'tfl' Dialek

Dialek TensorFlow Lite.

Dialek ini dipetakan ke operasi TensorFlow Lite.

Invarian:

• Semua nilai bertipe Tensor (khususnya, skalar direpresentasikan menggunakan tensor dimensi nol);

Operasi

tfl.abs (TFL::AbsOp)

Operator nilai absolut

Jika diberikan tensor x , operasi ini mengembalikan tensor yang berisi nilai absolut setiap elemen dalam x . Misalnya, jika x adalah elemen masukan dan y adalah elemen keluaran, operasi ini akan menghitung \(y = |x|\).

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau nilai tipe float atau QI8 atau tipe QI16 32-bit

Hasil:

Hasil	Keterangan
y	tensor bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau nilai tipe float atau QI8 atau tipe QI16 32-bit

tfl.add (TFL::TambahkanOp)

Operator tambahan

Operasi penjumlahan berdasarkan elemen.

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

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT

Operan:

Operan	Keterangan
lhs	tensor nilai float 32-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16
rhs	tensor nilai float 32-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16

tfl.add_n (TFL::TambahkanNOp)

_Tambahkan n operator

Menambahkan semua tensor masukan berdasarkan elemen.

Ciri-ciri: AlwaysSpeculatableImplTrait , Commutative

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
inputs	variadik tensor nilai jenis apa pun

Hasil:

Hasil	Keterangan
sum	tensor nilai float 32-bit atau nilai integer tanpa tanda 32-bit

tfl.arg_max (TFL::ArgMaxOp)

Operator ArgMax

Mengembalikan indeks dengan nilai terbesar di seluruh dimensi tensor.

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
output_type	::mlir::Atribut	atribut turunan

Operan:

Operan	Keterangan
input	tensor bilangan bulat tak bertanda 1-bit atau float 32-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8
dim	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai integer tanpa tanda 32/64-bit

tfl.arg_min (TFL::ArgMinOp)

Operator ArgMin

Mengembalikan indeks dengan nilai terkecil di seluruh dimensi tensor. a = [1, 10, 26.9, 2.8, 166.32, 62.3] b = tf.math.argmin(input = a) c = tf.keras.backend.eval(b)

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
output_type	::mlir::Atribut	atribut turunan

Operan:

Operan	Keterangan
input	tensor bilangan bulat tak bertanda 1-bit atau float 32-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8
dim	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai integer tanpa tanda 32/64-bit

tfl.assign_variable (TFL::AssignVariableOp)

Menetapkan nilai baru ke variabel.

ReadVariableOp apa pun dengan ketergantungan kontrol pada operasi ini dijamin akan mengembalikan nilai ini atau nilai variabel berikutnya yang lebih baru.

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
resource_id	tensor nilai sumber daya
value	tensor float 32-bit atau float 64-bit atau bilangan bulat tak bertanda 1-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau tipe QI8 atau tipe QUI8 atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau tipe QI16 atau tipe kompleks dengan elemen float 32-bit atau tipe kompleks dengan nilai elemen float 64-bit

tfl.atan2 (TFL::Atan2Op)

Operasi Atan2

Operasi "atan2" menghitung tangen busur dari elemen y/x, dengan memperhatikan tanda-tanda argumen.

Sifat: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
y	tensor nilai float 32-bit atau nilai float 64-bit
x	tensor nilai float 32-bit atau nilai float 64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit atau nilai float 64-bit

tfl.average_pool_2d (TFL::AveragePool2DOp)

_Rata-rata_kumpulan operator 2 hari

Melakukan operasi pengumpulan rata-rata pada input.

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
filter_height	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
filter_width	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
padding	::mlir::StringAttr	atribut string yang nilainya SAMA, atau VALID
stride_h	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT

Operan:

Operan	Keterangan
input	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16

Hasil:

Hasil	Keterangan
output	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16

tfl.basic_lstm (TFL::BasicLSTMOp)

Operator lstm dasar

Operator Seluler LSTM dasar.

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
cell_clip	::mlir::FloatAttr	Atribut float 32-bit yang nilainya non-negatif
proj_clip	::mlir::FloatAttr	Atribut float 32-bit yang nilainya non-negatif
kernel_type	::mlir::TFL::LSTMKernelTypeAttr	lstm_kernel_type yang nilainya mlir::TFL::LSTMKernelType::BASIC

Operan:

Operan	Keterangan
data_input	tensor nilai tipe float 32-bit atau QUI8
prev_activ_input	tensor nilai tipe float 32-bit atau QUI8
weights_input	tensor nilai tipe float 32-bit atau QUI8
biases_input	tensor nilai tipe float 32-bit atau QI32
prev_state_input	tensor nilai tipe float 32-bit atau QI16

Hasil:

Hasil	Keterangan
activ_output	Tensor 2D dari nilai jenis apa pun
state_output	Tensor 2D dari nilai jenis apa pun
concat_temp	Tensor 2D dari nilai jenis apa pun
activ_temp	Tensor 2D dari nilai jenis apa pun

tfl.batch_matmul (TFL::BatchMatMulOp)

Operator Penggandaan Matriks Batch

Melakukan perkalian matriks batch pada input. Mengikuti konvensi TensorFlow BatchMatMulV2, dengan dukungan untuk dimensi yang tidak diketahui dalam dimensi batch dan penyiaran.

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

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
adj_x	::mlir::BoolAttr	atribut bool
adj_y	::mlir::BoolAttr	atribut bool
asymmetric_quantize_inputs	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
x	tensor float 32-bit atau tipe QI8 atau tipe QI16 atau nilai integer tanpa tanda 8-bit
y	tensor float 32-bit atau tipe QI8 atau tipe QI16 atau nilai integer tanpa tanda 8-bit

Hasil:

Hasil	Keterangan
output	tensor tipe float 32-bit atau QI8 atau tipe QI16 atau nilai integer tanpa tanda 32-bit

tfl.batch_to_space_nd (TFL::BatchToSpaceNdOp)

Operator BatchToSpaceNd

Operasi ini membentuk ulang dimensi "batch" 0 menjadi dimensi ruang.

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16
block_shape	tensor nilai integer tanpa tanda 32-bit
indices	tensor nilai integer tanpa tanda 32-bit

Hasil:

Hasil	Keterangan
output	Tensor float 32-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16

tfl.bidirectional_sequence_lstm (TFL::BidirectSequenceLSTMOp)

Operator lstm urutan dua arah

Lstm dua arah pada dasarnya adalah dua lstm, satu berjalan maju & yang lainnya berjalan mundur. Dan outputnya adalah gabungan dari dua lstm.

Ciri-ciri: QuantizableResult

Antarmuka: DynamicRangeQuantizedOpInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
cell_clip	::mlir::FloatAttr	Atribut float 32-bit yang nilainya non-negatif
proj_clip	::mlir::FloatAttr	Atribut float 32-bit yang nilainya non-negatif
merge_outputs	::mlir::BoolAttr	atribut bool
time_major	::mlir::BoolAttr	atribut bool
asymmetric_quantize_inputs	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
input	tensor nilai integer 32-bit float atau 8-bit signless
fw_input_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_input_to_forget_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_input_to_cell_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_input_to_output_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_recurrent_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_recurrent_to_forget_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_recurrent_to_cell_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_recurrent_to_output_weights	tensor nilai integer 32-bit float atau 8-bit signless
fw_cell_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_cell_to_forget_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_cell_to_output_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_input_gate_bias	tensor nilai tipe apa pun atau tipe apa pun
fw_forget_gate_bias	tensor nilai float 32-bit
fw_cell_bias	tensor nilai float 32-bit
fw_output_gate_bias	tensor nilai float 32-bit
fw_projection_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_projection_bias	tensor nilai tipe apa pun atau tipe apa pun
bw_input_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_input_to_forget_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_input_to_cell_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_input_to_output_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_recurrent_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_recurrent_to_forget_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_recurrent_to_cell_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_recurrent_to_output_weights	tensor nilai integer 32-bit float atau 8-bit signless
bw_cell_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_cell_to_forget_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_cell_to_output_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_input_gate_bias	tensor nilai tipe apa pun atau tipe apa pun
bw_forget_gate_bias	tensor nilai float 32-bit
bw_cell_bias	tensor nilai float 32-bit
bw_output_gate_bias	tensor nilai float 32-bit
bw_projection_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_projection_bias	tensor nilai tipe apa pun atau tipe apa pun
fw_input_activation_state	tensor keadaan
fw_input_cell_state	tensor keadaan
bw_input_activation_state	tensor keadaan
bw_input_cell_state	tensor keadaan
aux_input	tensor nilai tipe apa pun atau tipe apa pun
fw_aux_input_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_aux_input_to_forget_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_aux_input_to_cell_weights	tensor nilai tipe apa pun atau tipe apa pun
fw_aux_input_to_output_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_aux_input_to_input_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_aux_input_to_forget_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_aux_input_to_cell_weights	tensor nilai tipe apa pun atau tipe apa pun
bw_aux_input_to_output_weights	tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
fw_output	tensor nilai jenis apa pun
bw_output	tensor nilai jenis apa pun

tfl.bitcast (TFL::BitcastOp)

Operator Bitcast

Mem-bitcast tensor dari satu jenis ke jenis lainnya.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor nilai tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor nilai jenis apa pun

tfl.bitwise_xor (TFL::BitwiseXorOp)

Operator Xor bitwise

Elementwise menghitung XOR bitwise dari lhs dan rhs .

Sifat: AlwaysSpeculatableImplTrait , Commutative , ResultsBroadcastableShape , SameOperandsAndResultElementType

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau nilai bilangan bulat tak bertanda 32-bit
rhs	tensor bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau nilai bilangan bulat tak bertanda 32-bit

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau nilai bilangan bulat tak bertanda 32-bit

tfl.broadcast_args (TFL::BroadcastArgsOp)

Kembalikan bentuk s0 op s1 dengan siaran.

Mengingat s0 dan s1 , tensor yang merepresentasikan bentuk, hitung r0 , bentuk yang disiarkan. s0 , s1 dan r0 semuanya adalah vektor bilangan bulat.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
s0	tensor nilai integer tanpa tanda 32/64-bit
s1	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil	Keterangan
r0	tensor nilai integer tanpa tanda 32/64-bit

tfl.broadcast_to (TFL::BroadcastToOp)

Siarkan array untuk bentuk yang kompatibel.

Penyiaran adalah proses membuat array memiliki bentuk yang kompatibel untuk operasi aritmatika. Dua bentuk dikatakan kompatibel jika untuk setiap pasangan dimensi keduanya sama atau salah satunya adalah satu. Saat mencoba menyiarkan Tensor ke suatu bentuk, Tensor dimulai dengan dimensi tambahan, dan terus berlanjut.

Misalnya,

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]], bentuk=(3, 3), tiped=int32)

Pada contoh di atas, Tensor masukan berbentuk [1, 3] disiarkan ke Tensor keluaran berbentuk [3, 3] .

Saat melakukan operasi penyiaran seperti mengalikan tensor dengan skalar, penyiaran (biasanya) memberikan manfaat waktu atau ruang, karena tensor yang disiarkan tidak pernah terwujud.

Namun, broadcast_to tidak memberikan manfaat apa pun. Tensor yang baru dibuat mengambil memori penuh dari bentuk yang disiarkan. (Namun, dalam konteks grafik, broadcast_to mungkin digabungkan ke operasi berikutnya dan kemudian dioptimalkan.)

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 1-bit atau bilangan bulat tanpa tanda 4-bit atau bilangan bulat tanpa tanda 8-bit atau tipe QI8 atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 32-bit atau tipe QUI8 atau 16 -bit integer tanpa tanda atau tipe QI16 atau integer tanpa tanda 64-bit atau tipe kompleks dengan nilai elemen float 32-bit
shape	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil

Keterangan

output

tensor float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 1-bit atau bilangan bulat tanpa tanda 4-bit atau bilangan bulat tanpa tanda 8-bit atau tipe QI8 atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 32-bit atau tipe QUI8 atau 16 -bit integer tanpa tanda atau tipe QI16 atau integer tanpa tanda 64-bit atau tipe kompleks dengan nilai elemen float 32-bit

tfl.bucketize (TFL::BucketizeOp)

Mengelompokkan 'masukan' berdasarkan 'batas'.

Contoh:

Jika masukannya adalah boundaries = [0, 10, 100] dan input = [[-5, 10000][150, 10][5, 100]] , maka keluarannya adalah output = [[0, 3][3, 2][1, 3]] .

Ciri-ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
boundaries	::mlir::ArrayAttr	Atribut array float 32-bit

Operan:

Operan	Keterangan
input	tensor nilai float 32-bit atau float 64-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai integer tanpa tanda 32-bit

tfl.call_once (TFL::CallOnceOp)

Memanggil fungsi inisialisasi

Operasi ini memanggil fungsi inisialisasi yang diberikan untuk penginisialisasi sesi dalam dialek model yang disimpan tf.

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
session_init_function	::mlir::StringAttr	atribut string

tfl.cast (TFL::CastOp)

Operator pemeran

Melemparkan input dari tipe input ke tipe output.

Ciri-ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan

Keterangan

input

tensor tipe float 16-bit atau bfloat16 atau float 32-bit atau float 64-bit atau bilangan bulat tak bertanda 1-bit atau bilangan bulat tak bertanda 4-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau Bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau tipe TFLite quint8 atau bilangan bulat tak bertanda 8-bit atau 8-bit tipe integer atau kompleks tanpa tanda dengan nilai elemen float 32-bit

Hasil:

Hasil

Keterangan

output

tensor tipe float 16-bit atau bfloat16 atau float 32-bit atau float 64-bit atau integer tak bertanda 1-bit atau integer tak bertanda 16-bit atau integer tak bertanda 16-bit atau integer tak bertanda 32-bit atau integer tak bertanda 32-bit atau Bilangan bulat tak bertanda 64-bit atau tipe TFLite quint8 atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 8-bit atau tipe kompleks dengan Nilai elemen float 32-bit

tfl.ceil (TFL::CeilOp)

Operator langit-langit

Mengembalikan nilai ceil berdasarkan elemen dari input.

Sifat: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor nilai float 32-bit

Hasil:

Hasil	Keterangan
y	tensor nilai float 32-bit

tfl.complex_abs (TFL::ComplexAbsOp)

Menghitung nilai absolut kompleks dari sebuah tensor.

Diberikan tensor x bilangan kompleks, operasi ini mengembalikan tensor bertipe float atau double yang merupakan nilai absolut setiap elemen dalam x . Semua elemen dalam x harus berbentuk bilangan kompleks \(a + bj\). Nilai absolut dihitung sebagai \( \sqrt{a^2 + b^2}\).

Ciri-ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor tipe kompleks dengan elemen float 32-bit atau tipe kompleks dengan nilai elemen float 64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit atau nilai float 64-bit

tfl.concatenation (TFL::PenggabunganOp)

Operator penggabungan

Menggabungkan tensor sepanjang satu dimensi

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
axis	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT

Operan:

Operan	Keterangan
values	variadik tensor nilai jenis apa pun

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau bilangan bulat tanpa tanda 64-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 8-bit atau tipe QI8 atau tipe QUI8 atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 32-bit atau 1 -bit nilai integer tanpa tanda

tfl.control_node (TFL::ControlNodeOp)

Operasi TFL.control_node membungkus operasi blok tunggal untuk melampirkan tepi kontrol.

Ini digunakan untuk menggabungkan wilayah dan melampirkan dependensi kontrol ke wilayah tersebut. Biasanya, hal ini akan terjadi pada salah satu langkah terakhir sebelum memancarkan model flatbuffer untuk mengaktifkan pengoptimalan yang mengandalkan urutan operasi tetap (seperti rematerialisasi.) Eksportir flatbuffer akan membuka bungkus wilayah yang dibungkus dan memberi anotasi pada model yang dihasilkan dengan metadata sedemikian rupa sehingga setiap pemesanan ulang runtime akan mengikuti urutan yang diberikan oleh dependensi kontrol.

Sifat: HasParent<mlir::func::FuncOp> , RecursiveMemoryEffects , SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Operan:

Operan	Keterangan
controlInputs	variadik kendali

Hasil:

Hasil	Keterangan
outputs	variadik tensor nilai jenis apa pun
control	kontrol

tfl.conv_2d (TFL::Konv2DOp)

Operator konvolusi

Melakukan operasi konvolusi pada input.

Input: inputs[0] : diperlukan: tensor aktivasi input inputs[1] : diperlukan: inputs[2] : opsional: tensor bias

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult , quant::AccumulatorUniformScale<2, 0, 1> , quant::AffineOpCoefficient<0, 1>

Antarmuka: AffineQuantizedOpInterface , ConditionallySpeculatable , DynamicRangeQuantizedOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface) , TFL_SparseOp , TflArithmeticCountOpInterface , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dilation_h_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_w_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
padding	::mlir::StringAttr	atribut string yang nilainya SAMA, atau VALID
stride_h	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan	Keterangan
input	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16
filter	tensor nilai tipe float atau QI4 32-bit atau tipe QI8 atau tipe QUI8
bias	tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16

tfl.conv_3d (TFL::Konv3DOp)

Operator 3D konvolusi

Melakukan operasi konvolusi pada input 3D. Input: inputs[0] : diperlukan: tensor aktivasi input inputs[1] : diperlukan: inputs[2] : opsional: tensor bias

Sifat: AlwaysSpeculatableImplTrait , quant::AccumulatorUniformScale<2, 0, 1>

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dilation_d_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_h_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_w_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
padding	::mlir::StringAttr	atribut string yang nilainya SAMA, atau VALID
stride_d	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_h	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan	Keterangan
input	tensor nilai float 32-bit
filter	tensor nilai float 32-bit
bias	tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit

tfl.conv_3d_transpose (TFL::Conv3DTransposeOp)

Operator 3D Konvolusi yang Dialihkan

Melakukan operasi konvolusi yang dialihkan pada input 3D. Inputs: inputs[0] : diperlukan: bentuk tensor keluaran inputs[1] : diperlukan: filter tensor bobot inputs[2] : diperlukan: aktivasi masukan tensor inputs[3] : opsional: tensor bias

Sifat: AlwaysSpeculatableImplTrait , quant::AccumulatorUniformScale<2, 0, 1>

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dilation_d_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_h_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_w_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
padding	::mlir::StringAttr	atribut string yang nilainya SAMA, atau VALID
stride_d	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_h	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan	Keterangan
output_shape	tensor nilai integer tanpa tanda 32-bit
filter	tensor nilai float 32-bit
input	tensor nilai float 32-bit
bias	tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit

tfl.cos (TFL::CosOp)

Operator kosinus

Menghitung kosinus input berdasarkan elemen

Sifat: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor nilai float 32-bit

Hasil:

Hasil	Keterangan
y	tensor nilai float 32-bit

tfl.cumsum (TFL::CumsumOp)

Operator cumsum

Hitung jumlah kumulatif tensor x sepanjang sumbu.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
exclusive	::mlir::BoolAttr	atribut bool
reverse	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
input	tensor nilai integer tanpa tanda 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit
axis	tensor nilai integer tanpa tanda 32-bit

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit

tfl.custom (TFL::CustomOp)

Operasi khusus

Operasi umum untuk operasi kustom TFLite apa pun.

input: Daftar input dalam operasi asli. custom_code: String yang digunakan untuk mengidentifikasi operasi mana yang sebenarnya, yang sesuai dengan operator_codes.custom_code di flatbuffer. custom_option: pemegang untuk menyimpan atribut op dalam mode byte. keluaran: Daftar keluaran dalam operasi asli.

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
custom_code	::mlir::StringAttr	atribut string
custom_option	::mlir::TFL::ConstBytesAttr	Representasi atribut string dari byte yang dikompilasi

Operan:

Operan	Keterangan
input	variadik tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	variadik tensor nilai jenis apa pun

tfl.custom_tf (TFL::CustomTfOp)

Operasi Pembungkus untuk operasi khusus TF.

Operasi pembungkus di sekitar operasi TF Kustom apa pun. Ini termasuk operasi yang ditentukan menggunakan custom_opdefs atau tertaut yang tidak ditentukan dalam dialek TF. Operasi ini hanya menggabungkan operasi khusus di dalam suatu wilayah. Catatan #1, Operasi ini tidak akan menyertakan operasi kustom TF Lite yang ditentukan menggunakan CustomOp. Catatan #2, operasi ini hanyalah representasi internal di dalam konverter dan tidak diekspos/diekspor saat model diekspor ke Flatbuffer.

Sifat: IsolatedFromAbove , RecursiveMemoryEffects , SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Antarmuka: InferTypeOpInterface , TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
input	variadik tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	variadik tensor nilai jenis apa pun

tfl.densify (TFL::DensifyOp)

Padatkan operator

Mengonversi tensor renggang menjadi format padat.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor nilai integer 32-bit float atau 8-bit signless

Hasil:

Hasil	Keterangan
output	tensor nilai integer 32-bit float atau 8-bit signless

tfl.depth_to_space (TFL::DepthToSpaceOp)

Operator DepthToSpace

Menyusun ulang data dari kedalaman menjadi blok data spasial. Ini adalah transformasi kebalikan dari SpaceToDepth. Lebih khusus lagi, operasi ini menghasilkan salinan tensor masukan di mana nilai dari dimensi depth dipindahkan dalam blok spasial ke dimensi height dan width . attr block_size menunjukkan ukuran blok masukan dan cara data dipindahkan.

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
block_size	::mlir::IntegerAttr	Atribut bilangan bulat tak bertanda 32-bit yang nilainya positif

Operan:

Operan	Keterangan
input	tensor bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe TFLite quint8 atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe TFLite quint8 atau bilangan bulat tak bertanda 8-bit atau nilai tipe QI8 atau tipe QUI8

tfl.depthwise_conv_2d (TFL::DepthwiseConv2DOp)

Operator konvolusi yang dapat dipisahkan secara mendalam

Melakukan operasi konvolusi pada input.

Input: inputs[0] : diperlukan: tensor aktivasi input inputs[1] : diperlukan: inputs[2] : opsional: tensor bias

Sifat: AlwaysSpeculatableImplTrait , QuantizableResult , quant::AccumulatorUniformScale<2, 0, 1> , quant::AffineOpCoefficient<3, 1>

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dilation_h_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
dilation_w_factor	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT
padding	::mlir::StringAttr	atribut string yang nilainya SAMA, atau VALID
stride_h	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit
depth_multiplier	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan	Keterangan
input	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16
filter	tensor nilai tipe float atau QI4 32-bit atau tipe QI8 atau tipe QUI8
bias	tensor nilai tipe apa pun atau tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor nilai tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16

tfl.dequantize (TFL::DequantizeOp)

Dekuantisasi operator

Mengonversi array bilangan bulat terkuantisasi menjadi floating-point sesuai dengan parameter kuantisasi.

Antarmuka: NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor tipe QI4 atau tipe QI8 atau tipe QUI8 atau tipe QI16 atau nilai float 16-bit

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit

tfl.dilate (TFL::DilatOp)

Operator pelebaran

Memperluas tensor dengan menambahkan elemen baru di antara elemen yang sudah ada.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau Nilai float 32-bit atau nilai float 64-bit
dilations	tensor nilai integer tanpa tanda 32-bit
padding_value	Tensor 0D dari nilai jenis apa pun

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau Nilai float 32-bit atau nilai float 64-bit

tfl.div (TFL::DivOp)

Operator divisi

Operasi pembagian berdasarkan elemen.

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait , AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::StringAttr	atribut string yang nilainya NONE, atau RELU, atau RELU_N1_TO_1, atau RELU6, atau TANH, atau SIGN_BIT

Operan:

Operan	Keterangan
lhs	tensor nilai tipe float 32-bit atau bilangan bulat tanpa tanda 32-bit atau QUI8
rhs	tensor nilai tipe float 32-bit atau bilangan bulat tanpa tanda 32-bit atau QUI8

Hasil:

Hasil	Keterangan
output	tensor nilai tipe float 32-bit atau bilangan bulat tanpa tanda 32-bit atau QUI8

tfl.dynamic_update_slice (TFL::DynamicUpdateSliceOp)

Irisan Pembaruan Dinamis.

Operasi DynamicUpdateSlice yang memiliki semantik yang sama dengan XLA DynamicUpdateSlice. Menghasilkan hasil yang merupakan nilai operan larik masukan, dengan pembaruan irisan ditimpa di start_indices.

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	Tensor bilangan bulat 1-bit bitless atau integer 8-bit atau integer tertandai atau 32-bit integer atau 64-bit bilangan bulat atau 32-bit float atau 16-bit float nilai
update	Tensor bilangan bulat 1-bit bitless atau integer 8-bit atau integer tertandai atau 32-bit integer atau 64-bit bilangan bulat atau 32-bit float atau 16-bit float nilai
start_indices	Tensor nilai integer 32/64-bit tanda tangan

Hasil:

Hasil	Keterangan
output	Tensor bilangan bulat 1-bit bitless atau integer 8-bit atau integer tertandai atau 32-bit integer atau 64-bit bilangan bulat atau 32-bit float atau 16-bit float nilai

tfl.elu (tfl :: eluop)

Operator Unit Linear Eksponensial

Menghitung linear eksponensial F (x) -> exp (x) -1 untuk x <0, x untuk x> = 0. Elemen -bijaksana.

Ciri -ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	Tensor dari 32-bit float atau 8-bit nilai integer tanpa tanda

Hasil:

Hasil	Keterangan
y	Tensor dari 32-bit float atau 8-bit nilai integer tanpa tanda

tfl.embedding_lookup (tfl :: embeddinglookupop)

Operator pencarian yang menanamkan

Mencari ID dalam daftar tensor yang menanamkan.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lookup	Tensor Nilai Integer Tanda Tanda Tanda Tanda
value	Tensor float 32-bit atau integer tanpa tanda 8-bit atau 8-bit unsigned integer atau qi8 tipe atau nilai tipe qi8 atau qi4 tipe

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau 8-bit integer tanpa tanda atau nilai integer unsigned 8-bit

tfl.equal (tfl :: equalop)

Operator yang sama

Mengembalikan elemen kebenaran x == y elemen bijaksana

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait ResultsBroadcastableShape AlwaysSpeculatableImplTrait , Commutative , QuantizableResult ,

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor bilangan bulat 1-bit bitless atau float 32-bit atau 16-bit integer sutra atau 32-bit integer sutra atau 64-bit integer atau tipe qi8 atau tipe qii8 atau 8-bit unsigned integer atau nilai tipe string tflite string tflite tflite
y	tensor bilangan bulat 1-bit bitless atau float 32-bit atau 16-bit integer sutra atau 32-bit integer sutra atau 64-bit integer atau tipe qi8 atau tipe qii8 atau 8-bit unsigned integer atau nilai tipe string tflite string tflite tflite

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.exp (tfl :: expop)

Operator eksponensial alami

Melakukan operasi eksponensial alami yang bijaksana pada input.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	Tensor 32-bit float atau qi8 tipe atau nilai tipe Qi16

Hasil:

Hasil	Keterangan
y	Tensor 32-bit float atau qi8 tipe atau nilai tipe Qi16

tfl.expand_dims (tfl :: expanddimsop)

Memasukkan dimensi 1 ke dalam bentuk tensor.

Diberi input tensor, operasi ini memasukkan dimensi 1 pada axis indeks dimensi bentuk input . axis indeks dimensi dimulai pada nol; Jika Anda menentukan angka negatif untuk axis itu dihitung mundur dari ujungnya.

Operasi ini berguna jika Anda ingin menambahkan dimensi batch ke satu elemen. Misalnya, jika Anda memiliki satu gambar bentuk [height, width, channels] , Anda dapat menjadikannya batch 1 gambar dengan expand_dims(image, 0) , yang akan membuat bentuk [1, height, width, channels] .

Contoh lain:

# '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]

Operasi ini mensyaratkan itu:

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

Operasi ini terkait dengan squeeze() , yang menghilangkan dimensi ukuran 1.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor nilai jenis apa pun
dim	Tensor nilai integer 32/64-bit tanda tangan

Hasil:

Hasil	Keterangan
output	tensor nilai jenis apa pun

tfl.external_const (tfl :: externalconstop)

Op Konstitusi Eksternal.

Eksternal const op memegang buffer_index yang menunjuk ke konstan di flatbuffer.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
buffer_index	:: mlir :: integerattr	Atribut integer 32-bit

Hasil:

Hasil	Keterangan
output	tensor nilai jenis apa pun

tfl.fake_quant (tfl :: fakequantop)

Operator palsu

Palsu-quatize tensor 'input' tipe float melalui float skalar min dan max ke 'output' tensor dengan bentuk yang sama dengan input.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
min	:: mlir :: floatattr	Atribut float 32-bit
max	:: mlir :: floatattr	Atribut float 32-bit
num_bits	:: mlir :: integerattr	Atribut integer 32-bit yang ditandakan yang nilai minimumnya adalah 2 yang nilai maksimumnya adalah 16
narrow_range	:: mlir :: boolattr	atribut bool yang nilainya salah

Operan:

Operan	Keterangan
input	Tensor nilai float 32-bit

Hasil:

Hasil	Keterangan
output	Tensor nilai float 32-bit

tfl.fill (tfl :: fillop)

Isi tensor dengan nilai yang diberikan.

Isi tensor dengan nilai yang diberikan.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
dims	Tensor nilai integer 32/64-bit tanda tangan
input	tensor float 32-bit atau 16-bit float atau 32-bit integer suthless atau 64-bit integer silsless atau 1-bit integer atau qi8 tipe atau qi16 tipe atau nilai tipe string tflite tflite tflite

Hasil:

Hasil	Keterangan
result	tensor float 32-bit atau 16-bit float atau 32-bit integer suthless atau 64-bit integer silsless atau 1-bit integer atau qi8 tipe atau qi16 tipe atau nilai tipe string tflite tflite tflite

tfl.floor (tfl :: floatop)

Operator lantai

Mengembalikan nilai lantai input yang bijaksana.

Ciri -ciri: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	Tensor nilai float 32-bit

Hasil:

Hasil	Keterangan
y	Tensor nilai float 32-bit

tfl.floor_div (tfl :: floordivop)

Operator Div Lantai

Operasi Div Lantai-Wise-Wise.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait ResultsBroadcastableShape AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor float 32-bit atau 8-bit integer tanpa tanda atau 16-bit integer tanda atau 32-bit nilai integer tanda tangan
rhs	tensor float 32-bit atau 8-bit integer tanpa tanda atau 16-bit integer tanda atau 32-bit nilai integer tanda tangan

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau 8-bit integer tanpa tanda atau 16-bit integer tanda atau 32-bit nilai integer tanda tangan

tfl.floor_mod (tfl :: floorModop)

Pengingat Divisi

Operasi Pengingat Divisi Elemen.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait ResultsBroadcastableShape AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor integer 8-bit integer atau 16-bit integer atau integer tertandai atau 32-bit integer atau 64-bit integer atau nilai float 32-bit float 32-bit
rhs	tensor integer 8-bit integer atau 16-bit integer atau integer tertandai atau 32-bit integer atau 64-bit integer atau nilai float 32-bit float 32-bit

Hasil:

Hasil	Keterangan
output	tensor integer 8-bit integer atau 16-bit integer atau integer tertandai atau 32-bit integer atau 64-bit integer atau nilai float 32-bit float 32-bit

tfl.fully_connected (tfl :: fullConnectedop)

OP yang sepenuhnya terhubung

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , quant::AccumulatorUniformScale<2, 0, 1> , quant::AffineOpCoefficient<0, 1>

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
fused_activation_function	:: mlir :: stringattr	atribut string yang nilainya tidak ada, atau relu, atau relu_n1_to_1, atau relu6, atau tanh, atau Sign_bit
weights_format	:: mlir :: stringattr	atribut string yang nilainya default, atau shuffled4x16int8
keep_num_dims	:: mlir :: boolattr	atribut bool
asymmetric_quantize_inputs	:: mlir :: boolattr	atribut bool

Operan:

Operan	Keterangan
input	tensor 32-bit float atau qi8 type atau qui8 type atau qi16 type atau qui16 tipe nilai
filter	tensor 32-bit float atau qi4 type atau qi8 type atau qui8 type atau qi16 tipe nilai
bias	tensor nilai jenis apa pun atau tidak sama sekali

Hasil:

Hasil	Keterangan
output	Variadik tensor dari nilai jenis apa pun

tfl.gather (tfl :: gatherop)

Kumpulkan operator

Kumpulkan irisan dari axis poros params sesuai dengan indices .

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
axis	:: mlir :: integerattr	Atribut integer 32-bit
batch_dims	:: mlir :: integerattr	Atribut integer 32-bit

Operan:

Operan	Keterangan
params	tensor float 32-bit atau 1-bit integer tanpa tanda atau 4-bit integer tanpa tanda atau integer 8-bit integer atau integer tanda-bit atau integer tanda-tanda atau tipe string tertandai atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit STIGLESS STIGLE atau TFLITE STRING atau 8-bit Jenis Integer atau Qi8 Tipe atau Jenis QI8 atau QI16 Jenis Nilai
indices	Tensor 16-bit Integer Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Sandat atau 64-bit Nilai Integer Tanda Tanda Tanda

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau 1-bit integer tanpa tanda atau 4-bit integer tanpa tanda atau integer 8-bit integer atau integer tanda-bit atau integer tanda-tanda atau tipe string tertandai atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit atau 84-bit STIGLESS STIGLE atau TFLITE STRING atau 8-bit Jenis Integer atau Qi8 Tipe atau Jenis QI8 atau QI16 Jenis Nilai

tfl.gather_nd (tfl :: callingndop)

_Gather ND Operator

Kumpulkan irisan dari params menjadi tensor dengan bentuk yang ditentukan oleh indices .

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
params	tensor float 32-bit atau 1-bit integer tanpa tanda atau 8-bit integer tanpa tanda atau 16-bit integer sutra atau integer 64-bit integer atau 32-bit integer atau nilai tipe string tanpa tanda atau 8-bit unsigned atau qi8 atau qi8 atau tflite string tipe string
indices	Tensor 16-bit Integer Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Tanda Sandat atau 64-bit Nilai Integer Tanda Tanda Tanda

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau 1-bit integer tanpa tanda atau 8-bit integer tanpa tanda atau 16-bit integer sutra atau integer 64-bit integer atau 32-bit integer atau nilai tipe string tanpa tanda atau 8-bit unsigned atau qi8 atau qi8 atau tflite string tipe string

tfl.gelu (tfl :: geluop)

Fungsi Aktivasi Gelu.

Menghitung elemen fungsi aktivasi gelu-bijaksana.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
approximate	:: mlir :: boolattr	atribut bool

Operan:

Operan	Keterangan
input	Tensor 32-bit float atau qi8 tipe atau nilai tipe QUI8

Hasil:

Hasil	Keterangan
output	Tensor 32-bit float atau qi8 tipe atau nilai tipe QUI8

tfl.greater (tfl :: greatop)

Operator yang lebih besar

Operasi yang lebih besar dari elemen.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait AlwaysSpeculatableImplTrait ResultsBroadcastableShape QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor 32-bit float atau 32-bit integer tanpa tanda atau 64-bit integer tanpa tanda atau tipe qii8 atau tipe qi8 atau nilai tipe quint8 tflite
rhs	Tensor 32-bit float atau 32-bit integer tanpa tanda atau 64-bit integer tanpa tanda atau tipe qii8 atau tipe qi8 atau nilai tipe quint8 tflite

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.greater_equal (tfl :: greaterequalop)

_ Operator yang sama

Operasi elemen-bijaksana Greater_Equal.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait AlwaysSpeculatableImplTrait ResultsBroadcastableShape QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor float 32-bit atau integer standless 16-bit atau 32-bit integer atau 64-bit integer atau nilai tipe qii8 atau qi8 tipe qi8
rhs	tensor float 32-bit atau integer standless 16-bit atau 32-bit integer atau 64-bit integer atau nilai tipe qii8 atau qi8 tipe qi8

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.hard_swish (tfl :: hardswishop)

Fungsi aktivasi hardswish.

Menghitung fungsi aktivasi keras-Swish f (x)-> (x * relu6 (x+3))/6 elemen-bijaksana.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	Tensor nilai 32-bit float atau qui8 atau qi8 tipe

Hasil:

Hasil	Keterangan
output	Tensor nilai 32-bit float atau qui8 atau qi8 tipe

tfl.hashtable (tfl :: hashtableop)

Membuat tabel hash yang tidak diinisialisasi.

OP ini membuat tabel hash, menentukan jenis kunci dan nilainya. Sebelum menggunakan tabel, Anda harus menginisialisasi. Setelah inisialisasi tabel akan tidak berubah.

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

Atribut	Jenis MLIR	Keterangan
table_id	:: mlir :: integerattr	Atribut integer 32-bit
key_dtype	:: mlir :: typeattr	atribut jenis apa pun
value_dtype	:: mlir :: typeattr	atribut jenis apa pun

Hasil:

Hasil	Keterangan
out	Tensor nilai sumber daya

tfl.hashtable_find (tfl :: hashtablefindop)

Mencari kunci dalam tabel, mengeluarkan nilai yang sesuai.

keys tensor harus dari jenis yang sama dengan kunci tabel. values output adalah jenis nilai tabel.

Scalar default_value adalah output nilai untuk kunci yang tidak ada dalam tabel. Itu juga harus memiliki tipe yang sama dengan nilai tabel.

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	Tensor nilai sumber daya
keys	tensor 32-bit integer tanpa tanda atau tipe string tflite atau nilai integer 64-bit tanda tangan
default_value	Tensor float 32-bit atau 32-bit integer tanpa tanda atau tipe string TFLITE atau nilai integer tertandat 64-bit

Hasil:

Hasil	Keterangan
out	Tensor float 32-bit atau 32-bit integer tanpa tanda atau tipe string TFLITE atau nilai integer tertandat 64-bit

tfl.hashtable_import (tfl :: hashtableimportop)

Menggantikan isi tabel dengan kunci dan nilai yang ditentukan.

keys tensor harus memiliki jenis yang sama dengan kunci tabel. values tensor harus dari jenis nilai tabel.

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	Tensor nilai sumber daya
keys	tensor 32-bit integer tanpa tanda atau tipe string tflite atau nilai integer 64-bit tanda tangan
values	Tensor float 32-bit atau 32-bit integer tanpa tanda atau tipe string TFLITE atau nilai integer tertandat 64-bit

tfl.hashtable_size (tfl :: hashtablessizeop)

Menghitung jumlah elemen dalam tabel yang diberikan.

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	Tensor nilai sumber daya

Hasil:

Hasil	Keterangan
out	Tensor dari nilai integer 64-bit yang tidak bertanda

tfl.if (tfl :: ifop)

Operasi If-Then-Else

Operasi tfl.if mewakili konstruk jika-kemudian-else untuk mengeksekusi secara kondisional dua wilayah kode. Operan ke operasi IF adalah nilai boolean. Misalnya:

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

tfl.if juga dapat mengembalikan hasil yang didefinisikan di daerahnya. Nilai -nilai yang ditentukan ditentukan oleh jalur eksekusi yang diambil.

Contoh:

%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 daerah selalu diakhiri dengan "tfl.yield". Jika "tfl.if" tidak mendefinisikan nilai, "tfl.yield" dapat ditinggalkan, dan akan dimasukkan secara implisit. Kalau tidak, itu harus eksplisit. Juga, jika "tfl.jika" mendefinisikan satu atau lebih nilai, blok 'lain' tidak dapat dihilangkan.

Contoh:

tfl.if %b  {
  ...
}

Ciri -ciri: NoRegionArguments , RecursiveMemoryEffects , SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Antarmuka: RegionBranchOpInterface , TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
cond	Tensor nilai integer 1-bit tanpa tanda

Hasil:

Hasil	Keterangan
results	Variadik tensor dari nilai jenis apa pun

tfl.imag (tfl :: imagop)

Mengembalikan bagian imajiner dari bilangan kompleks.

Dengan input tensor bilangan kompleks, operasi ini mengembalikan tensor tipe float yang merupakan bagian imajiner dari setiap elemen dalam input . Semua elemen dalam input harus berupa bilangan formulir yang kompleks \(a + bj\), di mana A adalah bagian nyata dan B adalah bagian imajiner yang dikembalikan oleh operasi ini.

Ciri -ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor tipe kompleks dengan elemen float 32-bit atau tipe kompleks dengan nilai elemen float 64-bit

Hasil:

Hasil	Keterangan
output	Tensor nilai pelampung 32-bit atau 64-bit float

tfl.l2_normalization (tfl :: l2normalizationop)

L2 menormalkan operator

L2normalisasi op

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
fused_activation_function	:: mlir :: stringattr	atribut string yang nilainya tidak ada, atau relu, atau relu_n1_to_1, atau relu6, atau tanh, atau Sign_bit

Operan:

Operan	Keterangan
input	tensor 32-bit float atau tipe qii8 atau qi8 tipe atau tipe qui16 atau qi16 tipe atau 8-bit nilai integer tanda tangan

Hasil:

Hasil	Keterangan
output	tensor 32-bit float atau tipe qii8 atau qi8 tipe atau tipe qui16 atau qi16 tipe atau 8-bit nilai integer tanda tangan

tfl.leaky_relu (tfl :: leakyreluop)

Operator Relu Bocor

Operator Relu Bocor Elemen -bijaksana X -> X> = 0? x: (alpha * x)

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
alpha	:: mlir :: floatattr	Atribut float 32-bit

Operan:

Operan	Keterangan
input	tensor 32-bit float atau qui8 type atau qi8 type atau tflite quint8 type atau qi16 tipe nilai

Hasil:

Hasil	Keterangan
output	tensor 32-bit float atau qui8 type atau qi8 type atau tflite quint8 type atau qi16 tipe nilai

tfl.less (tfl :: lessop)

Lebih sedikit operator

Elemen-bijaksana kurang operasi.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait AlwaysSpeculatableImplTrait ResultsBroadcastableShape QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor 32-bit float atau 16-bit integer tanpa tanda atau 32-bit integer tanpa tanda atau 64-bit integer sutra atau tipe qui8 atau tipe qi8 atau nilai tipe quint8 tflite
rhs	Tensor 32-bit float atau 16-bit integer tanpa tanda atau 32-bit integer tanpa tanda atau 64-bit integer sutra atau tipe qui8 atau tipe qi8 atau nilai tipe quint8 tflite

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.less_equal (tfl :: lessequalop)

_Less operator yang sama

Operasi Less_Equal elemen-bijaksana.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait AlwaysSpeculatableImplTrait ResultsBroadcastableShape QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor float 32-bit atau integer tanpa tanda 32-bit atau 64-bit integer atau qi8 tipe atau nilai tipe QUI8
rhs	Tensor float 32-bit atau integer tanpa tanda 32-bit atau 64-bit integer atau qi8 tipe atau nilai tipe QUI8

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.local_response_normalization (tfl :: localresponsenormalizationop)

Normalisasi respons lokal.

Tensor input 4-D diperlakukan sebagai array 3-D vektor 1-D (sepanjang dimensi terakhir), dan setiap vektor dinormalisasi secara independen. Dalam vektor yang diberikan, setiap komponen dibagi dengan jumlah input yang tertimbang, kuadrat dalam depth_radius . Secara terperinci,

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

Untuk detailnya, lihat Krizhevsky et al., Klasifikasi Imagenet dengan jaringan saraf konvolusional yang mendalam (NIPS 2012) .

Ciri -ciri: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
radius	:: mlir :: integerattr	Atribut integer 32-bit
bias	:: mlir :: floatattr	Atribut float 32-bit
alpha	:: mlir :: floatattr	Atribut float 32-bit
beta	:: mlir :: floatattr	Atribut float 32-bit

Operan:

Operan	Keterangan
input	Tensor nilai float 32-bit

Hasil:

Hasil	Keterangan
output	Tensor nilai float 32-bit

tfl.log (tfl :: logop)

Operator logaritma alami

Melakukan operasi logaritma alami yang bijaksana pada input.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	Tensor nilai tipe float 32-bit atau qi8

Hasil:

Hasil	Keterangan
y	Tensor nilai tipe float 32-bit atau qi8

tfl.log_softmax (tfl :: logsoftmaxop)

Operator Log Softmax

Menghitung aktivasi log softmax log-wise dengan rumus berikut

Input - Log (redike_sum (exp (input), DIM))

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor 32-bit float atau qui8 type atau qi8 type atau tflite quint8 value

Hasil:

Hasil	Keterangan
output	tensor 32-bit float atau qui8 type atau qi8 type atau tflite quint8 value

tfl.logical_and (tfl :: LogicalAndop)

Logis dan operator

Logis dan Operasi Elemen-bijaksana.

Ciri -ciri: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor nilai integer 1-bit tanpa tanda
rhs	Tensor nilai integer 1-bit tanpa tanda

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.logical_not (tfl :: LogicalNotop)

Logis bukan operator

Elemen-bijaksana logis bukan operasi.

Ciri -ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor nilai integer 1-bit tanpa tanda

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.logical_or (TFL :: Logicalorop)

Logis atau operator

Logis atau operasi elemen.

Ciri -ciri: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	Tensor nilai integer 1-bit tanpa tanda
rhs	Tensor nilai integer 1-bit tanpa tanda

Hasil:

Hasil	Keterangan
output	Tensor nilai integer 1-bit tanpa tanda

tfl.logistic (tfl :: logisticop)

Operator logistik

Menghitung input elemen bijaksana

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor 32-bit float atau qi8 type atau qui8 type atau qi16 type atau tflite quint8 values

Hasil:

Hasil	Keterangan
y	tensor 32-bit float atau qi8 type atau qui8 type atau qi16 type atau tflite quint8 values

tfl.lstm (tfl :: lstmop)

Operator LSTM lengkap

Lapisan Jaringan Unit Berulang Lama (LSTM) Berulang. Implementasi non-peephole default didasarkan pada: http://deeplearning.cs.cmu.edu/pdfs/hochreiter97_lstm.pdf S. Hochreiter dan J. Schmidhuber. 'Memori jangka pendek yang panjang'. Komputasi Saraf, 9 (8): 1735-1780, 1997. Implementasi Peephole didasarkan pada: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, dan Francoise Beaufays. 'Arsitektur jaringan saraf berulang memori jangka pendek untuk pemodelan akustik skala besar.' Interspeech, 2014. Kopling input dan lupa gerbang (CIFG) didasarkan pada: http://arxiv.org/pdf/1503.04069.pdf Greff et al. 'LSTM: ruang pencarian Odyssey' Normalisasi lapisan didasarkan pada: https://arxiv.org/pdf/1607.06450.pdf Ba et al. 'Normalisasi lapisan'

Ciri -ciri: QuantizableResult

Antarmuka: DynamicRangeQuantizedOpInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

Atribut:

Atribut	Jenis MLIR	Keterangan
fused_activation_function	:: mlir :: stringattr	atribut string yang nilainya tidak ada, atau relu, atau relu_n1_to_1, atau relu6, atau tanh, atau Sign_bit
cell_clip	:: mlir :: floatattr	Atribut float 32-bit yang nilainya non-negatif
proj_clip	:: mlir :: floatattr	Atribut float 32-bit yang nilainya non-negatif
kernel_type	:: mlir :: tfl :: lstmkerneltypeattr	lstm_kernel_type yang nilainya mlir :: tfl :: lstmkerneltype :: full
asymmetric_quantize_inputs	:: mlir :: boolattr	atribut bool
input_to_input_intermediate	:: mlir :: typeattr	atribut jenis apa pun
input_to_forget_intermediate	:: mlir :: typeattr	atribut jenis apa pun
input_to_cell_intermediate	:: mlir :: typeattr	atribut jenis apa pun
input_to_output_intermediate	:: mlir :: typeattr	atribut jenis apa pun
effective_hidden_scale_intermediate	:: mlir :: typeattr	atribut jenis apa pun

Operan:

Operan	Keterangan
input	Tensor 32-bit float atau qi8 tipe atau nilai tipe Qi16
input_to_input_weights	tensor nilai jenis apa pun atau tidak sama sekali
input_to_forget_weights	Tensor nilai tipe float 32-bit atau qi8
input_to_cell_weights	Tensor nilai tipe float 32-bit atau qi8
input_to_output_weights	Tensor nilai tipe float 32-bit atau qi8
recurrent_to_input_weights	tensor nilai jenis apa pun atau tidak sama sekali
recurrent_to_forget_weights	Tensor nilai tipe float 32-bit atau qi8
recurrent_to_cell_weights	Tensor nilai tipe float 32-bit atau qi8
recurrent_to_output_weights	Tensor nilai tipe float 32-bit atau qi8
cell_to_input_weights	tensor nilai jenis apa pun atau tidak sama sekali
cell_to_forget_weights	tensor nilai jenis apa pun atau tidak sama sekali
cell_to_output_weights	tensor nilai jenis apa pun atau tidak sama sekali
input_gate_bias	tensor nilai jenis apa pun atau tidak sama sekali
forget_gate_bias	Tensor nilai tipe float 32-bit atau Qi32
cell_bias	Tensor nilai tipe float 32-bit atau Qi32
output_gate_bias	Tensor nilai tipe float 32-bit atau Qi32
projection_weights	tensor nilai jenis apa pun atau tidak sama sekali
projection_bias	tensor nilai jenis apa pun atau tidak sama sekali
input_activation_state	Tensor yang stateful
input_cell_state	Tensor yang stateful
input_layer_norm_coefficients	tensor nilai jenis apa pun atau tidak sama sekali
forget_layer_norm_coefficients	tensor nilai jenis apa pun atau tidak sama sekali
cell_layer_norm_coefficients	tensor nilai jenis apa pun atau tidak sama sekali
output_layer_norm_coefficients	tensor nilai jenis apa pun atau tidak sama sekali

Hasil:

Hasil	Keterangan
output	tensor nilai jenis apa pun

tfl.matrix_diag (tfl :: matrixdiagop)

Mengembalikan tensor dengan diagonal yang disediakan dan yang lainnya empuk dengan nol.

Diberi diagonal, mengembalikan tensor dengan diagonal dan yang lainnya empuk dengan nol. Asumsikan diagonal memiliki dimensi k [I, J, K, ..., N] , maka outputnya adalah tensor peringkat k+1 dengan dimensi [I, J, K, ..., N, N] di mana: output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n].

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
diagonal	tensor float 32-bit atau 8-bit integer tanpa tanda atau 16-bit integer tanpa tanda atau 32-bit integer silsless atau integer 64-bit integer sutra atau integer tidak ditandatangani atau tipe tipe qi8 atau tipe qi8 atau tflite quint8 tflite quint8

Hasil:

Hasil	Keterangan
output	tensor float 32-bit atau 8-bit integer tanpa tanda atau 16-bit integer tanpa tanda atau 32-bit integer silsless atau integer atau 64-bit integer atau nilai integer unsigned atau tipe qi8 tipe atau tipe qi8 atau tflite quint8

tfl.matrix_set_diag (tfl :: matrixsetDiagop)

Mengembalikan tensor matriks batch dengan nilai diagonal batched baru.

Diberi input dan diagonal , operasi ini mengembalikan tensor dengan bentuk dan nilai yang sama seperti input , kecuali untuk diagonal utama dari matriks terdalam. Ini akan ditimpa oleh nilai -nilai di diagonal .

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor float 32-bit atau 8-bit integer tanpa tanda atau integer 16-bit bilangan bulat atau integer 32-bit atau integer tanpa tanda atau bitteger 64-bit atau jenis integer unsigned atau tipe qi8 atau tipe qi16 atau tipe qui8 atau nilai tipe quint8 tflite quint8 tflite qiint8
diagonal	tensor float 32-bit atau 8-bit integer tanpa tanda atau integer 16-bit bilangan bulat atau integer 32-bit atau integer tanpa tanda atau bitteger 64-bit atau jenis integer unsigned atau tipe qi8 atau tipe qi16 atau tipe qui8 atau nilai tipe quint8 tflite quint8 tflite qiint8

Hasil:

Hasil	Keterangan
result	tensor float 32-bit atau 8-bit integer tanpa tanda atau integer 16-bit bilangan bulat atau integer 32-bit atau integer tanpa tanda atau bitteger 64-bit atau jenis integer unsigned atau tipe qi8 atau tipe qi16 atau tipe qui8 atau nilai tipe quint8 tflite quint8 tflite qiint8

tfl.max_pool_2d (tfl :: maxpool2dop)

Max Pool 2D OP

Melakukan Max Pool 2D pada Input.

Input: inputs[0] : Diperlukan: Input Tensor

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
padding	:: mlir :: stringattr	atribut string yang nilainya sama, atau valid
stride_w	:: mlir :: integerattr	Atribut integer 32-bit
stride_h	:: mlir :: integerattr	Atribut integer 32-bit
filter_width	:: mlir :: integerattr	Atribut integer 32-bit
filter_height	:: mlir :: integerattr	Atribut integer 32-bit
fused_activation_function	:: mlir :: stringattr	atribut string yang nilainya tidak ada, atau relu, atau relu_n1_to_1, atau relu6, atau tanh, atau Sign_bit

Operan:

Operan	Keterangan
input	tensor 32-bit float atau qui8 type atau qi8 type atau qi16 type atau tflite quint8 values

Hasil:

Hasil	Keterangan
output	tensor 32-bit float atau qui8 type atau qi8 type atau qi16 type atau tflite quint8 values

tfl.maximum (tfl :: maksimum)

Operator Max

Operasi maksimum elemen-bijaksana.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait ResultsBroadcastableShape AlwaysSpeculatableImplTrait , Commutative , QuantizableResult ,

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor float 32-bit atau 32/64-bit integer atau qi8 tipe atau qui8 tipe atau nilai tipe qi16
rhs	tensor float 32-bit atau 32/64-bit integer atau qi8 tipe atau qui8 tipe atau nilai tipe qi16

Hasil:

Hasil	Keterangan
max	tensor float 32-bit atau 32/64-bit integer atau qi8 tipe atau qui8 tipe atau nilai tipe qi16

tfl.mean (tfl :: meanop)

Operator berarti

Menghitung rata -rata elemen lintas dimensi tensor. Mengurangi input_tensor di sepanjang dimensi yang diberikan dalam sumbu. Kecuali KeepDims benar, peringkat tensor dikurangi dengan 1 untuk setiap entri dalam sumbu. Jika KeepDims benar, dimensi yang dikurangi dipertahankan dengan panjang 1.

Ciri -ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Jenis MLIR	Keterangan
keep_dims	:: mlir :: boolattr	atribut bool

Operan:

Operan	Keterangan
input	tensor 32-bit float atau 32-bit integer tanpa tanda atau 64-bit integer tanpa tanda atau tipe qi8 atau tipe qui8 atau 8-bit unsigned integer atau qi16 jenis nilai
axis	Tensor Nilai Integer Tanda Tanda Tanda Tanda

Hasil:

Hasil	Keterangan
output	tensor 32-bit float atau 32-bit integer tanpa tanda atau 64-bit integer tanpa tanda atau tipe qi8 atau tipe qui8 atau 8-bit unsigned integer atau qi16 jenis nilai

tfl.minimum (tfl :: minimumop)

Operator min

Operasi Min-Wise Min.

Ciri -ciri: ::mlir::OpTrait::TFLRuntimeOpTrait ResultsBroadcastableShape AlwaysSpeculatableImplTrait , Commutative , QuantizableResult ,

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

Effects: MemoryEffects::Effect{}

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
mode	::mlir::TFL::MirrorPaddingTypeAttr	mirror_pad_enum

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
seed	::mlir::IntegerAttr	64-bit signless integer attribute
seed2	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:

Operand	Keterangan
logits	tensor of 32-bit float values
num_samples	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
value	::mlir::UnitAttr	unit attribute

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
tolerance	::mlir::FloatAttr	32-bit float attribute
log_if_failed	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
axis	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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) . Dll.

Misalnya:

# '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{}

Atribut:

Atribut	MLIR Type	Keterangan
values_count	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive
axis	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:

Operand	Keterangan
values	variadic of tensor of any type values

Results:

Hasil	Keterangan
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)

Misalnya:

# '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	Keterangan
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:

Hasil	Keterangan
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)

Misalnya:

# '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	Keterangan
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:

Hasil	Keterangan
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<YieldOp> , SingleBlock

Interfaces: RegionBranchOpInterface

Operands:

Operand	Keterangan
input	variadic of tensor of any type values

Results:

Hasil	Keterangan
output	variadic of 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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
value	::mlir::ElementsAttr	constant vector/tensor attribute

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
qtype	::mlir::TypeAttr	Tensor type attribute
value	::mlir::ElementsAttr	constant vector/tensor attribute

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
value	::mlir::ElementsAttr	constant vector/tensor attribute
s_param	::mlir::TFL::SparsityParameterAttr	Sparsity parameter.
compressed_data	::mlir::ElementsAttr	constant vector/tensor attribute

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
qtype	::mlir::TypeAttr	Tensor type attribute

Operands:

Operand	Keterangan
input	tensor of 32-bit float or QI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Results:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
seed	::mlir::IntegerAttr	64-bit signless integer attribute
seed2	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:

Operand	Keterangan
shape	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
seed	::mlir::IntegerAttr	64-bit signless integer attribute
seed2	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:

Operand	Keterangan
shape	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
input	tensor of any type values

Results:

Hasil	Keterangan
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	Keterangan
resource_id	tensor of resource values

Results:

Hasil	Keterangan
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	Keterangan
input	tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
input	tensor of 1-bit signless integer values
reduction_indices	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
input	tensor of 1-bit signless integer values
reduction_indices	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or QUI8 type or QI8 type values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or QUI8 type or QI8 type values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or QUI8 type or QI8 type values

Results:

Hasil	Keterangan
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	Keterangan
input	tensor of any type values
shape	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
align_corners	::mlir::BoolAttr	bool attribute
half_pixel_centers	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
align_corners	::mlir::BoolAttr	bool attribute
half_pixel_centers	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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 ketentuan.

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	Keterangan
input	tensor of 32-bit float values
fft_length	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or QI8 type or QI16 type values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
input	tensor of 32-bit float or 32-bit signless integer values
segment_ids	tensor of 32-bit signless integer values

Results:

Hasil	Keterangan
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:

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Effects: MemoryEffects::Effect{}

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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:

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

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

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

Effects: MemoryEffects::Effect{}

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
out_type	::mlir::Attribute	derived attribute

Operands:

Operand	Keterangan
input	tensor of any type values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float or 64-bit float or 32-bit signless integer values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float values

Results:

Hasil	Keterangan
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	Keterangan
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:

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
beta	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Keterangan
input	tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
block_size	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
num_splits	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
outputs	variadic of 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{}

Atribut:

Atribut	MLIR Type	Keterangan
num_splits	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
outputs	variadic of 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	Keterangan
x	tensor of 32-bit float values

Results:

Hasil	Keterangan
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	Keterangan
x	tensor of 32-bit float values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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 .

Misalnya:

# '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{}

Atribut:

Atribut	MLIR Type	Keterangan
squeeze_dims	::mlir::ArrayAttr	64-bit integer array attribute whose size is at most 8

Operands:

Operand	Keterangan
input	tensor of any type values

Results:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
keep_dims	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
input	tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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{}

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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 . Dengan kata lain:

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
idx_out_type	::mlir::Attribute	derived attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
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) . Dll.

This is the opposite of pack .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultElementType

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
num	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative
axis	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:

Operand	Keterangan
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:

Hasil	Keterangan
outputs	variadic of 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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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

Atribut:

Atribut	MLIR Type	Keterangan
container	::mlir::StringAttr	string attribute
shared_name	::mlir::StringAttr	string attribute

Results:

Hasil	Keterangan
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	Keterangan
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:

Hasil	Keterangan
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<YieldOp> , SingleBlock

Interfaces: LoopLikeOpInterface , TflRuntimeVerifyOpInterface

Atribut:

Atribut	MLIR Type	Keterangan
is_stateless	::mlir::BoolAttr	bool attribute

Operands:

Operand	Keterangan
input	variadic of tensor of any type values

Results:

Hasil	Keterangan
output	variadic of 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	Keterangan
«unnamed»	variadic of 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	Keterangan
input	tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values

Results:

Hasil	Keterangan
output	tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values

Attributes

DimensionMetadataAttr

Dimension metadata.

Sintaksis:

#tfl.dimension_metadata<
  ::mlir::TFL::DimensionTypeAttr,   # format
  int32_t,   # dense_size
  ::llvm::ArrayRef<int32_t>,   # segments
  ::llvm::ArrayRef<int32_t>   # indices
>

Parameters:

Parameter	C++ type	Keterangan
format	::mlir::TFL::DimensionTypeAttr	dimension_type
dense_size	int32_t
segments	::llvm::ArrayRef<int32_t>
indices	::llvm::ArrayRef<int32_t>

SparsityParameterAttr

Sparsity parameter.

Sintaksis:

#tfl.sparsity_parameter<
  ::llvm::ArrayRef<int32_t>,   # traversal_order
  ::llvm::ArrayRef<int32_t>,   # block_map
  ::llvm::ArrayRef<DimensionMetadataAttr>   # dim_metadata
>

Parameters:

Parameter	C++ type	Keterangan
traversal_order	::llvm::ArrayRef<int32_t>
block_map	::llvm::ArrayRef<int32_t>
dim_metadata	::llvm::ArrayRef<DimensionMetadataAttr>

ConstBytesAttr

A string attribute representation of compiled bytes

Syntax Examples:

#tfl<const_bytes : "0xDEADBEEF">

Parameters:

Parameter	C++ type	Keterangan
nilai	::llvm::StringRef

DimensionTypeAttr

dimension_type

Sintaksis:

#tfl.dimension_type_attr<
  ::mlir::TFL::DimensionType   # value
>

Enum cases:

• DENSE ( DENSE )
• SPARSE_CSR ( SPARSE_CSR )

Parameters:

Parameter	C++ type	Keterangan
nilai	::mlir::TFL::DimensionType	an enum of type DimensionType

LSTMKernelTypeAttr

lstm_kernel_type

Sintaksis:

#tfl.lstm_kernel_type_attr<
  ::mlir::TFL::LSTMKernelType   # value
>

Enum cases:

• FULL ( FULL )
• BASIC ( BASIC )

Parameters:

Parameter	C++ type	Keterangan
nilai	::mlir::TFL::LSTMKernelType	an enum of type LSTMKernelType

MirrorPaddingTypeAttr

mirror_pad_enum

Sintaksis:

#tfl.mirror_pad_attr<
  ::mlir::TFL::MirrorPaddingType   # value
>

Enum cases:

• REFLECT ( REFLECT )
• SYMMETRIC ( SYMMETRIC )

Parameters:

Parameter	C++ type	Keterangan
nilai	::mlir::TFL::MirrorPaddingType	an enum of type MirrorPaddingType

Enums

DimensionType

dimension_type

Cases:

Simbol	Nilai	Rangkaian
PADAT	0	PADAT
SPARSE_CSR	1	SPARSE_CSR

LSTMKernelType

lstm_kernel_type

Cases:

Simbol	Nilai	Rangkaian
PENUH	0	PENUH
DASAR	1	DASAR

MirrorPaddingType

mirror_pad_enum

Cases:

Simbol	Nilai	Rangkaian
MENCERMINKAN	0	MENCERMINKAN
SIMETRIS	1	SIMETRIS