_input_to_cell_weights | 모든 유형 값 또는 없음 유형의 텐서 |
bw_aux_input_to_output_weights | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
fw_output | 모든 유형 값의 텐서 |
bw_output | 모든 유형 값의 텐서 |
tfl.bitcast
(TFL::BitcastOp)
비트캐스트 연산자
한 유형에서 다른 유형으로 텐서를 비트캐스팅합니다.
특성: AlwaysSpeculatableImplTrait
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 모든 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 모든 유형 값의 텐서 |
tfl.bitwise_xor
(TFL::BitwiseXorOp)
비트별 Xor 연산자
Elementwise는 lhs
및 rhs
의 비트별 XOR을 계산합니다.
특성: AlwaysSpeculatableImplTrait, Commutative, SameOperandsAndResultElementType
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
lhs | 8비트 무부호 정수 또는 8비트 무부호 정수 또는 16비트 무부호 정수 또는 16비트 무부호 정수 또는 32비트 무부호 정수 또는 32비트 무부호 정수 값의 텐서 |
rhs | 8비트 무부호 정수 또는 8비트 무부호 정수 또는 16비트 무부호 정수 또는 16비트 무부호 정수 또는 32비트 무부호 정수 또는 32비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 8비트 무부호 정수 또는 8비트 무부호 정수 또는 16비트 무부호 정수 또는 16비트 무부호 정수 또는 32비트 무부호 정수 또는 32비트 무부호 정수 값의 텐서 |
tfl.broadcast_args
(TFL::BroadcastArgsOp)
브로드캐스트로 s0 op s1의 모양을 반환합니다.
모양을 나타내는 텐서인 s0
및 s1
주어지면 브로드캐스트된 모양인 r0
계산합니다. s0
, s1
및 r0
은 모두 정수 벡터입니다.
특성: AlwaysSpeculatableImplTrait
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
s0 | 32/64비트 무부호 정수 값의 텐서 |
s1 | 32/64비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
r0 | 32/64비트 무부호 정수 값의 텐서 |
tfl.broadcast_to
(TFL::BroadcastToOp)
호환되는 모양에 대한 배열을 브로드캐스트합니다.
브로드캐스팅은 산술 연산을 위해 호환되는 모양을 갖도록 배열을 만드는 프로세스입니다. 두 모양은 각 차원 쌍에 대해 같거나 그 중 하나가 하나인 경우 호환됩니다. Tensor를 셰이프에 브로드캐스트하려고 할 때 Tensor는 후행 차원에서 시작하여 앞으로 작업합니다.
예를 들어,
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]], 모양=(3, 3), dtype=int32)
위의 예에서 [1, 3]
모양의 입력 Tensor는 [3, 3]
모양의 출력 Tensor로 브로드캐스팅됩니다.
텐서에 스칼라를 곱하는 것과 같은 브로드캐스팅 작업을 수행할 때 브로드캐스팅된 텐서는 절대 구체화되지 않으므로 브로드캐스팅은 (보통) 시간적 또는 공간적 이점을 제공합니다.
그러나 broadcast_to
에는 그러한 이점이 없습니다. 새로 생성된 텐서는 브로드캐스트된 모양의 전체 메모리를 사용합니다. (그래프 컨텍스트에서 broadcast_to
후속 작업에 융합된 다음 최적화될 수 있습니다.)
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 또는 32비트 부호 없는 정수 또는 1비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 8비트 부호 없는 정수 또는 QUI8 유형 또는 16비트 부호 없는 정수 또는 QI16 유형 또는 64비트 부호 없는 텐서 정수 또는 32비트 부동 요소 값이 있는 복합 유형 |
shape | 32/64비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 또는 32비트 부호 없는 정수 또는 1비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 8비트 부호 없는 정수 또는 QUI8 유형 또는 16비트 부호 없는 정수 또는 QI16 유형 또는 64비트 부호 없는 텐서 정수 또는 32비트 부동 요소 값이 있는 복합 유형 |
tfl.bucketize
(TFL::BucketizeOp)
'경계'를 기준으로 '입력'을 버킷화합니다.
예:
입력이 boundaries = [0, 10, 100]
이고 input = [[-5, 10000][150, 10][5, 100]]
경우 출력은 output = [[0, 3][3, 2][1, 3]]
.
특성: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
boundaries | ::mlir::ArrayAttr | 32비트 부동 배열 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 소수점 또는 64비트 부동 소수점 또는 32비트 무부호 정수 또는 64비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부호 없는 정수 값의 텐서 |
tfl.call_once
(TFL::CallOnceOp)
초기화 함수 호출
이 작업은 tf 저장된 모델 방언의 세션 초기화 프로그램에 대해 지정된 초기화 함수를 호출합니다.
인터페이스: TflRuntimeVerifyOpInterface
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
session_init_function | ::mlir::문자열 속성 | 문자열 속성 |
tfl.cast
(TFL::CastOp)
캐스트 연산자
입력 유형에서 출력 유형으로 입력을 캐스트합니다.
특성: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 16비트 부동 소수점 또는 32비트 부동 소수점 또는 64비트 부동 소수점 또는 1비트 부호 없는 정수 또는 16비트 부호 없는 정수 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트의 텐서 부호 없는 정수 또는 TFLite quint8 유형 또는 8비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 32비트 부동 요소 값이 있는 복합 유형 |
결과:
결과 | 설명 |
---|---|
output | 16비트 부동 소수점 또는 32비트 부동 소수점 또는 64비트 부동 소수점 또는 1비트 부호 없는 정수 또는 16비트 부호 없는 정수 또는 16비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트의 텐서 부호 없는 정수 또는 TFLite quint8 유형 또는 8비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 32비트 부동 요소 값이 있는 복합 유형 |
tfl.ceil
(TFL::CeilOp)
셀 오퍼레이터
입력의 요소별 셀 값을 반환합니다.
특성: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
인터페이스: 조건부 추측 가능, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
x | 32비트 부동 소수점 값의 텐서 |
결과:
결과 | 설명 |
---|---|
y | 32비트 부동 소수점 값의 텐서 |
tfl.complex_abs
(TFL::ComplexAbsOp)
텐서의 복소수 절댓값을 계산합니다.
복소수의 텐서 x
주어지면 이 연산은 x
에 있는 각 요소의 절대값인 float
또는 double
유형의 텐서를 반환합니다. x
의 모든 요소는 \(a + bj\)형식의 복소수여야 합니다. 절대값은 \( \sqrt{a^2 + b^2}\)으로 계산됩니다.
특성: AlwaysSpeculatableImplTrait, SameOperandsAndResultShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 요소가 있는 복합 유형 또는 64비트 부동 요소 값이 있는 복합 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 64비트 부동 소수점 값의 텐서 |
tfl.concatenation
(TFL::연결Op)
연결 연산자
한 차원을 따라 텐서를 연결합니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
axis | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
피연산자:
피연산자 | 설명 |
---|---|
values | 모든 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 또는 64비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 16비트 부호 없는 정수 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 또는 8비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 1의 텐서 -bit 무부호 정수 값 |
tfl.control_node
(TFL::ControlNodeOp)
_ TFL.control_node
작업은 제어 에지를 연결하기 위해 단일 블록 작업을 래핑합니다. _
영역을 래핑하고 컨트롤 종속성을 연결하는 데 사용됩니다. 일반적으로 이는 고정된 작업 순서(예: 재물질화)에 의존하는 최적화를 활성화하기 위해 플랫 버퍼 모델을 내보내기 전 마지막 단계 중 하나에서 발생합니다. 플랫 버퍼 내보내기는 래핑된 영역을 풀고 생성된 모델에 메타데이터로 주석을 추가합니다. 모든 런타임 재정렬은 제어 종속성에 의해 지정된 순서를 존중합니다.
특성: HasParent mlir::func::FuncOp , RecursiveMemoryEffects, SingleBlockImplicitTerminator
피연산자:
피연산자 | 설명 |
---|---|
controlInputs | 제어 |
결과:
결과 | 설명 |
---|---|
outputs | 모든 유형 값의 텐서 |
control | 제어 |
tfl.conv_2d
(TFL::Conv2DOp)
컨볼루션 연산자
입력에 대해 컨볼루션 연산을 수행합니다.
입력: inputs[0]
: 필수: 입력 활성화 텐서 inputs[1]
: 필수: 필터 가중치 텐서 inputs[2]
: 선택 사항: 바이어스 텐서
특성: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<0, 1>
인터페이스: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, InferTypeOpInterface, NoMemoryEffect(MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
dilation_h_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_w_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
padding | ::mlir::문자열 속성 | 값이 SAME 또는 VALID인 문자열 속성 |
stride_h | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_w | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
filter | 32비트 float 또는 QI4 유형 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
bias | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
tfl.conv_3d
(TFL::Conv3DOp)
컨볼루션 3D 연산자
3D 입력에서 컨볼루션 연산을 수행합니다. 입력: inputs[0]
: 필수: 입력 활성화 텐서 inputs[1]
: 필수: 필터 가중치 텐서 inputs[2]
: 선택 사항: 바이어스 텐서
특성: AlwaysSpeculatableImplTrait, quant::AccumulatorUniformScale<2, 0, 1>
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
dilation_d_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_h_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_w_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
padding | ::mlir::문자열 속성 | 값이 SAME 또는 VALID인 문자열 속성 |
stride_d | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_h | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_w | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 소수점 값의 텐서 |
filter | 32비트 부동 소수점 값의 텐서 |
bias | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 값의 텐서 |
tfl.conv_3d_transpose
(TFL::Conv3DTransposeOp)
Transposed Convolution 3D 연산자
3D 입력에서 Transposed Convolution 작업을 수행합니다. 입력: inputs[0]
: 필수: 출력 텐서의 모양 inputs[1]
: 필수: 필터 가중치 텐서 inputs[2]
: 필수: 입력 활성화 텐서 inputs[3]
: 선택 사항: 바이어스 텐서
특성: AlwaysSpeculatableImplTrait, quant::AccumulatorUniformScale<2, 0, 1>
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
dilation_d_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_h_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_w_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
padding | ::mlir::문자열 속성 | 값이 SAME 또는 VALID인 문자열 속성 |
stride_d | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_h | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_w | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
피연산자:
피연산자 | 설명 |
---|---|
output_shape | 32비트 부호 없는 정수 값의 텐서 |
filter | 32비트 부동 소수점 값의 텐서 |
input | 32비트 부동 소수점 값의 텐서 |
bias | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 값의 텐서 |
tfl.cos
(TFL::CosOp)
코사인 연산자
입력의 요소별 코사인을 계산합니다.
특성: AlwaysSpeculatableImplTrait, InferTensorType, TF::SameOperandsAndResultTypeResolveRef
인터페이스: 조건부 추측 가능, InferShapedTypeOpInterface, InferTypeOpInterface, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
x | 32비트 부동 소수점 값의 텐서 |
결과:
결과 | 설명 |
---|---|
y | 32비트 부동 소수점 값의 텐서 |
tfl.cumsum
(TFL::CumsumOp)
누적 연산자
축을 따라 텐서 x의 누적 합계를 계산합니다.
특성: AlwaysSpeculatableImplTrait
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
exclusive | ::mlir::부울 속성 | 부울 속성 |
reverse | ::mlir::부울 속성 | 부울 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 소수점 또는 32비트 무부호 정수 또는 64비트 무부호 정수 값의 텐서 |
axis | 32비트 부호 없는 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 32비트 무부호 정수 또는 64비트 무부호 정수 값의 텐서 |
tfl.custom
(TFL::CustomOp)
맞춤 작업
모든 TFLite 사용자 지정 작업을 위한 일반 작업입니다.
입력: 원래 작업의 입력 목록입니다. custom_code: 플랫 버퍼의 operator_codes.custom_code에 해당하는 이 작업이 정확히 무엇인지 식별하는 데 사용되는 문자열입니다. custom_option: op 속성을 바이트 방식으로 저장하기 위한 홀더. 출력: 원래 작업의 출력 목록입니다.
인터페이스: TflRuntimeVerifyOpInterface
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
custom_code | ::mlir::문자열 속성 | 문자열 속성 |
custom_option | ::mlir::TFL::ConstBytesAttr | 컴파일된 바이트의 문자열 속성 표현 |
피연산자:
피연산자 | 설명 |
---|---|
input | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 모든 유형 값의 텐서 |
tfl.custom_tf
(TFL::CustomTfOp)
TF 커스텀 작업을 위한 래퍼 작업.
Custom TF op 주변의 wrapper op. 여기에는 custom_opdefs를 사용하여 정의된 작업 또는 TF 방언으로 정의되지 않은 연결된 작업이 포함됩니다. 이 작업은 사용자 지정 작업을 영역 내부에 래핑합니다. 참고 #1, 이 작업에는 CustomOp를 사용하여 정의된 TF Lite 사용자 지정 작업이 포함되지 않습니다. 참고 #2, 이 작업은 변환기 내부의 내부 표현일 뿐이며 모델을 Flatbuffer로 내보낼 때 노출/내보내지 않습니다.
특성: IsolatedFromAbove, RecursiveMemoryEffects, SingleBlockImplicitTerminator
인터페이스: InferTypeOpInterface, TflRuntimeVerifyOpInterface
피연산자:
피연산자 | 설명 |
---|---|
input | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 모든 유형 값의 텐서 |
tfl.densify
(TFL::DensifyOp)
조밀화 연산자
희소 텐서를 고밀도 형식으로 변환합니다.
특성: AlwaysSpeculatableImplTrait
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 8비트 무부호 정수 값의 텐서 |
tfl.depth_to_space
(TFL::DepthToSpaceOp)
DepthToSpace 연산자
데이터를 깊이에서 공간 데이터 블록으로 재정렬합니다. 이것은 SpaceToDepth의 역 변환입니다. 보다 구체적으로, 이 작업은 depth
차원의 값이 공간 블록에서 height
및 width
차원으로 이동되는 입력 텐서의 복사본을 출력합니다. attr block_size
입력 블록 크기와 데이터 이동 방법을 나타냅니다.
특성: AlwaysSpeculateableImplTrait, QuantizableResult
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
block_size | ::mlir::IntegerAttr | 값이 양수인 32비트 부호 없는 정수 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 부동 또는 8비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 TFLite quint8 유형 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 또는 8비트 부호 없는 정수 또는 32비트 부호 없는 정수 또는 64비트 부호 없는 정수 또는 TFLite quint8 유형 또는 8비트 부호 없는 정수 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
tfl.depthwise_conv_2d
(TFL::DepthwiseConv2DOp)
깊이 분리 가능한 컨볼루션 연산자
입력에 대해 컨볼루션 연산을 수행합니다.
입력: inputs[0]
: 필수: 입력 활성화 텐서 inputs[1]
: 필수: 필터 가중치 텐서 inputs[2]
: 선택 사항: 바이어스 텐서
특성: AlwaysSpeculatableImplTrait, QuantizableResult, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<3, 1>
인터페이스: AffineQuantizedOpInterface, ConditionallySpeculatable, DynamicRangeQuantizedOpInterface, NoMemoryEffect(MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
dilation_h_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
dilation_w_factor | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
padding | ::mlir::문자열 속성 | 값이 SAME 또는 VALID인 문자열 속성 |
stride_h | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
stride_w | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
depth_multiplier | ::mlir::IntegerAttr | 32비트 무부호 정수 속성 |
피연산자:
피연산자 | 설명 |
---|---|
input | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
filter | 32비트 float 또는 QI4 유형 또는 QI8 유형 또는 QUI8 유형 값의 텐서 |
bias | 모든 유형 값 또는 없음 유형의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 float 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 값의 텐서 |
tfl.dequantize
(TFL::DequantizeOp)
역양자화 연산자
양자화 매개변수에 따라 양자화된 정수 배열을 부동 소수점으로 변환합니다.
인터페이스: NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
input | QI4 유형 또는 QI8 유형 또는 QUI8 유형 또는 QI16 유형 또는 16비트 부동 소수점 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 값의 텐서 |
tfl.div
(TFL::DivOp)
나눗셈 연산자
요소별 나눗셈 연산.
특성: ::mlir::OpTrait::TFLRuntimeOpTrait, AlwaysSpeculatableImplTrait, ResultsBroadcastableShape
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
속성:
기인하다 | MLIR 유형 | 설명 |
---|---|---|
fused_activation_function | ::mlir::문자열 속성 | 값이 NONE, RELU, RELU_N1_TO_1, RELU6, TANH 또는 SIGN_BIT인 문자열 속성 |
피연산자:
피연산자 | 설명 |
---|---|
lhs | 32비트 부동 소수점 또는 32비트 부호 없는 정수 또는 QUI8 유형 값의 텐서 |
rhs | 32비트 부동 소수점 또는 32비트 부호 없는 정수 또는 QUI8 유형 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 32비트 부동 소수점 또는 32비트 부호 없는 정수 또는 QUI8 유형 값의 텐서 |
tfl.dynamic_update_slice
(TFL::DynamicUpdateSliceOp)
DynamicUpdateSlice.
XLA DynamicUpdateSlice와 동일한 시맨틱을 갖는 DynamicUpdateSlice op. start_indices에서 슬라이스 업데이트를 덮어쓰는 입력 배열 피연산자의 값인 결과를 생성합니다.
https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice 참조
특성: AlwaysSpeculatableImplTrait
인터페이스: 조건부 추측 가능, NoMemoryEffect(MemoryEffectOpInterface), TflRuntimeVerifyOpInterface
효과: MemoryEffects::Effect{}
피연산자:
피연산자 | 설명 |
---|---|
operand | 1비트 무부호 정수 또는 8비트 무부호 정수 또는 32비트 무부호 정수 또는 64비트 무부호 정수 또는 32비트 부동 소수점 값의 텐서 |
update | 1비트 무부호 정수 또는 8비트 무부호 정수 또는 32비트 무부호 정수 또는 64비트 무부호 정수 또는 32비트 부동 소수점 값의 텐서 |
start_indices | 32비트 부호 없는 정수 값의 텐서 |
결과:
결과 | 설명 |
---|---|
output | 1비트 무부호 정수 또는 8비트 무부호 정수 또는 32비트 무부호 정수 또는 64비트 무부호 정수 또는 32비트 부동 소수점 값의 텐서 |
tfl.elu
(TFL::EluOp)
지수 선형 단위 연산자
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 | 설명 |
---|---|
x | tensor of 32-bit float or 8-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QI8 type or QI16 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of any type values |
dim | tensor of 32/64-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
buffer_index | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
batch_dims | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
approximate | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
hash_table | tensor of resource values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
cond | tensor of 1-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
lhs | tensor of 1-bit signless integer values |
rhs | tensor of 1-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
lhs | tensor of 1-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
lhs | tensor of 1-bit signless integer values |
rhs | tensor of 1-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
mode | ::mlir::TFL::MirrorPaddingTypeAttr | mirror_pad_enum |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
logits | tensor of 32-bit float values |
num_samples | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
value | ::mlir::UnitAttr | unit attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
tolerance | ::mlir::FloatAttr | 32-bit float attribute |
log_if_failed | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
values_count | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
values | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
qtype | ::mlir::TypeAttr | Tensor type attribute |
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
value | ::mlir::ElementsAttr | constant vector/tensor attribute |
s_param | ::mlir::TFL::SparsityParameterAttr | Sparsity parameter. |
compressed_data | ::mlir::ElementsAttr | constant vector/tensor attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
qtype | ::mlir::TypeAttr | Tensor type attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
shape | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signless integer attribute |
seed2 | ::mlir::IntegerAttr | 64-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
shape | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
resource_id | tensor of resource values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of 1-bit signless integer values |
reduction_indices | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of 1-bit signless integer values |
reduction_indices | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QUI8 type or QI8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of any type values |
shape | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
align_corners | ::mlir::BoolAttr | bool attribute |
half_pixel_centers | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
align_corners | ::mlir::BoolAttr | bool attribute |
half_pixel_centers | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float values |
fft_length | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or QI8 type or QI16 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float or 32-bit signless integer values |
segment_ids | tensor of 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
out_type | ::mlir::Attribute | derived attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float or 64-bit float or 32-bit signless integer values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
beta | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
block_size | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
num_splits | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
num_splits | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
x | tensor of 32-bit float values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
squeeze_dims | ::mlir::ArrayAttr | 64-bit integer array attribute whose size is at most 8 |
Operands:
Operand | 설명 |
---|---|
input | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
keep_dims | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
input | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
idx_out_type | ::mlir::Attribute | derived attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
num | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative |
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
Operand | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
container | ::mlir::StringAttr | string attribute |
shared_name | ::mlir::StringAttr | string attribute |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|
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 | 설명 |
---|---|---|
is_stateless | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | 설명 |
---|---|
input | tensor of any type values |
Results:
Result | 설명 |
---|---|
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 | 설명 |
---|---|
«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 | 설명 |
---|---|
input | tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values |
Results:
Result | 설명 |
---|---|
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
>
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
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
>
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
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">
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
value | ::llvm::StringRef |
DimensionTypeAttr
dimension_type
Syntax:
#tfl.dimension_type_attr<
::mlir::TFL::DimensionType # value
>
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
value | ::mlir::TFL::DimensionType | an enum of type DimensionType |
LSTMKernelTypeAttr
lstm_kernel_type
Syntax:
#tfl.lstm_kernel_type_attr<
::mlir::TFL::LSTMKernelType # value
>
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
value | ::mlir::TFL::LSTMKernelType | an enum of type LSTMKernelType |
MirrorPaddingTypeAttr
mirror_pad_enum
Syntax:
#tfl.mirror_pad_attr<
::mlir::TFL::MirrorPaddingType # value
>
매개변수:
Parameter | C++ type | 설명 |
---|---|---|
value | ::mlir::TFL::MirrorPaddingType | an enum of type MirrorPaddingType |