Bantuan melindungi Great Barrier Reef dengan TensorFlow pada Kaggle Bergabung Tantangan

Dialek 'tfl'

Dialek TensorFlow Lite.

Dialek ini dipetakan ke operasi TensorFlow Lite.

Invarian:

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

Definisi operasi

tfl.abs (:: mlir :: TFL :: AbsOp)

Operator nilai absolut

Mengingat tensor x , operasi ini mengembalikan sebuah tensor mengandung nilai absolut dari setiap elemen dalam x . Sebagai contoh, jika x adalah elemen input dan y adalah output elemen, operasi ini menghitung \(y = |x|\).

Sifat: SameOperandsAndResultShape, SameOperandsAndResultType

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.add_n (:: mlir :: TFL :: AddNOp)

tambahkan_n operator

Menambahkan semua elemen tensor input.

Sifat: Komutatif, Hasil Tidak Dapat Dikuantifikasi

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
inputs tensor dari semua jenis nilai

Hasil:

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

tfl.add (:: mlir :: TFL :: AddOp)

Operator tambahan

Operasi penambahan elemen-bijaksana.

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait, Komutatif, HasilBroadcastableShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
fused_activation_function ::mlir::StringAttr enum aktivasi menyatu

Operan:

Operan Keterangan
lhs tensor float 32-bit atau integer tanpa sinyal 32-bit atau integer tanpa sinyal 64-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16
rhs tensor float 32-bit atau integer tanpa sinyal 32-bit atau integer tanpa sinyal 64-bit atau nilai tipe QI8 atau tipe QUI8 atau tipe QI16

Hasil:

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

tfl.arg_max (:: mlir :: TFL :: ArgMaxOp)

Operator ArgMax

Mengembalikan indeks dengan nilai terbesar di seluruh dimensi tensor.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan Keterangan
input tensor float 32-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 8-bit atau nilai tipe QI8 atau tipe QUI8
dim tensor dari nilai integer tanpa tanda 32/64-bit

Hasil:

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

tfl.arg_min (:: mlir :: 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)

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan Keterangan
input tensor float 32-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 8-bit atau integer tanpa tanda 8-bit atau nilai tipe QI8 atau tipe QUI8
dim tensor dari nilai integer tanpa tanda 32/64-bit

Hasil:

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

tfl.assign_variable (:: mlir :: TFL :: AssignVariableOp)

Memberikan nilai baru ke variabel.

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

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

Operan Keterangan
resource_id tensor nilai sumber daya
value tensor float 32-bit atau float 64-bit atau 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.average_pool_2d (:: mlir :: TFL :: AveragePool2DOp)

Rata-rata_pool_2d operator

Melakukan operasi pengumpulan rata-rata pada input.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
filter_height ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit
filter_width ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit
padding ::mlir::StringAttr padding enum
stride_h ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit
stride_w ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit
fused_activation_function ::mlir::StringAttr enum aktivasi menyatu

Operan:

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

Hasil:

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

tfl.basic_lstm (:: mlir :: TFL :: BasicLSTMOp)

Operator lstm dasar

Operator Sel LSTM dasar.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
fused_activation_function ::mlir::StringAttr enum aktivasi menyatu
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::StringAttr lstm kernel tipe enum case BASIC

Operan:

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

Hasil:

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

tfl.batch_matmul (:: mlir :: 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_lhs`: optional: Transpose LHS (default false)

Sifat: DynamicRangeQuantizableOp

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

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

Hasil:

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

tfl.batch_to_space_nd (:: mlir :: TFL :: BatchToSpaceNdOp)

Operator BatchToSpaceNd

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

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

Hasil Keterangan
output tensor 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 nilai tipe QI8 atau tipe QUI8

tfl.bidirectional_sequence_lstm (:: mlir :: TFL :: BidirectionalSequenceLSTMOp)

Operator lstm urutan dua arah

lstm dua arah pada dasarnya adalah dua lstm, satu berjalan ke depan & yang lainnya berjalan mundur. Dan outputnya adalah gabungan dari dua lstms.

Antarmuka: TFL_StatefulOp, TflRuntimeVerifyOpInterface

Atribut:

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

Hasil:

Hasil Keterangan
fw_output tensor dari semua jenis nilai
bw_output tensor dari semua jenis nilai

tfl.broadcast_args (:: mlir :: TFL :: BroadcastArgsOp)

Kembalikan bentuk s0 op s1 dengan siaran.

Mengingat s0 dan s1 , tensor yang mewakili bentuk, menghitung r0 , bentuk disiarkan. s0 , s1 dan r0 semua vektor integer.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.broadcast_to (:: mlir :: TFL :: BroadcastToOp)

Siarkan array untuk bentuk yang kompatibel.

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

Sebagai contoh,

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

Dalam contoh di atas, Tensor masukan dengan bentuk [1, 3] disiarkan untuk output Tensor dengan bentuk [3, 3] .

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

Namun, broadcast_to tidak membawa dengan itu manfaat tersebut. Tensor yang baru dibuat mengambil memori penuh dari bentuk yang disiarkan. (Dalam konteks grafik, broadcast_to mungkin menyatu untuk operasi berikutnya dan kemudian akan dioptimalkan pergi, namun.)

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
input tensor float 32-bit atau integer tanpa tanda 32-bit atau integer tanpa sinyal 1-bit atau integer tanpa sinyal 8-bit atau tipe QI8 atau integer tanpa tanda 8-bit atau tipe QUI8 atau integer tanpa sinyal 16-bit atau tipe QI16 atau tanpa sinyal 64-bit tipe integer atau kompleks dengan nilai elemen float 32-bit
shape tensor dari nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil Keterangan
output tensor float 32-bit atau integer tanpa tanda 32-bit atau integer tanpa sinyal 1-bit atau integer tanpa sinyal 8-bit atau tipe QI8 atau integer tanpa tanda 8-bit atau tipe QUI8 atau integer tanpa sinyal 16-bit atau tipe QI16 atau tanpa sinyal 64-bit tipe integer atau kompleks dengan nilai elemen float 32-bit

tfl.bucketize (:: mlir :: TFL :: BucketizeOp)

Bucketizes 'input' berdasarkan 'batas'.

Contoh:

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

Sifat: NoQuantizableResult, SameOperandsAndResultShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan Keterangan
input tensor float 32-bit atau float 64-bit atau integer tanpa tanda 32-bit atau nilai integer tanpa sinyal 64-bit

Hasil:

Hasil Keterangan
output tensor dari nilai integer tanpa tanda 32-bit

tfl.call_once (:: mlir :: TFL :: CallOnceOp)

Memanggil fungsi inisialisasi

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

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

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

tfl.cast (:: mlir :: TFL :: CastOp)

Operator pemeran

Melemparkan input dari tipe input ke tipe output.

Sifat: NoQuantizableResult, SameOperandsAndResultShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
input tensor float 32-bit atau integer tanpa tanda 1-bit atau integer tanpa tanda 16-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe quint8 TFLite atau integer tanpa tanda 8-bit atau 8-bit integer tanpa tanda atau tipe kompleks dengan nilai elemen float 32-bit

Hasil:

Hasil Keterangan
output tensor float 32-bit atau integer tanpa tanda 1-bit atau integer tanpa tanda 16-bit atau integer tanpa tanda 32-bit atau integer tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe quint8 TFLite atau integer tanpa tanda 8-bit atau 8-bit integer tanpa tanda atau tipe kompleks dengan nilai elemen float 32-bit

tfl.ceil (:: mlir :: TFL :: CeilOp)

Operator langit-langit

Mengembalikan nilai batas elemen-bijaksana dari input.

Sifat: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Antarmuka: NoSideEffect (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 (:: mlir :: TFL :: ComplexAbsOp)

Menghitung nilai absolut kompleks dari sebuah tensor.

Mengingat tensor x bilangan kompleks, operasi ini kembali sebuah tensor jenis float atau double yang merupakan nilai absolut dari setiap elemen dalam x . Semua elemen dalam x harus bilangan kompleks dari bentuk \(a + bj\). Nilai absolut dihitung sebagai \( \sqrt{a^2 + b^2}\).

Sifat: SameOperandsAndResultShape

Antarmuka: NoSideEffect (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 (:: mlir :: TFL :: ConcatenationOp)

Operator gabungan

Menggabungkan tensor sepanjang satu dimensi

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
axis ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit
fused_activation_function ::mlir::StringAttr enum aktivasi menyatu

Operan:

Operan Keterangan
values tensor dari semua jenis nilai

Hasil:

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

tfl.pseudo_const (:: mlir :: TFL :: ConstOp)

Operasi semu konstan.

Mewakili nilai konstan dalam dialek TensorFlow Lite. Ini bukan operasi yang sebenarnya dan akan diturunkan ke buffer sebagai gantinya.

Operasi diperbolehkan untuk memiliki semua jenis atribut yang sama seperti tf.Const (misalnya, atribut TF buram diperbolehkan).

Sifat: ConstantLike, FirstAttrDerivedResultType

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Hasil:

Hasil Keterangan
output tensor dari semua jenis nilai

tfl.conv_2d (:: mlir :: TFL :: Conv2DOp)

Operator konvolusi

Melakukan operasi konvolusi pada input.

Input: inputs[0] : diperlukan: aktivasi masukan tensor inputs[1] : diperlukan: tensor Filter berat inputs[2] : opsional: bias tensor

Sifat: DynamicRangeQuantizableOp, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<0, 1>

Antarmuka: AffineQuantizedOpInterface, InferTypeOpInterface, NoSideEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis 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::StringAttr enum aktivasi menyatu
padding ::mlir::StringAttr padding enum
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 tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16
filter tensor nilai float 32-bit atau tipe QI8 atau tipe QUI8
bias tensor dari nilai tipe apa pun atau tidak ada tipe

Hasil:

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

tfl.conv_3d (:: mlir :: TFL :: Conv3DOp)

Operator 3D konvolusi

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

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

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis 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::StringAttr enum aktivasi menyatu
padding ::mlir::StringAttr padding enum
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 dari nilai tipe apa pun atau tidak ada tipe

Hasil:

Hasil Keterangan
output tensor nilai float 32-bit

tfl.conv_3d_transpose (:: mlir :: TFL :: Conv3DTransposeOp)

Operator 3D Konvolusi yang Ditransposisikan

Melakukan operasi konvolusi yang dialihkan pada input 3D. Input: inputs[0] : diperlukan: bentuk output tensor inputs[1] : diperlukan: tensor Filter berat inputs[2] : diperlukan: aktivasi masukan tensor inputs[3] : opsional: bias tensor

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

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis 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::StringAttr enum aktivasi menyatu
padding ::mlir::StringAttr padding enum
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 dari nilai integer tanpa tanda 32-bit
filter tensor nilai float 32-bit
input tensor nilai float 32-bit
bias tensor dari nilai tipe apa pun atau tidak ada tipe

Hasil:

Hasil Keterangan
output tensor nilai float 32-bit

tfl.cos (:: mlir :: TFL :: CosOp)

Operator kosinus

Menghitung Cosinus input berdasarkan elemen

Sifat: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Antarmuka: NoSideEffect (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 (:: mlir :: TFL :: CumsumOp)

Operator Cumsum

Hitung jumlah kumulatif tensor x sepanjang sumbu.

Sifat: Tidak AdaHasil yang Dapat Dikuantifikasi

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

Operan Keterangan
input tensor float 32-bit atau integer tanpa sinyal 32-bit atau nilai integer tanpa sinyal 64-bit
axis tensor dari nilai integer tanpa tanda 32-bit

Hasil:

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

tfl.custom (:: mlir :: TFL :: CustomOp)

Operasi kustom

Operasi umum untuk operasi kustom TFLite apa pun.

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

Sifat: Tidak AdaHasil yang Dapat Dikuantifikasi

Antarmuka: TflRuntimeVerifyOpInterface

Atribut:

Atribut Jenis MLIR Keterangan
custom_code ::mlir::StringAttr atribut string
custom_option BuramElemenAttr atribut byte buram

Operan:

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

Hasil:

Hasil Keterangan
output tensor dari semua jenis nilai

tfl.custom_tf (:: mlir :: TFL :: CustomTfOp)

Wrapper Op untuk operasi kustom TF.

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

Sifat: Terisolasi Dari Atas, Tidak Ada Hasil yang Dapat Dikuantifikasi, Efek Samping Rekursif, SingleBlockImplicitTerminator

Antarmuka: InferTypeOpInterface, TflRuntimeVerifyOpInterface

Operan:

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

Hasil:

Hasil Keterangan
output tensor dari semua jenis nilai

tfl.densify (:: mlir :: TFL :: DensifyOp)

operator padatkan

Mengonversi tensor jarang ke format padat.

Sifat: Tidak AdaHasil yang Dapat Dikuantifikasi

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.depth_to_space (:: mlir :: TFL :: DepthToSpaceOp)

Operator DepthToSpace

Menata ulang data dari kedalaman menjadi blok-blok data spasial. Ini adalah transformasi kebalikan dari SpaceToDepth. Lebih khusus, op ini output salinan dari tensor masukan di mana nilai-nilai dari depth dimensi yang pindah blok spasial dengan height dan width dimensi. The attr block_size menunjukkan ukuran blok masukan dan bagaimana data tersebut akan dipindahkan.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Operan:

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

Hasil:

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

tfl.depthwise_conv_2d (:: mlir :: TFL :: DepthwiseConv2DOp)

Operator konvolusi yang dapat dipisahkan secara mendalam

Melakukan operasi konvolusi pada input.

Input: inputs[0] : diperlukan: aktivasi masukan tensor inputs[1] : diperlukan: tensor Filter berat inputs[2] : opsional: bias tensor

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

Antarmuka: AffineQuantizedOpInterface, NoSideEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis 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::StringAttr enum aktivasi menyatu
padding ::mlir::StringAttr padding enum
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 tipe float atau QI8 32-bit atau tipe QUI8 atau tipe QI16
filter tensor nilai float 32-bit atau tipe QI8 atau tipe QUI8
bias tensor dari nilai tipe apa pun atau tidak ada tipe

Hasil:

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

tfl.dequantize (:: mlir :: TFL :: DequantizeOp)

Operator dekuantisasi

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

Sifat: Tidak AdaHasil yang Dapat Dikuantifikasi

Antarmuka: TflRuntimeVerifyOpInterface

Operan:

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

Hasil:

Hasil Keterangan
output tensor nilai float 32-bit

tfl.div (:: mlir :: TFL :: DivOp)

Operator divisi

Operasi pembagian elemen-bijaksana.

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait, NoQuantizableResult, ResultsBroadcastableShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
fused_activation_function ::mlir::StringAttr enum aktivasi menyatu

Operan:

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

Hasil:

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

tfl.elu (:: mlir :: TFL :: EluOp)

Operator Satuan Linier Eksponensial

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

Sifat: SameOperandsAndResultShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

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

Hasil:

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

tfl.embedding_lookup (:: mlir :: TFL :: EmbeddingLookupOp)

Menyematkan operator pencarian

Mencari id dalam daftar penyematan tensor.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
lookup tensor dari nilai integer tanpa tanda 32-bit
value tensor float 32-bit atau integer tanpa tanda 8-bit atau nilai integer tak bertanda 8-bit

Hasil:

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

tfl.equal (:: mlir :: TFL :: EqualOp)

Operator yang sama

Mengembalikan elemen kebenaran dari x == y elemen-bijaksana

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait, Komutatif, HasilBroadcastableShape

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
x tensor bilangan bulat tanpa tanda 1-bit atau float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe QI8 atau tipe QUI8 atau bilangan bulat tak bertanda 8-bit atau nilai tipe string TFLite
y tensor bilangan bulat tanpa tanda 1-bit atau float 32-bit atau bilangan bulat tanpa tanda 32-bit atau bilangan bulat tanpa tanda 64-bit atau tipe QI8 atau tipe QUI8 atau bilangan bulat tak bertanda 8-bit atau nilai tipe string TFLite

Hasil:

Hasil Keterangan
output tensor dari nilai integer tanpa tanda 1-bit

tfl.exp (:: mlir :: TFL :: ExpOp)

Operator eksponensial alami

Melakukan operasi eksponensial alami elemen-bijaksana pada input.

Sifat: NoQuantizableResult, SameOperandsAndResultType

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
x tensor nilai float 32-bit

Hasil:

Hasil Keterangan
y tensor nilai float 32-bit

tfl.expand_dims (:: mlir :: TFL :: ExpandDimsOp)

Menyisipkan dimensi 1 ke dalam bentuk tensor.

Mengingat tensor input , operasi ini menyisipkan dimensi 1 di indeks dimensi axis dari input 's bentuk. Indeks dimensi axis dimulai dengan nol; jika Anda menentukan angka negatif untuk axis itu dihitung mundur dari akhir.

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

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

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

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
input tensor dari semua jenis nilai
dim tensor dari nilai integer tanpa tanda 32/64-bit

Hasil:

Hasil Keterangan
output tensor dari semua jenis nilai

tfl.external_const (:: mlir :: TFL :: ExternalConstOp)

Operasi konstanta eksternal

Eksternal const op memegang buffer_index yang menunjuk ke sebuah konstan dalam flatbuffer tersebut.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

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

Hasil:

Hasil Keterangan
output tensor dari semua jenis nilai

tfl.fake_quant (:: mlir :: TFL :: FakeQuantOp)

Operator Quant Palsu

Kuantisasi palsu tensor 'input' dari tipe float melalui skalar float min dan maks ke tensor 'output' dengan bentuk yang sama dengan input.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Atribut:

Atribut Jenis MLIR Keterangan
min ::mlir::FloatAttr Atribut float 32-bit
max ::mlir::FloatAttr Atribut float 32-bit
num_bits ::mlir::IntegerAttr Atribut integer tanpa tanda 32-bit yang nilai minimumnya adalah 2 yang nilai maksimumnya adalah 16
narrow_range ::mlir::BoolAttr atribut bool yang nilainya salah

Operan:

Operan Keterangan
input tensor nilai float 32-bit

Hasil:

Hasil Keterangan
output tensor nilai float 32-bit

tfl.fill (:: mlir :: TFL :: FillOp)

Isi tensor dengan nilai yang diberikan.

Isi tensor dengan nilai yang diberikan.

Antarmuka: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Efek: MemoryEffects::Effect{}

Operan:

Operan Keterangan
dims tensor dari nilai integer tanpa tanda 32/64-bit
input tensor float 32-bit atau integer tanpa sinyal 32-bit atau integer tanpa sinyal 64-bit atau integer tanpa sinyal 1-bit atau tipe QI8 atau tipe QI16 atau nilai tipe string TFLite

Hasil:

Hasil Keterangan
result tensor float 32-bit atau integer tanpa sinyal 32-bit atau integer tanpa sinyal 64-bit atau integer tanpa sinyal 1-bit atau tipe QI8 atau tipe QI16 atau nilai tipe string TFLite

tfl.floor_div (:: mlir :: TFL :: FloorDivOp)

Operator lantai div

Operasi div lantai berdasarkan elemen.

Sifat: ::mlir::OpTrait::TFLRuntimeOpTrait, NoQuantizableResult, ResultsBroadcastableShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit float or 32-bit signless integer values
rhs tensor of 32-bit float or 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer values

tfl.floor_mod (::mlir::TFL::FloorModOp)

Division reminder

Element-wise division reminder operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait, NoQuantizableResult, ResultsBroadcastableShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit signless integer or 64-bit signless integer or 32-bit float values
rhs tensor of 32-bit signless integer or 64-bit signless integer or 32-bit float values

Results:

Result Description
output tensor of 32-bit signless integer or 64-bit signless integer or 32-bit float values

tfl.floor (::mlir::TFL::FloorOp)

Floor operator

Returns element-wise floor value of the input.

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.fully_connected (::mlir::TFL::FullyConnectedOp)

Fully connected op

Traits: DynamicRangeQuantizableOp, quant::AccumulatorUniformScale<2, 0, 1>, quant::AffineOpCoefficient<-1, 1>

Interfaces: AffineQuantizedOpInterface, NoSideEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum
weights_format ::mlir::StringAttr fully connected options weights format
keep_num_dims ::mlir::BoolAttr bool attribute
asymmetric_quantize_inputs ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or QUI16 type values
filter tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values
bias tensor of any type values or none type

Results:

Result Description
output tensor of any type values

tfl.gather_nd (::mlir::TFL::GatherNdOp)

Gather_nd operator

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
params tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or TFLite string type values
indices tensor of 32/64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or TFLite string type values

tfl.gather (::mlir::TFL::GatherOp)

Gather operator

Gather slices from params axis axis according to indices .

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
axis ::mlir::IntegerAttr 32-bit signless integer attribute
batch_dims ::mlir::IntegerAttr 32-bit signless integer attribute

Operands:

Operand Description
params tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 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 32-bit signless integer or 64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 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.greater_equal (::mlir::TFL::GreaterEqualOp)

Greater_equal operator

Element-wise greater_equal operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.greater (::mlir::TFL::GreaterOp)

Greater operator

Element-wise greater operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.hard_swish (::mlir::TFL::HardSwishOp)

Hardswish activation function.

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

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or QUI8 type or QI8 type values

Results:

Result Description
output tensor of 32-bit float or QUI8 type or QI8 type values

tfl.hashtable_find (::mlir::TFL::HashtableFindOp)

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

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

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

Interfaces: TflRuntimeVerifyOpInterface

Operands:

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

Results:

Result Description
out tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values

tfl.hashtable_import (::mlir::TFL::HashtableImportOp)

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

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

Interfaces: TflRuntimeVerifyOpInterface

Operands:

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

tfl.hashtable (::mlir::TFL::HashtableOp)

Creates a non-initialized hash table.

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

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
table_id ::mlir::IntegerAttr 32-bit signless integer attribute
key_dtype ::mlir::TypeAttr any type attribute
value_dtype ::mlir::TypeAttr any type attribute

Results:

Result Description
out tensor of resource values

tfl.hashtable_size (::mlir::TFL::HashtableSizeOp)

Computes the number of elements in the given table.

Interfaces: TflRuntimeVerifyOpInterface

Operands:

Operand Description
hash_table tensor of resource values

Results:

Result Description
out tensor of 64-bit signless integer values

tfl.if (::mlir::TFL::IfOp)

if-then-else operation

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

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

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

Example:

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

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

Example:

tfl.if %b  {
  ...
}

Traits: NoRegionArguments, RecursiveSideEffects, SingleBlockImplicitTerminator

Interfaces: RegionBranchOpInterface, TflRuntimeVerifyOpInterface

Operands:

Operand Description
cond tensor of 1-bit signless integer values

Results:

Result Description
results tensor of any type values

tfl.imag (::mlir::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: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or 64-bit float values

tfl.l2_normalization (::mlir::TFL::L2NormalizationOp)

L2 Normalize Operator

L2Normalization Op

Interfaces: FixedOutputRangeInterface, NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum

Operands:

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

Results:

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

tfl.lstm (::mlir::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'

Interfaces: TFL_StatefulOp, TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum
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::StringAttr lstm kernel type enum case FULL
asymmetric_quantize_inputs ::mlir::BoolAttr bool attribute
input_to_input_intermediate ::mlir::TypeAttr any type attribute
input_to_forget_intermediate ::mlir::TypeAttr any type attribute
input_to_cell_intermediate ::mlir::TypeAttr any type attribute
input_to_output_intermediate ::mlir::TypeAttr any type attribute
effective_hidden_scale_intermediate ::mlir::TypeAttr any type attribute

Operands:

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

Results:

Result Description
output tensor of any type values

tfl.leaky_relu (::mlir::TFL::LeakyReluOp)

Leaky Relu operator

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

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
alpha ::mlir::FloatAttr 32-bit float attribute

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values

tfl.less_equal (::mlir::TFL::LessEqualOp)

Less_equal operator

Element-wise less_equal operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.less (::mlir::TFL::LessOp)

Less operator

Element-wise less operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.local_response_normalization (::mlir::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: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
radius ::mlir::IntegerAttr 32-bit signless integer attribute
bias ::mlir::FloatAttr 32-bit float attribute
alpha ::mlir::FloatAttr 32-bit float attribute
beta ::mlir::FloatAttr 32-bit float attribute

Operands:

Operand Description
input tensor of 32-bit float values

Results:

Result Description
output tensor of 32-bit float values

tfl.log (::mlir::TFL::LogOp)

Natural logarithm operator

Performs element-wise natural logarithm operation on input.

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.log_softmax (::mlir::TFL::LogSoftmaxOp)

Log softmax operator

Computes element-wise log softmax activations with the following formula

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

Traits: SameOperandsAndResultShape

Interfaces: FixedOutputRangeInterface, NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values

tfl.logical_and (::mlir::TFL::LogicalAndOp)

Logical AND operator

Element-wise logical AND operation.

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 1-bit signless integer values
rhs tensor of 1-bit signless integer values

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.logical_not (::mlir::TFL::LogicalNotOp)

Logical NOT operator

Element-wise logical NOT operation.

Traits: NoQuantizableResult, SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 1-bit signless integer values

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.logical_or (::mlir::TFL::LogicalOrOp)

Logical OR operator

Element-wise logical OR operation.

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 1-bit signless integer values
rhs tensor of 1-bit signless integer values

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.logistic (::mlir::TFL::LogisticOp)

Logistic operator

Computes element-wise Sigmoid of input

Traits: SameOperandsAndResultShape

Interfaces: FixedOutputRangeInterface, NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Results:

Result Description
y tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

tfl.matrix_diag (::mlir::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].

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values

tfl.matrix_set_diag (::mlir::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 .

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

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

tfl.max_pool_2d (::mlir::TFL::MaxPool2DOp)

Max Pool 2D op

Performs max pool 2D on input.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
padding ::mlir::StringAttr padding enum
stride_w ::mlir::IntegerAttr 32-bit signless integer attribute
stride_h ::mlir::IntegerAttr 32-bit signless integer attribute
filter_width ::mlir::IntegerAttr 32-bit signless integer attribute
filter_height ::mlir::IntegerAttr 32-bit signless integer attribute
fused_activation_function ::mlir::StringAttr fused activation enum

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values

tfl.maximum (::mlir::TFL::MaximumOp)

Max operator

Element-wise max operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values
rhs tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values

Results:

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

tfl.mean (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

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

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values

tfl.minimum (::mlir::TFL::MinimumOp)

Min operator

Element-wise min operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values
rhs tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values

Results:

Result Description
min tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values

tfl.mirror_pad (::mlir::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)

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
mode ::mlir::StringAttr Mirror pad enum

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values
pad tensor of 32-bit signless integer or 64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values

tfl.mul (::mlir::TFL::MulOp)

Multiplication operator

Element-wise multiplication operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum

Operands:

Operand Description
lhs tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values
rhs tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values

tfl.multinomial (::mlir::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.

Traits: NoQuantizableResult

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
seed ::mlir::IntegerAttr 64-bit signless integer attribute
seed2 ::mlir::IntegerAttr 64-bit signless integer attribute

Operands:

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

Results:

Result Description
out tensor of 64-bit signless integer values

tfl.neg (::mlir::TFL::NegOp)

Negation operator

Computes element-wise negation of input

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
y tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values

tfl.non_max_suppression_v4 (::mlir::TFL::NonMaxSuppressionV4Op)

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

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
boxes tensor of 32-bit float values
scores tensor of 32-bit float values
max_output_size tensor of 32-bit signless integer values
iou_threshold tensor of 32-bit float values
score_threshold tensor of 32-bit float values

Results:

Result Description
selected_indices tensor of 32-bit signless integer values
valid_outputs tensor of 32-bit signless integer values

tfl.non_max_suppression_v5 (::mlir::TFL::NonMaxSuppressionV5Op)

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

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
boxes tensor of 32-bit float values
scores tensor of 32-bit float values
max_output_size tensor of 32-bit signless integer values
iou_threshold tensor of 32-bit float values
score_threshold tensor of 32-bit float values
soft_nms_sigma tensor of 32-bit float values

Results:

Result Description
selected_indices tensor of 32-bit signless integer values
selected_scores tensor of 32-bit float values
valid_outputs tensor of 32-bit signless integer values

tfl.not_equal (::mlir::TFL::NotEqualOp)

Not_equal operator

Element-wise not_equal operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type or TFLite string type values
rhs tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type or TFLite string type values

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.NumericVerify (::mlir::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: SameOperandsShape

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
tolerance ::mlir::FloatAttr 32-bit float attribute
log_if_failed ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of QI8 type or QUI8 type or QI16 type or 16-bit float or TFLite quint8 type values
ref tensor of 32-bit float values

Results:

Result Description
output tensor of 32-bit float values

tfl.one_hot (::mlir::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).

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
axis ::mlir::IntegerAttr 32-bit signless integer attribute

Operands:

Operand Description
indices tensor of 32-bit signless integer or 64-bit signless integer values
depth tensor of 32-bit signless integer values
on_value tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values
off_value tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values

tfl.prelu (::mlir::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, ResultsBroadcastableShape

Interfaces: AffineQuantizedOpInterface, NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values
alpha tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values

Results:

Result Description
output tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values

tfl.pack (::mlir::TFL::PackOp)

Packs a list of tensors along a dimension into one tensor

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

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

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

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

For example:

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

This is the opposite of unpack .

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
values tensor of any type values

Results:

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

tfl.pad (::mlir::TFL::PadOp)

Padding operator

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

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

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

For example:

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
padding tensor of 32/64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.padv2 (::mlir::TFL::PadV2Op)

Padding operator v2

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

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

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

For example:

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values
padding tensor of 32/64-bit signless integer values
constant_values tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values

tfl.poly_call (::mlir::TFL::PolyCallOp)

Poly call

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

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

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

Traits: SingleBlockImplicitTerminator

Interfaces: RegionBranchOpInterface

Operands:

Operand Description
input tensor of any type values

Results:

Result Description
output tensor of any type values

tfl.pow (::mlir::TFL::PowOp)

Power operator

Element-wise power operation.

Traits: ::mlir::OpTrait::TFLRuntimeOpTrait, NoQuantizableResult, ResultsBroadcastableShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit float or 32-bit signless integer values
rhs tensor of 32-bit float or 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer values

tfl.pseudo_qconst (::mlir::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: FirstAttrDerivedResultType, NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
qtype ::mlir::TypeAttr Tensor type attribute
value ::mlir::ElementsAttr constant vector/tensor attribute

Results:

Result Description
output tensor of QUI8 type or QI8 type or QI16 type or QUI16 type or TFLite quint8 type values

tfl.quantize (::mlir::TFL::QuantizeOp)

Quantize operator

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

Traits: FirstAttrDerivedResultType, NoQuantizableResult, SameOperandsAndResultShape

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
qtype ::mlir::TypeAttr Tensor type attribute

Operands:

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

Results:

Result Description
output tensor of QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

tfl.rfft2d (::mlir::TFL::RFFT2dOp)

2D real-valued fast Fourier transform.

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

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

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of complex type with 32-bit float elements values

tfl.random_standard_normal (::mlir::TFL::RandomStandardNormalOp)

Outputs random values from a normal distribution.

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

Traits: NoQuantizableResult

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
seed ::mlir::IntegerAttr 64-bit signless integer attribute
seed2 ::mlir::IntegerAttr 64-bit signless integer attribute

Operands:

Operand Description
shape tensor of 32-bit signless integer values

Results:

Result Description
out tensor of 32-bit float values

tfl.random_uniform (::mlir::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.

Traits: NoQuantizableResult

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
seed ::mlir::IntegerAttr 64-bit signless integer attribute
seed2 ::mlir::IntegerAttr 64-bit signless integer attribute

Operands:

Operand Description
shape tensor of 32-bit signless integer values

Results:

Result Description
out tensor of 32-bit float values

tfl.range (::mlir::TFL::RangeOp)

Range operator

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
start tensor of 32-bit signless integer or 32-bit float values
limit tensor of 32-bit signless integer or 32-bit float values
delta tensor of 32-bit signless integer or 32-bit float values

Results:

Result Description
result tensor of 32-bit signless integer or 32-bit float values

tfl.rank (::mlir::TFL::RankOp)

Rank operator.

Returns the rank of a tensor.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of any type values

Results:

Result Description
output tensor of any integer type

tfl.read_variable (::mlir::TFL::ReadVariableOp)

Reads variable value.

Read variable data identified by 'resource_id'.

Interfaces: TflRuntimeVerifyOpInterface

Operands:

Operand Description
resource_id tensor of resource values

Results:

Result Description
result tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values

tfl.real (::mlir::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: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or 64-bit float values

tfl.reduce_all (::mlir::TFL::ReduceAllOp)

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

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.reduce_any (::mlir::TFL::ReduceAnyOp)

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

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

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

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

Results:

Result Description
output tensor of 1-bit signless integer values

tfl.reduce_max (::mlir::TFL::ReduceMaxOp)

Max-reduction operator

Computes the max reduction along the specified axes

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
axes tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.reduce_min (::mlir::TFL::ReduceMinOp)

Min-reduction operator

Computes the min reduction along the specified axes

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
axes tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.reduce_prod (::mlir::TFL::ReduceProdOp)

Prod-reduction operator

Computes the product along the specified axes

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
axes tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.relu_n1_to_1 (::mlir::TFL::Relu1Op)

Relu1 operator

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

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
y tensor of 32-bit float or QUI8 type or QI8 type values

tfl.relu6 (::mlir::TFL::Relu6Op)

Relu6 operator

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

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
y tensor of 32-bit float or QUI8 type or QI8 type values

tfl.relu (::mlir::TFL::ReluOp)

Relu operator

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

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
y tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values

tfl.reshape (::mlir::TFL::ReshapeOp)

Reshape operator

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of any type values

tfl.resize_bilinear (::mlir::TFL::ResizeBilinearOp)

ResizeBilinear Op

Resize images to size using bilinear interpolation.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
align_corners ::mlir::BoolAttr bool attribute
half_pixel_centers ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values
size tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values

tfl.resize_nearest_neighbor (::mlir::TFL::ResizeNearestNeighborOp)

ResizeNearestNeighbor Op

Resize images to size using nearest neighbor interpolation.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
align_corners ::mlir::BoolAttr bool attribute
half_pixel_centers ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values
size tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values

tfl.reverse_sequence (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
seq_dim ::mlir::IntegerAttr 32-bit signless integer attribute whose value is non-negative
batch_dim ::mlir::IntegerAttr 32-bit signless integer attribute whose value is non-negative

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values
seq_lengths tensor of 32/64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values

tfl.reverse_v2 (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values
axis tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values

tfl.round (::mlir::TFL::RoundOp)

Round operator

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

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.rsqrt (::mlir::TFL::RsqrtOp)

Reciprocal of square root operator

Computes element-wise reverse square root of input

Traits: SameOperandsAndResultShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
y tensor of 32-bit float or QI8 type or QI16 type values

tfl.svdf (::mlir::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: quant::AccumulatorUniformScale<3, 2, 4>

Interfaces: TFL_StatefulOp, TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
rank ::mlir::IntegerAttr 32-bit signless integer attribute whose value is positive
fused_activation_function ::mlir::StringAttr fused activation enum
asymmetric_quantize_inputs ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or QI8 type values
feature_weights tensor of 32-bit float or QI8 type or QUI8 type values
time_weights tensor of 32-bit float or QI16 type values
input_gate_bias tensor of any type values or none type
activation_state stateful tensor

Results:

Result Description
output tensor of 32-bit float or QI8 type values

tfl.scatter_nd (::mlir::TFL::ScatterNdOp)

Scatter_nd operator

Scatter updates into a new tensor according to indices

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
indices tensor of 32-bit signless integer values
updates tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer values
shape 1D tensor of any type values

Results:

Result Description
output tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer values

tfl.segment_sum (::mlir::TFL::SegmentSumOp)

SegmentSum operator

Computes the sum along segments of a tensor.

Traits: NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer values

tfl.select (::mlir::TFL::SelectOp)

Select operator

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

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
condition tensor of 1-bit signless integer values
x tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 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 QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Results:

Result Description
output tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

tfl.select_v2 (::mlir::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, ResultsBroadcastableShape

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
condition tensor of 1-bit signless integer values
x tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 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 QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

Results:

Result Description
output tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

tfl.shape (::mlir::TFL::ShapeOp)

Shape operator

Returns the shape of a tensor.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
out_type ::mlir::Attribute derived attribute

Operands:

Operand Description
input tensor of any type values

Results:

Result Description
output tensor of 32-bit signless integer or 64-bit signless integer values

tfl.sin (::mlir::TFL::SinOp)

Sine operator

Computes element-wise Sine of input

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.slice (::mlir::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)

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
begin tensor of 32/64-bit signless integer values
size tensor of 32/64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.softmax (::mlir::TFL::SoftmaxOp)

Softmax operator

Computes element-wise softmax activations with the following formula

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

Traits: SameOperandsAndResultShape

Interfaces: FixedOutputRangeInterface, NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
beta ::mlir::FloatAttr 32-bit float attribute

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.space_to_batch_nd (::mlir::TFL::SpaceToBatchNdOp)

SpaceToBatchNd operator

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values
block_shape tensor of 32-bit signless integer values
paddings tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values

tfl.space_to_depth (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values

tfl.pseudo_sparse_const (::mlir::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: FirstAttrDerivedResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Results:

Result Description
output tensor of any type values

tfl.pseudo_sparse_qconst (::mlir::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: FirstAttrDerivedResultType, NoQuantizableResult

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
qtype ::mlir::TypeAttr Tensor type attribute
value ::mlir::ElementsAttr constant vector/tensor attribute
s_param TFL::SparsityParameterAttr Sparsity parameter.
compressed_data ::mlir::ElementsAttr constant vector/tensor attribute

Results:

Result Description
output tensor of QUI8 type or QI8 type or QI16 type or QUI16 type or TFLite quint8 type values

tfl.sparse_to_dense (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
sparse_indices tensor of 32/64-bit signless integer values
output_shape tensor of 32/64-bit signless integer values
sparse_values tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values
default_value tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values

Results:

Result Description
dense tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values

tfl.split (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
split_dim tensor of 32-bit signless integer values
value tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values

Results:

Result Description
outputs tensor of any type values

tfl.split_v (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
value tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values
size_splits 1D tensor of 32-bit signless integer values
split_dim 0D tensor of 32-bit signless integer values

Results:

Result Description
outputs tensor of any type values

tfl.sqrt (::mlir::TFL::SqrtOp)

Square root operator

Computes element-wise Square root of input

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.square (::mlir::TFL::SquareOp)

Square operator

Computes element-wise Square of input

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
x tensor of 32-bit float values

Results:

Result Description
y tensor of 32-bit float values

tfl.squared_difference (::mlir::TFL::SquaredDifferenceOp)

Squared difference operator

Element-wise squared difference operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
lhs tensor of 32-bit float or 32-bit signless integer or QI8 type values
rhs tensor of 32-bit float or 32-bit signless integer or QI8 type values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or QI8 type values

tfl.squeeze (::mlir::TFL::SqueezeOp)

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

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

For example:

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

Or, to remove specific size 1 dimensions:

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
input tensor of any type values

Results:

Result Description
output tensor of any type values

tfl.strided_slice (::mlir::TFL::StridedSliceOp)

StridedSlice Op

Return a strided slice from input .

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
begin_mask ::mlir::IntegerAttr 32-bit signless integer attribute
end_mask ::mlir::IntegerAttr 32-bit signless integer attribute
ellipsis_mask ::mlir::IntegerAttr 32-bit signless integer attribute
new_axis_mask ::mlir::IntegerAttr 32-bit signless integer attribute
shrink_axis_mask ::mlir::IntegerAttr 32-bit signless integer attribute

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or 1-bit signless integer or 16-bit signless integer or QI16 type or TFLite quint8 type or TFLite string type values
begin tensor of 32-bit signless integer values
end tensor of 32-bit signless integer values
strides tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 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 (::mlir::TFL::SubOp)

Subtraction operator

Element-wise subtraction operation.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum

Operands:

Operand Description
lhs tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values
rhs tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values

tfl.sum (::mlir::TFL::SumOp)

Sum operator

Computes the sum reduction along the specified axes

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
keep_dims ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values
axes tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values

tfl.tanh (::mlir::TFL::TanhOp)

Hyperbolic tangent operator

Computes element-wise Hyperbolic tangent of input

Traits: SameOperandsAndResultShape

Interfaces: FixedOutputRangeInterface, NoSideEffect (MemoryEffectOpInterface), TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values

tfl.tile (::mlir::TFL::TileOp)

Tile operator.

Constructs a tensor by tiling a given tensor.

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

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values
multiples tensor of 32/64-bit signless integer values

Results:

Result Description
output tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values

tfl.topk_v2 (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit float or 8-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 32-bit signless integer values

Results:

Result Description
values tensor of 32-bit float or 8-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 32-bit signless integer values

tfl.transpose_conv (::mlir::TFL::TransposeConvOp)

Transpose convolution operator

Performs transpose convolution operation on input.

Traits: quant::AccumulatorUniformScale<3, 1, 2>, quant::AffineOpCoefficient<0, 1>

Interfaces: AffineQuantizedOpInterface, NoSideEffect (MemoryEffectOpInterface), TFL_SparseOp, TflArithmeticCountOpInterface, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
padding ::mlir::StringAttr padding enum
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

Operands:

Operand Description
output_shape tensor of 32-bit signless integer values
weights tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values
input tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values
bias tensor of any type values or none type

Results:

Result Description
output tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values

tfl.transpose (::mlir::TFL::TransposeOp)

Transpose operator

Returns the Transpose of x

Interfaces: NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 32-bit signless integer or 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values
perm tensor of 32-bit signless integer values

Results:

Result Description
output tensor of 32-bit signless integer or 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values

tfl.unidirectional_sequence_lstm (::mlir::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”).

Interfaces: InferTypeOpInterface, TFL_StatefulOp, TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
fused_activation_function ::mlir::StringAttr fused activation enum
cell_clip ::mlir::FloatAttr 32-bit float attribute whose value is non-negative
proj_clip ::mlir::FloatAttr 32-bit float attribute whose value is non-negative
time_major ::mlir::BoolAttr bool attribute
asymmetric_quantize_inputs ::mlir::BoolAttr bool attribute
input_to_input_intermediate ::mlir::TypeAttr any type attribute
input_to_forget_intermediate ::mlir::TypeAttr any type attribute
input_to_cell_intermediate ::mlir::TypeAttr any type attribute
input_to_output_intermediate ::mlir::TypeAttr any type attribute
effective_hidden_scale_intermediate ::mlir::TypeAttr any type attribute

Operands:

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

Results:

Result Description
output tensor of 32-bit float or QI8 type values

tfl.unidirectional_sequence_rnn (::mlir::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”).

Interfaces: TFL_StatefulOp, TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
time_major ::mlir::BoolAttr bool attribute
fused_activation_function ::mlir::StringAttr fused activation enum
asymmetric_quantize_inputs ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of 32-bit float values
input_to_input_weights tensor of 32-bit float or QI8 type values
recurrent_to_input_weights tensor of 32-bit float or QI8 type values
input_gate_bias tensor of 32-bit float values
hidden_state stateful tensor

Results:

Result Description
output tensor of 32-bit float values

tfl.unique (::mlir::TFL::UniqueOp)

Unique Op.

This operation returns a tensor output containing all of the unique elements input sorted in the same order that they occur in input . This operation so returns a tensor idx the same size as x that contains the index of each lue of input in the unique output output . In other words:

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute MLIR Type Description
idx_out_type ::mlir::Attribute derived attribute

Operands:

Operand Description
input tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values

Results:

Result Description
output tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values
idx tensor of 32/64-bit signless integer values

tfl.unpack (::mlir::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: SameOperandsAndResultElementType

Interfaces: InferTypeOpInterface, NoSideEffect (MemoryEffectOpInterface), SameOperandsAndResultsScale, TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description
input tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit signless integer or QI8 type or QUI8 type or 16-bit signless integer or QI16 type values

Results:

Result Description
outputs tensor of any type values

tfl.var_handle (::mlir::TFL::VarHandleOp)

Returns a handle to a variable resource from its name.

Returns a handle for a variable resource from its name. container: the container this variable is placed in. shared_name: the name by which this variable is referred to.

Interfaces: TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
container ::mlir::StringAttr string attribute
shared_name ::mlir::StringAttr string attribute

Results:

Result Description
resource_handle tensor of resource values

tfl.where (::mlir::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.

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
condition tensor of 1-bit signless integer or 32-bit float or 32/64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer values

Results:

Result Description
index tensor of 64-bit signless integer values

tfl.while (::mlir::TFL::WhileOp)

While loop

output = input; while (cond(output)) { output = body(output) }

While loop where all values are passes through arguments with implicit capture.

input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. cond: A region that takes 'input' and returns a boolean scalar tensor. body: A region that takes a list of tensors and returns another list of tensors. Both lists have the same types.

Traits: SingleBlockImplicitTerminator

Interfaces: LoopLikeOpInterface, TflRuntimeVerifyOpInterface

Attributes:

Attribute MLIR Type Description
is_stateless ::mlir::BoolAttr bool attribute

Operands:

Operand Description
input tensor of any type values

Results:

Result Description
output tensor of any type values

tfl.yield (::mlir::TFL::YieldOp)

Yield operation

The "yield" operation represents a return operation within the conditional and body of structured control flow (eg, while). The operation takes 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: Terminator

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
operands any type

tfl.zeros_like (::mlir::TFL::ZerosLikeOp)

ZerosLike operator

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

Traits: NoQuantizableResult, SameOperandsAndResultShape, SameOperandsAndResultType

Interfaces: NoSideEffect (MemoryEffectOpInterface), TflRuntimeVerifyOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description
input tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values

Results:

Result Description
output tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values