'tfl' Dialek

Dialek TensorFlow Lite.

Dialek ini memetakan ke operasi TensorFlow Lite.

Invarian:

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

Operasi

tfl.abs (TFL::AbsOp)

Operator nilai absolut

Diberikan tensor x , operasi ini mengembalikan tensor yang berisi nilai absolut setiap elemen dalam x . Misalnya, jika x adalah elemen input dan y adalah elemen output, operasi ini menghitung \(y = |x|\).

Ciri: 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 float 32-bit atau tipe QI8 atau tipe QI16

Hasil:

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

tfl.add (TFL::AddOp)

Operator penjumlahan

Operasi penjumlahan per elemen.

Ciri-ciri: ::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::AtributString	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 integer tanpa tanda 16-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau tipe QI8 atau tipe QUI8 atau tipe QI16
rhs	tensor bertipe float 32-bit atau integer tanpa tanda 16-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau tipe QI8 atau tipe QUI8 atau nilai tipe QI16

Hasil:

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

tfl.add_n (TFL::TambahNOp)

_Tambahkan operator n

Menambahkan semua tensor masukan berdasarkan elemen.

Ciri-ciri: AlwaysSpeculatableImplTrait , Commutative

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
inputs	variadic dari nilai tensor tipe 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.

Ciri: 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 tanpa tanda 1-bit atau nilai float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tak bertanda 8-bit atau 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)

Ciri: 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 tanpa tanda 1-bit atau nilai float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tak bertanda 8-bit atau 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 suatu variabel.

ReadVariableOp apa pun dengan ketergantungan kontrol pada op 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 bertipe float 32-bit atau float 64-bit atau integer tanpa tanda 1-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 8-bit atau tipe QI8 atau tipe QUI8 atau integer tanpa tanda 32-bit atau integer tanpa tanda 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 arctangent dari y/x per elemen, dengan memperhatikan tanda argumen.

Ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.average_pool_2d (TFL::AveragePool2DOp)

Operator 2d _Rata-rata_kolam

Melakukan operasi pengumpulan rata-rata pada input.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
filter_height	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
filter_width	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
padding	::mlir::AtributString	atribut string yang nilainya SAMA, atau VALID
stride_h	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
stride_w	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
fused_activation_function	::mlir::AtributString	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 float 32-bit atau tipe QI8 atau tipe QUI8 atau tipe QI16

Hasil:

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

tfl.basic_lstm (TFL::BasicLSTMOp)

Operator lstm dasar

Operator Sel LSTM dasar.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	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 adalah 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	Nilai tensor 2D dari tipe apa pun
state_output	Nilai tensor 2D dari tipe apa pun
concat_temp	Nilai tensor 2D dari tipe apa pun
activ_temp	Nilai tensor 2D dari tipe apa pun

tfl.batch_matmul (TFL::BatchMatMulOp)

Operator Perkalian 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)

Ciri-ciri: ::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 8-bit tanpa tanda
y	tensor float 32-bit atau tipe QI8 atau tipe QI16 atau nilai integer 8-bit tanpa tanda

Hasil:

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

tfl.batch_to_space_nd (TFL::BatchKeRuangNdOp)

Operator BatchToSpaceNd

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

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor bertipe float 32-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau integer tak bertanda 8-bit atau 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 bertipe float 32-bit atau integer tanpa tanda 16-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau integer tak bertanda 8-bit atau tipe QI8 atau tipe QUI8 atau tipe QI16

tfl.bidirectional_sequence_lstm (TFL::BidirectionalSequenceLSTMOp)

Operator lstm urutan dua arah

LSTM dua arah pada dasarnya terdiri dari dua LSTM, satu berjalan maju dan yang lainnya berjalan mundur. Outputnya merupakan gabungan dari kedua LSTM tersebut.

Ciri-ciri: QuantizableResult

Antarmuka: DynamicRangeQuantizedOpInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	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 float 32-bit atau nilai integer tanpa tanda 8-bit
fw_input_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_input_to_forget_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_input_to_cell_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_input_to_output_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_recurrent_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_recurrent_to_forget_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_recurrent_to_cell_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_recurrent_to_output_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
fw_cell_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_cell_to_forget_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_cell_to_output_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_input_gate_bias	tensor nilai tipe apa pun atau tidak ada tipe
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 tidak ada tipe
fw_projection_bias	tensor nilai tipe apa pun atau tidak ada tipe
bw_input_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_input_to_forget_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_input_to_cell_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_input_to_output_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_recurrent_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_recurrent_to_forget_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_recurrent_to_cell_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_recurrent_to_output_weights	tensor nilai float 32-bit atau nilai integer tanpa tanda 8-bit
bw_cell_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_cell_to_forget_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_cell_to_output_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_input_gate_bias	tensor nilai tipe apa pun atau tidak ada tipe
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 tidak ada tipe
bw_projection_bias	tensor nilai tipe apa pun atau tidak ada tipe
fw_input_activation_state	tensor berstatus
fw_input_cell_state	tensor berstatus
bw_input_activation_state	tensor berstatus
bw_input_cell_state	tensor berstatus
aux_input	tensor nilai tipe apa pun atau tidak ada tipe
fw_aux_input_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_aux_input_to_forget_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_aux_input_to_cell_weights	tensor nilai tipe apa pun atau tidak ada tipe
fw_aux_input_to_output_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_aux_input_to_input_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_aux_input_to_forget_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_aux_input_to_cell_weights	tensor nilai tipe apa pun atau tidak ada tipe
bw_aux_input_to_output_weights	tensor nilai tipe apa pun atau tidak ada tipe

Hasil:

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

tfl.bitcast (TFL::BitcastOp)

Operator Bitcast

Bitcast tensor dari satu tipe ke tipe 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 tipe apa pun

tfl.bitwise_xor (TFL::BitwiseXorOp)

Operator Xor Bitwise

Elementwise menghitung XOR bitwise dari lhs dan rhs .

Ciri: AlwaysSpeculatableImplTrait , Commutative , ResultsBroadcastableShape , SameOperandsAndResultElementType

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.broadcast_args (TFL::BroadcastArgsOp)

Kembalikan bentuk s0 op s1 dengan siaran.

Diberikan s0 dan s1 , tensor yang merepresentasikan bentuk, hitung r0 , bentuk yang disiarkan. s0 , s1 , dan r0 semuanya adalah vektor integer.

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::SiaranKeOp)

Menyiarkan array untuk bentuk yang kompatibel.

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

Misalnya,

x = tf.konstan([1, 2, 3]) y = tf.siarkan_ke(x, [3, 3]) cetak(y) tf.Tensor( [[1 2 3] [1 2 3] [1 2 3]], bentuk=(3, 3), tipe data=int32)

Pada contoh di atas, Tensor input dengan bentuk [1, 3] disiarkan ke Tensor output dengan bentuk [3, 3] .

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

Namun, broadcast_to tidak memberikan manfaat tersebut. Tensor yang baru dibuat mengambil alih seluruh memori bentuk yang disiarkan. (Namun, dalam konteks graf, broadcast_to mungkin digabungkan ke operasi selanjutnya dan kemudian dioptimalkan.)

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

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

tfl.bucketize (TFL::BucketizeOp)

Mengelompokkan 'input' berdasarkan 'batas'.

Contoh:

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

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 integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai integer tanpa tanda 32-bit

tfl.call_once (TFL::PanggilSekaliOp)

Memanggil fungsi inisialisasi

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

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

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

tfl.cast (TFL::CastOp)

Operator pemeran

Mengubah masukan dari tipe masukan ke tipe keluaran.

Ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan

Keterangan

input

tensor bertipe float 16-bit atau bfloat16 atau float 32-bit atau float 64-bit atau integer tanpa tanda 1-bit atau integer tanpa tanda 2-bit atau integer tanpa tanda 4-bit atau integer tanpa tanda 16-bit atau integer tak bertanda 16-bit atau integer tanpa tanda 32-bit atau integer tak bertanda 32-bit atau integer tanpa tanda 64-bit atau tipe TFLite quint8 atau integer tak bertanda 8-bit atau integer tanpa tanda 8-bit atau tipe kompleks dengan nilai elemen float 32-bit

Hasil:

Hasil

Keterangan

output

tensor bertipe float 16-bit atau bfloat16 atau float 32-bit atau float 64-bit atau integer tanpa tanda 1-bit atau integer tanpa tanda 2-bit atau integer tanpa tanda 4-bit atau integer tanpa tanda 16-bit atau integer tak bertanda 16-bit atau integer tanpa tanda 32-bit atau integer tak bertanda 32-bit atau integer tanpa tanda 64-bit atau tipe TFLite quint8 atau integer tak bertanda 8-bit atau integer tanpa tanda 8-bit atau tipe kompleks dengan nilai elemen float 32-bit

tfl.ceil (TFL::CeilOp)

Operator langit-langit

Mengembalikan nilai plafon input per elemen.

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.complex_abs (TFL::ComplexAbsOp)

Menghitung nilai absolut kompleks suatu 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 berupa bilangan kompleks dalam bentuk \(a + bj\)Nilai absolut dihitung sebagai \( \sqrt{a^2 + b^2}\).

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 float 64-bit

tfl.concatenation (TFL::OpPenggabungan)

Operator penggabungan

Menggabungkan tensor sepanjang satu dimensi

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan	Keterangan
values	variadic dari nilai tensor tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 64-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 16-bit atau bilangan bulat tak bertanda 8-bit atau tipe QI8 atau tipe QUI8 atau bilangan bulat tak bertanda 8-bit atau bilangan bulat tak bertanda 32-bit atau bilangan bulat tak bertanda 1-bit

tfl.control_node (TFL::OperasiNodeKontrol)

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

Ini digunakan untuk membungkus region dan melampirkan dependensi kontrol ke region tersebut. Biasanya, ini akan terjadi di salah satu langkah terakhir sebelum memancarkan model flatbuffer untuk mengaktifkan optimasi yang bergantung pada urutan operasi yang tetap (seperti rematerialisasi). Eksportir flatbuffer akan membongkar region yang dibungkus dan memberi anotasi pada model yang dihasilkan dengan metadata sehingga setiap penataan ulang runtime akan mengikuti urutan yang diberikan oleh dependensi kontrol.

Ciri-ciri: HasParent<mlir::func::FuncOp> , RecursiveMemoryEffects , SingleBlockImplicitTerminator<YieldOp> , SingleBlock

Operan:

Operan	Keterangan
controlInputs	variadik kontrol

Hasil:

Hasil	Keterangan
outputs	variadic dari nilai tensor tipe apa pun
control	kontrol

tfl.conv_2d (TFL::Conv2DOp)

Operator konvolusi

Melakukan operasi konvolusi pada masukan.

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

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

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

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

Hasil:

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

tfl.conv_3d (TFL::Conv3DOp)

Operator konvolusi 3D

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

Ciri: AlwaysSpeculatableImplTrait , TFL::AccumulatorUniformScale<2, 0, 1>

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit

tfl.conv_3d_transpose (TFL::Conv3DTransposeOp)

Operator Konvolusi 3D yang Ditransposisikan

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

Ciri: AlwaysSpeculatableImplTrait , TFL::AccumulatorUniformScale<2, 0, 1>

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Hasil:

Hasil	Keterangan
output	tensor nilai float 32-bit

tfl.cos (TFL::CosOp)

Operator kosinus

Menghitung kosinus elemen-bijaksana dari input

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.cumsum (TFL::CumsumOp)

Operator Cumsum

Hitunglah 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 float 32-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit
axis	tensor nilai integer tanpa tanda 32-bit

Hasil:

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

tfl.custom (TFL::CustomOp)

Operasi khusus

Operasi generik untuk semua operasi kustom TFLite.

input: Daftar input dalam op asli. custom_code: String yang digunakan untuk mengidentifikasi op mana tepatnya, yang sesuai dengan operator_codes.custom_code dalam flatbuffer. custom_option: tempat untuk menyimpan atribut op dalam bentuk byte. output: Daftar output dalam op asli.

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

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

Operan:

Operan	Keterangan
input	variadic dari tensor nilai tipe apa pun atau tidak ada tipe

Hasil:

Hasil	Keterangan
output	variadic dari nilai tensor tipe apa pun

tfl.custom_tf (TFL::CustomTfOp)

Wrapper Op untuk op kustom TF.

Sebuah op pembungkus di sekitar setiap op TF Kustom. Ini mencakup op yang didefinisikan menggunakan custom_opdefs atau tertaut yang tidak didefinisikan dalam dialek TF. Op ini hanya membungkus op kustom di dalam suatu region. Catatan #1, Op ini tidak akan menyertakan op kustom TF Lite yang didefinisikan menggunakan CustomOp. Catatan #2, Op ini hanyalah representasi internal di dalam konverter dan tidak diekspos/diekspor saat model diekspor ke Flatbuffer.

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

Antarmuka: InferTypeOpInterface , TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
input	variadic dari tensor nilai tipe apa pun atau tidak ada tipe

Hasil:

Hasil	Keterangan
output	variadic dari nilai tensor tipe apa pun

tfl.densify (TFL::DensifyOp)

Operator Densify

Mengonversi tensor jarang ke format padat.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.depth_to_space (TFL::DepthToSpaceOp)

Operator KedalamanKeRuang

Menyusun ulang data dari kedalaman ke dalam blok-blok data spasial. Ini adalah transformasi kebalikan dari SpaceToDepth. Lebih spesifiknya, operasi ini menghasilkan salinan tensor input di mana nilai-nilai dari dimensi depth dipindahkan dalam blok-blok spasial ke dimensi height dan width . Atribut block_size menunjukkan ukuran blok input dan bagaimana data dipindahkan.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
block_size	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit yang nilainya positif

Operan:

Operan	Keterangan
input	tensor bertipe float 32-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau tipe TFLite quint8 atau integer tak bertanda 8-bit atau tipe QI8 atau tipe QUI8

Hasil:

Hasil	Keterangan
output	tensor bertipe float 32-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 64-bit atau tipe TFLite quint8 atau integer tak bertanda 8-bit atau tipe QI8 atau tipe QUI8

tfl.depthwise_conv_2d (TFL::DepthwiseConv2DOp)

Operator konvolusi yang dapat dipisahkan berdasarkan kedalaman

Melakukan operasi konvolusi pada masukan.

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

Ciri-ciri: AlwaysSpeculatableImplTrait , QuantizableResult , TFL::AccumulatorUniformScale<2, 0, 1> , TFL::AffineOpCoefficient<3, 1>

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

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

Hasil:

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

tfl.dequantize (TFL::DequantizeOp)

Operator dekuantisasi

Mengonversi array bilangan bulat terkuantisasi menjadi bilangan titik-mengambang berdasarkan parameter kuantisasi.

Antarmuka: NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor tipe QI2 atau 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::DilateOp)

Operator dilatasi

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 tanpa tanda 8-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 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 float 64-bit
dilations	tensor nilai integer tanpa tanda 32-bit
padding_value	Tensor 0D dari nilai tipe apa pun

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tanpa tanda 8-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 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 float 64-bit

tfl.div (TFL::DivOp)

Operator divisi

Operasi pembagian berdasarkan elemen.

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

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	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 integer tanpa tanda 32-bit atau tipe QUI8 atau tipe QI8 atau tipe QI16
rhs	tensor nilai float 32-bit atau integer tanpa tanda 32-bit atau tipe QUI8 atau tipe QI8 atau tipe QI16

Hasil:

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

tfl.dynamic_update_slice (TFL::DynamicUpdateSliceOp)

DynamicUpdateSlice.

DynamicUpdateSlice op yang memiliki semantik yang sama dengan DynamicUpdateSlice XLA. Menghasilkan hasil yang merupakan nilai dari operan array input, dengan pembaruan irisan yang ditimpa pada 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 tanpa tanda 1-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai float 32-bit atau float 16-bit
update	tensor bilangan bulat tanpa tanda 1-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai float 32-bit atau float 16-bit
start_indices	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil	Keterangan
output	tensor bilangan bulat tanpa tanda 1-bit atau bilangan bulat tanpa tanda 8-bit atau bilangan bulat tanpa tanda 16-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau nilai float 32-bit atau float 16-bit

tfl.elu (TFL::EluOp)

Operator Satuan Linear Eksponensial

Menghitung persamaan linear eksponensial f(x) -> exp(x) - 1 untuk x < 0, x untuk x >= 0. elemen demi elemen.

Ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.embedding_lookup (TFL::PencarianEmbeddingOp)

Operator pencarian penyematan

Mencari id dalam daftar tensor penyematan.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lookup	tensor nilai integer tanpa tanda 32-bit
value	tensor nilai float 32-bit atau integer tanpa tanda 8-bit atau integer tak bertanda 8-bit atau tipe QI8 atau tipe QUI8 atau tipe QI4

Hasil:

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

tfl.equal (TFL::EqualOp)

Operator sama dengan

Mengembalikan elemen kebenaran dari x == y berdasarkan elemen

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

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor bilangan bulat tanpa tanda 1-bit atau bilangan bulat 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 tipe QI8 atau tipe QUI8 atau tipe QI16 atau bilangan bulat tak bertanda 8-bit atau tipe string TFLite
y	tensor bilangan bulat tanpa tanda 1-bit atau bilangan bulat 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 tipe QI8 atau tipe QUI8 atau tipe QI16 atau bilangan bulat tak bertanda 8-bit atau tipe string TFLite

Hasil:

Hasil	Keterangan
output	tensor nilai integer 1-bit tanpa tanda

tfl.exp (TFL::ExpOp)

Operator eksponensial natural

Melakukan operasi eksponensial alami per elemen pada input.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.expand_dims (TFL::ExpandDimsOp)

Memasukkan dimensi 1 ke dalam bentuk tensor.

Dengan input tensor, operasi ini menyisipkan dimensi 1 pada axis indeks dimensi bentuk input . axis indeks dimensi dimulai dari nol; jika Anda menentukan angka negatif untuk axis angka tersebut akan dihitung mundur dari akhir.

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

Contoh lainnya:

# '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 memerlukan:

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

Operasi ini terkait dengan squeeze() , yang menghapus dimensi berukuran 1.

Ciri: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor nilai tipe apa pun
dim	tensor nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil	Keterangan
output	tensor nilai tipe apa pun

tfl.external_const (TFL::OperasiKonstanEksternal)

Konstanta eksternal op.

Const op eksternal menampung buffer_index yang menunjuk ke sebuah konstanta dalam flatbuffer.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
buffer_index	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit

Hasil:

Hasil	Keterangan
output	tensor of any type values

tfl.fake_quant (TFL::FakeQuantOp)

FakeQuant operator

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
min	::mlir::FloatAttr	32-bit float attribute
max	::mlir::FloatAttr	32-bit float attribute
num_bits	::mlir::IntegerAttr	32-bit signless integer attribute whose minimum value is 2 whose maximum value is 16
narrow_range	::mlir::BoolAttr	bool attribute whose value is false

Operan:

Operan	Keterangan
input	tensor of 32-bit float values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float values

tfl.fill (TFL::FillOp)

Fill the tensor with given value.

Fill the tensor with given value.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
dims	tensor of 32/64-bit signless integer values
input	tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values

Hasil:

Hasil	Keterangan
result	tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values

tfl.floor (TFL::FloorOp)

Floor operator

Returns element-wise floor value of the input.

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor of 32-bit float values

Hasil:

Hasil	Keterangan
y	tensor of 32-bit float values

tfl.floor_div (TFL::FloorDivOp)

Floor div operator

Element-wise floor div operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values
rhs	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values

tfl.floor_mod (TFL::FloorModOp)

Division reminder

Element-wise division reminder operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values
rhs	tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values

Hasil:

Hasil	Keterangan
output	tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values

tfl.fully_connected (TFL::FullyConnectedOp)

Fully connected op

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

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT
weights_format	::mlir::AtributString	string attribute whose value is DEFAULT, or SHUFFLED4x16INT8
keep_num_dims	::mlir::BoolAttr	atribut bool
asymmetric_quantize_inputs	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
input	tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or QUI16 type values
filter	tensor of 32-bit float or QI2 type or QI4 type or QI8 type or QUI8 type or QI16 type values
bias	tensor of any type values or none type

Hasil:

Hasil	Keterangan
output	variadic of tensor of any type values

tfl.gather (TFL::GatherOp)

Gather operator

Gather slices from params axis axis according to indices .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
axis	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
batch_dims	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit

Operan:

Operan	Keterangan
params	tensor of 32-bit float or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values
indices	tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values

tfl.gather_nd (TFL::GatherNdOp)

_Gather nd operator

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
params	tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or QI8 type or TFLite string type values
indices	tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or QI8 type or TFLite string type values

tfl.gelu (TFL::GeluOp)

GELU activation function.

Computes GELU activation function element-wise.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
approximate	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
input	tensor of 32-bit float or QI8 type or QUI8 type values

Hasil:

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

tfl.greater (TFL::GreaterOp)

Greater operator

Element-wise greater operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values
rhs	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.greater_equal (TFL::GreaterEqualOp)

_Greater equal operator

Element-wise greater_equal operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values
rhs	tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.hard_swish (TFL::HardSwishOp)

Hardswish activation function.

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor of 32-bit float or QUI8 type or QI8 type values

Hasil:

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

tfl.hashtable (TFL::HashtableOp)

Creates a non-initialized hash table.

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

Interfaces: TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
table_id	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
key_dtype	::mlir::TypeAttr	atribut tipe apa pun
value_dtype	::mlir::TypeAttr	atribut tipe apa pun

Hasil:

Hasil	Keterangan
out	tensor of resource values

tfl.hashtable_find (TFL::HashtableFindOp)

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

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

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

Interfaces: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	tensor of resource values
keys	tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values
default_value	tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values

Hasil:

Hasil	Keterangan
out	tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values

tfl.hashtable_import (TFL::HashtableImportOp)

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

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

Interfaces: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	tensor of resource values
keys	tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values
values	tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values

tfl.hashtable_size (TFL::HashtableSizeOp)

Computes the number of elements in the given table.

Interfaces: TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
hash_table	tensor of resource values

Hasil:

Hasil	Keterangan
out	tensor of 64-bit signless integer values

tfl.if (TFL::IfOp)

Operasi jika-maka-lain

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

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

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

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

Contoh:

tfl.if %b  {
  ...
}

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

Interfaces: RegionBranchOpInterface , TflRuntimeVerifyOpInterface

Operan:

Operan	Keterangan
cond	tensor of 1-bit signless integer values

Hasil:

Hasil	Keterangan
results	variadic of tensor of any type values

tfl.imag (TFL::ImagOp)

Returns the imaginary part of a complex number.

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

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.l2_normalization (TFL::L2NormalizationOp)

L2 Normalize Operator

L2Normalization Op

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT

Operan:

Operan	Keterangan
input	tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values

tfl.leaky_relu (TFL::LeakyReluOp)

Leaky Relu operator

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
alpha	::mlir::FloatAttr	32-bit float attribute

Operan:

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

Hasil:

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

tfl.less (TFL::LessOp)

Less operator

Element-wise less operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values
rhs	tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.less_equal (TFL::LessEqualOp)

_Less equal operator

Element-wise less_equal operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values
rhs	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.local_response_normalization (TFL::LocalResponseNormalizationOp)

Local Response Normalization.

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

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

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType , TF::SameOperandsAndResultTypeResolveRef

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
radius	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
bias	::mlir::FloatAttr	32-bit float attribute
alpha	::mlir::FloatAttr	32-bit float attribute
beta	::mlir::FloatAttr	32-bit float attribute

Operan:

Operan	Keterangan
input	tensor of 32-bit float values

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float values

tfl.log (TFL::LogOp)

Natural logarithm operator

Performs element-wise natural logarithm operation on input.

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.log_softmax (TFL::LogSoftmaxOp)

Log softmax operator

Computes element-wise log softmax activations with the following formula

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.logical_and (TFL::LogicalAndOp)

Logical AND operator

Element-wise logical AND operation.

Traits: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 1-bit signless integer values
rhs	tensor of 1-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.logical_not (TFL::LogicalNotOp)

Logical NOT operator

Element-wise logical NOT operation.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 1-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.logical_or (TFL::LogicalOrOp)

Logical OR operator

Element-wise logical OR operation.

Traits: AlwaysSpeculatableImplTrait , ResultsBroadcastableShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor of 1-bit signless integer values
rhs	tensor of 1-bit signless integer values

Hasil:

Hasil	Keterangan
output	tensor of 1-bit signless integer values

tfl.logistic (TFL::LogisticOp)

Logistic operator

Computes element-wise Sigmoid of input

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
x	tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Hasil:

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

tfl.lstm (TFL::LSTMOp)

The full lstm operator

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

Traits: QuantizableResult

Interfaces: DynamicRangeQuantizedOpInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT
cell_clip	::mlir::FloatAttr	32-bit float attribute whose value is non-negative
proj_clip	::mlir::FloatAttr	32-bit float attribute whose value is non-negative
kernel_type	::mlir::TFL::LSTMKernelTypeAttr	lstm_kernel_type whose value is mlir::TFL::LSTMKernelType::FULL
asymmetric_quantize_inputs	::mlir::BoolAttr	atribut bool
input_to_input_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_forget_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_cell_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_output_intermediate	::mlir::TypeAttr	atribut tipe apa pun
effective_hidden_scale_intermediate	::mlir::TypeAttr	atribut tipe apa pun

Operan:

Operan	Keterangan
input	tensor of 32-bit float or QI8 type or QI16 type values
input_to_input_weights	tensor of any type values or none type
input_to_forget_weights	tensor of 32-bit float or QI8 type values
input_to_cell_weights	tensor of 32-bit float or QI8 type values
input_to_output_weights	tensor of 32-bit float or QI8 type values
recurrent_to_input_weights	tensor of any type values or none type
recurrent_to_forget_weights	tensor of 32-bit float or QI8 type values
recurrent_to_cell_weights	tensor of 32-bit float or QI8 type values
recurrent_to_output_weights	tensor of 32-bit float or QI8 type values
cell_to_input_weights	tensor of any type values or none type
cell_to_forget_weights	tensor of any type values or none type
cell_to_output_weights	tensor of any type values or none type
input_gate_bias	tensor of any type values or none type
forget_gate_bias	tensor of 32-bit float or QI32 type values
cell_bias	tensor of 32-bit float or QI32 type values
output_gate_bias	tensor of 32-bit float or QI32 type values
projection_weights	tensor of any type values or none type
projection_bias	tensor of any type values or none type
input_activation_state	stateful tensor
input_cell_state	stateful tensor
input_layer_norm_coefficients	tensor of any type values or none type
forget_layer_norm_coefficients	tensor of any type values or none type
cell_layer_norm_coefficients	tensor of any type values or none type
output_layer_norm_coefficients	tensor of any type values or none type

Hasil:

Hasil	Keterangan
output	tensor of any type values

tfl.matrix_diag (TFL::MatrixDiagOp)

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

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
diagonal	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values

Hasil:

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 QUI8 type or QI8 type or TFLite quint8 type values

tfl.matrix_set_diag (TFL::MatrixSetDiagOp)

Returns a batched matrix tensor with new batched diagonal values.

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	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 QI16 type or QUI8 type or TFLite quint8 type values
diagonal	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values

Hasil:

Hasil	Keterangan
result	tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values

tfl.max_pool_2d (TFL::MaxPool2DOp)

Max Pool 2D op

Performs max pool 2D on input.

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
padding	::mlir::AtributString	string attribute whose value is SAME, or VALID
stride_w	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
stride_h	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
filter_width	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
filter_height	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT

Operan:

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

Hasil:

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

tfl.maximum (TFL::MaximumOp)

Max operator

Element-wise max operation.

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

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

Hasil	Keterangan
max	tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values

tfl.mean (TFL::MeanOp)

Mean operator

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

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
input	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values
axis	tensor of 32-bit signless integer values

Hasil:

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 8-bit unsigned integer or QI16 type values

tfl.minimum (TFL::MinimumOp)

Min operator

Element-wise min operation.

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

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan	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

Hasil:

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)

Operator perkalian

Element-wise multiplication operation.

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

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT

Operan:

Operan	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

Hasil:

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	Tipe MLIR	Keterangan
seed	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit
seed2	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Antarmuka: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
value	::mlir::UnitAttr	atribut unit

Hasil:

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)

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	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 QI16 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 QI16 type or TFLite quint8 type or TFLite string type values

Hasil:

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	Tipe MLIR	Keterangan
tolerance	::mlir::FloatAttr	32-bit float attribute
log_if_failed	::mlir::BoolAttr	atribut bool

Operan:

Operan	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

Hasil:

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
axis	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit

Operan:

Operan	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

Hasil:

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) . Etc.

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

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan	Keterangan
values	variadic of tensor of any type values

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
input	tensor of 32-bit float or 1-bit signless integer 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

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or 1-bit signless integer 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

Efek: MemoryEffects::Effect{}

Operan:

Operan	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 quint8 type values
padding	tensor of 32/64-bit signless integer values
constant_values	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 quint8 type values

Hasil:

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 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

Operan:

Operan	Keterangan
input	variadic of tensor of any type values

Hasil:

Hasil	Keterangan
output	variadic of tensor of any type values

tfl.pow (TFL::PowOp)

Operator daya

Element-wise power operation.

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

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

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 , TFL::AffineOpCoefficient<-1, 1>

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	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

Hasil:

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
value	::mlir::ElementsAttr	atribut vektor/tensor konstan

Hasil:

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

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
qtype	::mlir::TypeAttr	Tensor type attribute
value	::mlir::ElementsAttr	atribut vektor/tensor konstan

Hasil:

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
value	::mlir::ElementsAttr	atribut vektor/tensor konstan
s_param	::mlir::TFL::SparsityParameterAttr	Sparsity parameter.
compressed_data	::mlir::ElementsAttr	atribut vektor/tensor konstan

Hasil:

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
qtype	::mlir::TypeAttr	Tensor type attribute
value	::mlir::ElementsAttr	atribut vektor/tensor konstan
s_param	::mlir::TFL::SparsityParameterAttr	Sparsity parameter.
compressed_data	::mlir::ElementsAttr	atribut vektor/tensor konstan

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

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

Hasil:

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

Attributes:

Atribut	Tipe MLIR	Keterangan
seed	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit
seed2	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit

Operan:

Operand	Keterangan
shape	tensor of 32-bit signless integer values

Hasil:

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

Attributes:

Atribut	Tipe MLIR	Keterangan
seed	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit
seed2	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit

Operan:

Operand	Keterangan
shape	tensor of 32-bit signless integer values

Hasil:

Hasil	Keterangan
out	tensor of 32-bit float values

tfl.range (TFL::RangeOp)

Operator jangkauan

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operand	Keterangan
input	tensor of any type values

Hasil:

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

Operan:

Operand	Keterangan
resource_id	tensor of resource values

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
align_corners	::mlir::BoolAttr	atribut bool
half_pixel_centers	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
align_corners	::mlir::BoolAttr	atribut bool
half_pixel_centers	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	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

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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 terms.

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operand	Keterangan
x	tensor of 32-bit float values

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.select (TFL::SelectOp)

Pilih 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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

Operand	Keterangan
input	tensor of any type values

Hasil:

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 , SameOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operand	Keterangan
x	tensor of 32-bit float values

Hasil:

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

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

Efek: MemoryEffects::Effect{}

Operan:

Operand	Keterangan
input	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI4 type 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

Hasil:

Hasil	Keterangan
output	tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI4 type 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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

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

Hasil:

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 , QuantizableResult , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

Operand	Keterangan
x	tensor of 32-bit float values

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

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

Operan:

Operand	Keterangan
input	tensor of any type values

Hasil:

Hasil	Keterangan
output	tensor of any type values

tfl.strided_slice (TFL::StridedSliceOp)

StridedSlice Op

Return a strided slice from input .

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

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
begin_mask	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
end_mask	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
ellipsis_mask	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
new_axis_mask	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
shrink_axis_mask	::mlir::IntegerAttr	Atribut integer tanpa tanda 32-bit
offset	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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)

Operator pengurangan

Element-wise subtraction operation.

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

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT

Operan:

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
keep_dims	::mlir::BoolAttr	atribut bool

Operan:

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

Hasil:

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

Interfaces: DynamicRangeQuantizedOpInterface , RequiresQuantizedBiasInterface , TFL_StatefulOp , TflRuntimeVerifyOpInterface

Attributes:

Atribut	Tipe MLIR	Keterangan
rank	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive
fused_activation_function	::mlir::AtributString	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	atribut bool

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

Hasil:

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

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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)

Operator ubin.

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

Efek: 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

Hasil:

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

Efek: 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

Hasil:

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

Efek: 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 4-bit signless integer or QI4 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

Hasil:

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 4-bit signless integer or QI4 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 , TFL::AccumulatorUniformScale<3, 1, 2> , TFL::AffineOpCoefficient<0, 1>

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

Efek: MemoryEffects::Effect{}

Attributes:

Atribut	Tipe MLIR	Keterangan
padding	::mlir::AtributString	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::AtributString	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

Hasil:

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

Attributes:

Atribut	Tipe MLIR	Keterangan
fused_activation_function	::mlir::AtributString	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	atribut bool
asymmetric_quantize_inputs	::mlir::BoolAttr	atribut bool
diagonal_recurrent_tensors	::mlir::BoolAttr	atribut bool
input_to_input_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_forget_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_cell_intermediate	::mlir::TypeAttr	atribut tipe apa pun
input_to_output_intermediate	::mlir::TypeAttr	atribut tipe apa pun
effective_hidden_scale_intermediate	::mlir::TypeAttr	atribut tipe apa pun

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

Hasil:

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

Attributes:

Atribut	Tipe MLIR	Keterangan
time_major	::mlir::BoolAttr	atribut bool
fused_activation_function	::mlir::AtributString	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	atribut bool

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

Hasil:

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 . In other words:

Traits: AlwaysSpeculatableImplTrait , QuantizableResult

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Attributes:

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

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

Hasil:

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) . Etc.

This is the opposite of pack .

Traits: AlwaysSpeculatableImplTrait , QuantizableResult , SameOperandsAndResultElementType

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

Efek: MemoryEffects::Effect{}

Attributes:

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

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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: 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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: 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

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: 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

Hasil:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: 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

Hasil:

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

Attributes:

Atribut	Tipe MLIR	Keterangan
container	::mlir::AtributString	atribut string
shared_name	::mlir::AtributString	atribut string

Hasil:

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.

Ciri-ciri: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface) , TflRuntimeVerifyOpInterface

Efek: 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

Hasil:

Hasil	Keterangan
index	tensor of 64-bit signless integer values

tfl.while (TFL::WhileOp)

Perulangan while

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

Attributes:

Atribut	Tipe MLIR	Keterangan
is_stateless	::mlir::BoolAttr	atribut bool

Operands:

Operand	Keterangan
input	variadic of tensor of any type values

Hasil:

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{}

Operan:

Operand	Keterangan
"tanpa nama"	variadic jenis apa pun

tfl.zeros_like (TFL::ZerosLikeOp)

ZerosLike operator

Returns a tensor of zeros with the same shape and type as the input tensor.

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
input	tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values

Hasil:

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

Atribut

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
>

Parameternya:

Parameter	Tipe C++	Keterangan
format	::mlir::TFL::DimensionTypeAttr	dimension_type
dense_size	int32_t
segmen	::llvm::ArrayRef<int32_t>
indeks	::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
>

Parameternya:

Parameter	Tipe C++	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">

Parameternya:

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

DimensionTypeAttr

_Dimension type

Sintaksis:

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

Parameternya:

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

LSTMKernelTypeAttr

_Lstm_kernel type

Sintaksis:

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

Parameternya:

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

MirrorPaddingTypeAttr

_Mirror_pad enum

Sintaksis:

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

Parameternya:

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

Enum

DimensionType

_Dimension type

Kasus:

Simbol	Nilai	Rangkaian
PADAT	0	PADAT
SPARSE_CSR	1	SPARSE_CSR

LSTMKernelType

_Lstm_kernel type

Kasus:

Simbol	Nilai	Rangkaian
PENUH	0	PENUH
DASAR	1	DASAR

MirrorPaddingType

_Mirror_pad enum

Kasus:

Simbol	Nilai	Rangkaian
MENCERMINKAN	0	MENCERMINKAN
SIMETRIS	1	SIMETRIS