The TensorFlow Lite dialect. This dialect maps to TensorFlow Lite operations. Invariants: - All values are of Tensor type (in particular, scalars are represented using zero-dimensional tensors); ## Operations ### `tfl.abs` (TFL::AbsOp) *Absolute value operator* Given a tensor `x`, this operation returns a tensor containing the absolute value of each element in `x`. For example, if x is an input element and y is an output element, this operation computes \\(y = \|x\|\\). Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 16-bit signless integer or 32-bit signless integer or 32-bit float or QI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 16-bit signless integer or 32-bit signless integer or 32-bit float or QI8 type or QI16 type values | ### `tfl.add` (TFL::AddOp) *Addition operator* Element-wise addition operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-bit float or 16-bit signless integer 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 16-bit float or 16-bit signless integer 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 16-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.add_n` (TFL::AddNOp) _Add*n operator* Adds all input tensors element-wise. Traits: `AlwaysSpeculatableImplTrait`, `Commutative` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `inputs` | variadic of tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `sum` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.arg_max` (TFL::ArgMaxOp) *ArgMax operator* Returns the index with the largest value across dimensions of a tensor. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `output_type` | ::mlir::Attribute | derived attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values | | `dim` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32/64-bit signless integer values | ### `tfl.arg_min` (TFL::ArgMinOp) *ArgMin operator* Returns the index with the smallest value across dimensions of a tensor. a = \[1, 10, 26.9, 2.8, 166.32, 62.3\] b = tf.math.argmin(input = a) c = tf.keras.backend.eval(b) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `output_type` | ::mlir::Attribute | derived attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values | | `dim` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32/64-bit signless integer values | ### `tfl.assign_variable` (TFL::AssignVariableOp) *Assigns a new value to a variable.* Any ReadVariableOp with a control dependency on this op is guaranteed to return this value or a subsequent newer value of the variable. Interfaces: `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `resource_id` | tensor of resource values | | `value` | 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.atan2` (TFL::Atan2Op) *Atan2 operation* The "atan2" operation computes the arctangent of y/x element-wise, respecting signs of the arguments. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultType` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `y` | tensor of 32-bit float or 64-bit float values | | `x` | tensor of 32-bit float or 64-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 64-bit float values | ### `tfl.average_pool_2d` (TFL::AveragePool2DOp) _Average_pool*2d operator* Performs average-pooling operation on input. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `filter_height` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `filter_width` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | ### `tfl.basic_lstm` (TFL::BasicLSTMOp) *The basic lstm operator* basic LSTM Cell Operator. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `cell_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `proj_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `kernel_type` | ::mlir::TFL::LSTMKernelTypeAttr | lstm_kernel_type whose value is mlir::TFL::LSTMKernelType::BASIC | #### Operands: | Operand | Description | |---|---| | `data_input` | tensor of 32-bit float or QUI8 type values | | `prev_activ_input` | tensor of 32-bit float or QUI8 type values | | `weights_input` | tensor of 32-bit float or QUI8 type values | | `biases_input` | tensor of 32-bit float or QI32 type values | | `prev_state_input` | tensor of 32-bit float or QI16 type values | #### Results: | Result | Description | |---|---| | `activ_output` | 2D tensor of any non-token type values | | `state_output` | 2D tensor of any non-token type values | | `concat_temp` | 2D tensor of any non-token type values | | `activ_temp` | 2D tensor of any non-token type values | ### `tfl.batch_matmul` (TFL::BatchMatMulOp) *Batch Matrix Multiply Operator* Performs a batched matrix multiplication on the inputs. Follows the conventions of TensorFlow BatchMatMulV2, with support for unknown dimensions in the batch dimensions and broadcasting. Inputs: `inputs[0]`: required: input LHS `inputs[1]`: required: input RHS `adjoint_lhs`: optional: Transpose LHS (default false) `adjoint_rhs`: optional: Transpose RHS (default false) Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `adj_x` | ::mlir::BoolAttr | bool attribute | | `adj_y` | ::mlir::BoolAttr | bool attribute | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-bit float or QI8 type or QI16 type or 8-bit signless integer values | | `y` | tensor of 32-bit float or 16-bit float or QI8 type or QI16 type or 8-bit signless integer or blockwise quantized type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or QI8 type or QI16 type or 32-bit signless integer values | ### `tfl.batch_to_space_nd` (TFL::BatchToSpaceNdOp) *BatchToSpaceNd operator* This operation reshapes the "batch" dimension 0 into space dimensions. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values | | `block_shape` | tensor of 32-bit signless integer values | | `indices` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float 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 values | ### `tfl.bidirectional_sequence_lstm` (TFL::BidirectionalSequenceLSTMOp) *Bidirectional sequence lstm operator* Bidirectional lstm is essentially two lstms, one running forward \& the other running backward. And the output is the concatenation of the two lstms. Traits: `QuantizableResult` Interfaces: `DynamicRangeQuantizedOpInterface`, `TFL_StatefulOp`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `cell_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `proj_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `merge_outputs` | ::mlir::BoolAttr | bool attribute | | `time_major` | ::mlir::BoolAttr | bool attribute | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer values | | `fw_input_to_input_weights` | tensor of any non-token type values or none type | | `fw_input_to_forget_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_input_to_cell_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_input_to_output_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_recurrent_to_input_weights` | tensor of any non-token type values or none type | | `fw_recurrent_to_forget_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_recurrent_to_cell_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_recurrent_to_output_weights` | tensor of 32-bit float or 8-bit signless integer values | | `fw_cell_to_input_weights` | tensor of any non-token type values or none type | | `fw_cell_to_forget_weights` | tensor of any non-token type values or none type | | `fw_cell_to_output_weights` | tensor of any non-token type values or none type | | `fw_input_gate_bias` | tensor of any non-token type values or none type | | `fw_forget_gate_bias` | tensor of 32-bit float values | | `fw_cell_bias` | tensor of 32-bit float values | | `fw_output_gate_bias` | tensor of 32-bit float values | | `fw_projection_weights` | tensor of any non-token type values or none type | | `fw_projection_bias` | tensor of any non-token type values or none type | | `bw_input_to_input_weights` | tensor of any non-token type values or none type | | `bw_input_to_forget_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_input_to_cell_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_input_to_output_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_recurrent_to_input_weights` | tensor of any non-token type values or none type | | `bw_recurrent_to_forget_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_recurrent_to_cell_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_recurrent_to_output_weights` | tensor of 32-bit float or 8-bit signless integer values | | `bw_cell_to_input_weights` | tensor of any non-token type values or none type | | `bw_cell_to_forget_weights` | tensor of any non-token type values or none type | | `bw_cell_to_output_weights` | tensor of any non-token type values or none type | | `bw_input_gate_bias` | tensor of any non-token type values or none type | | `bw_forget_gate_bias` | tensor of 32-bit float values | | `bw_cell_bias` | tensor of 32-bit float values | | `bw_output_gate_bias` | tensor of 32-bit float values | | `bw_projection_weights` | tensor of any non-token type values or none type | | `bw_projection_bias` | tensor of any non-token type values or none type | | `fw_input_activation_state` | stateful tensor | | `fw_input_cell_state` | stateful tensor | | `bw_input_activation_state` | stateful tensor | | `bw_input_cell_state` | stateful tensor | | `aux_input` | tensor of any non-token type values or none type | | `fw_aux_input_to_input_weights` | tensor of any non-token type values or none type | | `fw_aux_input_to_forget_weights` | tensor of any non-token type values or none type | | `fw_aux_input_to_cell_weights` | tensor of any non-token type values or none type | | `fw_aux_input_to_output_weights` | tensor of any non-token type values or none type | | `bw_aux_input_to_input_weights` | tensor of any non-token type values or none type | | `bw_aux_input_to_forget_weights` | tensor of any non-token type values or none type | | `bw_aux_input_to_cell_weights` | tensor of any non-token type values or none type | | `bw_aux_input_to_output_weights` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `fw_output` | tensor of any non-token type values | | `bw_output` | tensor of any non-token type values | ### `tfl.bitcast` (TFL::BitcastOp) *Bitcast operator* Bitcasts a tensor from one type to another. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.bitwise_xor` (TFL::BitwiseXorOp) *Bitwise Xor operator* Elementwise computes the bitwise XOR of `lhs` and `rhs`. Traits: `AlwaysSpeculatableImplTrait`, `Commutative`, `ResultsBroadcastableShape`, `SameOperandsAndResultElementType` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | | `rhs` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | ### `tfl.blockwise_dequantize` (TFL::BlockwiseDequantizeOp) *Blockwise dequantize operator* Reconstructs floating point values from a blockwise quantized tensor as `scale * (input - zero_point)`. A missing `zero_points` operand means a zero point of 0. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `block_shape` | ::mlir::ArrayAttr | 64-bit integer array attribute | | `symmetric` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 2-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer values | | `scales` | tensor of f8E8M0FNU type or 16-bit float or bfloat16 type or 32-bit float values | | `zero_points` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.blockwise_quantize` (TFL::BlockwiseQuantizeOp) *Blockwise dynamic quantize operator* Computes a per-block scale (and, when asymmetric, zero point) from the input at runtime and quantizes the input with them. The computed parameters are returned alongside the quantized tensor so that a matching `tfl.blockwise_dequantize` can undo the transformation. The scale of a block is `max(abs(block)) / (q_max + range_dilation)`, where `q_max` is the largest representable value of the output storage type. `range_dilation` widens the denominator so that outliers clip instead of stretching the whole block; it defaults to 0. When `scale_type` is `f8E8M0FNU`, the scale is rounded up to the next power of two. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `block_shape` | ::mlir::ArrayAttr | 64-bit integer array attribute | | `scale_type` | ::mlir::TypeAttr | scale element type attribute | | `symmetric` | ::mlir::BoolAttr | bool attribute | | `range_dilation` | ::mlir::FloatAttr | 32-bit float attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 2-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer values | | `scale` | tensor of f8E8M0FNU type or 16-bit float or bfloat16 type or 32-bit float values | | `zero_point` | tensor of any non-token type values or none type | ### `tfl.broadcast_args` (TFL::BroadcastArgsOp) *Return the shape of s0 op s1 with broadcast.* Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `s0` | tensor of 32/64-bit signless integer values | | `s1` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `r0` | tensor of 32/64-bit signless integer values | ### `tfl.broadcast_to` (TFL::BroadcastToOp) *Broadcast an array for a compatible shape.* Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward. For example, > > > x = tf.constant(\[1, 2, 3\]) > > > y = tf.broadcast_to(x, \[3, 3\]) > > > print(y) > > > tf.Tensor( > > > \[\[1 2 3\] > > > \[1 2 3\] > > > \[1 2 3\]\], shape=(3, 3), dtype=int32) In the above example, the input Tensor with the shape of `[1, 3]` is broadcasted to output Tensor with shape of `[3, 3]`. When doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized. However, `broadcast_to` does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, `broadcast_to` might be fused to subsequent operation and then be optimized away, however.) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or f8E4M3FN type or f8E5M2 type or 32-bit signless integer or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or 32-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 64-bit signless integer or complex type with 32-bit float elements values | | `shape` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or f8E4M3FN type or f8E5M2 type or 32-bit signless integer or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or 32-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 64-bit signless integer or complex type with 32-bit float elements values | ### `tfl.bucketize` (TFL::BucketizeOp) *Bucketizes 'input' based on 'boundaries'.* Example: If the inputs are `boundaries = [0, 10, 100]` and `input = [[-5, 10000][150, 10][5, 100]]`, then the output will be `output = [[0, 3][3, 2][1, 3]]`. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `boundaries` | ::mlir::ArrayAttr | 32-bit float array attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 64-bit float or 32-bit signless integer or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit signless integer values | ### `tfl.call_once` (TFL::CallOnceOp) *Invokes an initialization function* This operation invokes the given initialization function for the session initializer in tf saved model dialect. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `session_init_function` | ::mlir::StringAttr | string attribute | ### `tfl.cast` (TFL::CastOp) *Cast operator* Casts input from input type to output type. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 16-bit float or bfloat16 type or f8E4M3FN type or f8E5M2 type or 32-bit float or 64-bit float or 1-bit signless integer or 2-bit signless integer or 4-bit signless integer or 4-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or TFLite quint8 type or 8-bit unsigned integer or 8-bit signless integer or complex type with 32-bit float elements values | #### Results: | Result | Description | |---|---| | `output` | tensor of 16-bit float or bfloat16 type or f8E4M3FN type or f8E5M2 type or 32-bit float or 64-bit float or 1-bit signless integer or 2-bit signless integer or 4-bit signless integer or 4-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or TFLite quint8 type or 8-bit unsigned integer or 8-bit signless integer or complex type with 32-bit float elements values | ### `tfl.ceil` (TFL::CeilOp) *Ceil operator* Returns element-wise ceil value of the input. Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float values | ### `tfl.complex_abs` (TFL::ComplexAbsOp) *Computes the complex absolute value of a tensor.* Given a tensor `x` of complex numbers, this operation returns a tensor of type `float` or `double` that is the absolute value of each element in `x`. All elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute value is computed as \\( \\sqrt{a\^2 + b\^2}\\). Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 64-bit float values | ### `tfl.concatenation` (TFL::ConcatenationOp) *Concatenation operator* Concatenates tensors along one dimension Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `axis` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `values` | variadic of tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or f8E4M3FN type or f8E5M2 type or 64-bit signless integer or 32-bit signless integer or 16-bit signless integer or QI4 type or 8-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer values | ### `tfl.control_node` (TFL::ControlNodeOp) *The `TFL.control_node` operation wraps single-block operations in order to attach control edges.* This is used to wrap regions and attach control dependencies to them. Typically, this will happen in one of the last steps before emitting the flatbuffer model in order to enable optimizations that rely on a fixed order of operations (such as rematerialization.) The flatbuffer exporter will unwrap the wrapped region and annotate the generated model with metadata such that any runtime reorderings will respect the order given by the control dependencies. Traits: `HasParent`, `RecursiveMemoryEffects`, `SingleBlockImplicitTerminator`, `SingleBlock` #### Operands: | Operand | Description | |---|---| | `controlInputs` | variadic of control | #### Results: | Result | Description | |---|---| | `outputs` | variadic of tensor of any non-token type values | | `control` | control | ### `tfl.conv_2d` (TFL::Conv2DOp) *Convolution operator* Performs convolution operation on inputs. Inputs: `inputs[0]`: required: the input activation tensor `inputs[1]`: required: the filter weight tensor `inputs[2]`: optional: the bias tensor Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `TFL::AccumulatorUniformScale<2, 0, 1>`, `TFL::AffineOpCoefficient<0, 1>` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TFL_SparseOp`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `dilation_h_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_w_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | | `filter` | tensor of 32-bit float or QI4 type or QI8 type or QUI8 type values | | `bias` | tensor of any non-token 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.conv_3d` (TFL::Conv3DOp) *Convolution 3D operator* Performs convolution operation on 3D inputs. Inputs: `inputs[0]`: required: the input activation tensor `inputs[1]`: required: the filter weight tensor `inputs[2]`: optional: the bias tensor Traits: `AlwaysSpeculatableImplTrait`, `TFL::AccumulatorUniformScale<2, 0, 1>` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `dilation_d_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_h_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_w_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_d` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | | `filter` | tensor of 32-bit float values | | `bias` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.conv_3d_transpose` (TFL::Conv3DTransposeOp) *Transposed Convolution 3D operator* Performs transposed convolution operation on 3D inputs. Inputs: `inputs[0]`: required: the shape of output tensor `inputs[1]`: required: the filter weight tensor `inputs[2]`: required: the input activation tensor `inputs[3]`: optional: the bias tensor Traits: `AlwaysSpeculatableImplTrait`, `TFL::AccumulatorUniformScale<3, 1, 2>` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `dilation_d_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_h_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_w_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_d` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `output_shape` | tensor of 32-bit signless integer values | | `filter` | tensor of 32-bit float values | | `input` | tensor of 32-bit float values | | `bias` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.cos` (TFL::CosOp) *Cosine operator* Computes element-wise Cosine of input Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or 16-bit float values | ### `tfl.cumsum` (TFL::CumsumOp) *Cumsum operator* Compute the cumulative sum of the tensor x along axis. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `exclusive` | ::mlir::BoolAttr | bool attribute | | `reverse` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values | | `axis` | 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 values | ### `tfl.custom` (TFL::CustomOp) *Custom op* A generic op for any TFLite custom operation. input: A list of inputs in the original op. custom_code: A string used to identify which exactly this op is, which corresponds to operator_codes.custom_code in the flatbuffer. custom_option: a holder to save the op attributes in bytes fashion. output: A list of outputs in the original op. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `custom_code` | ::mlir::StringAttr | string attribute | | `custom_option` | ::mlir::TFL::ConstBytesAttr | A string attribute representation of compiled bytes | #### Operands: | Operand | Description | |---|---| | `input` | variadic of tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | variadic of tensor of any non-token type values | ### `tfl.custom_tf` (TFL::CustomTfOp) *Wrapper Op for TF custom ops.* A wrapper op around any Custom TF op. These includes ops defined using custom_opdefs or linked which are not defined in TF dialect. This Op just wraps the custom op inside a region. Note #1, this Op will not include TF Lite custom ops defined using CustomOp. Note #2, this op is just internal representation inside the converter and are not exposed/exported when the model is exported to Flatbuffer. Traits: `IsolatedFromAbove`, `RecursiveMemoryEffects`, `SingleBlockImplicitTerminator`, `SingleBlock` Interfaces: `InferTypeOpInterface`, `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `input` | variadic of tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | variadic of tensor of any non-token type values | ### `tfl.densify` (TFL::DensifyOp) *Densify operator* Converts sparse tensor to dense format. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer values | ### `tfl.depth_to_space` (TFL::DepthToSpaceOp) *DepthToSpace operator* Rearranges data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the `depth` dimension are moved in spatial blocks to the `height` and `width` dimensions. The attr `block_size` indicates the input block size and how the data is moved. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `block_size` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite quint8 type or 8-bit unsigned integer or QI8 type or QUI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite quint8 type or 8-bit unsigned integer or QI8 type or QUI8 type values | ### `tfl.depthwise_conv_2d` (TFL::DepthwiseConv2DOp) *Depthwise-separable convolution operator* Performs convolution operation on inputs. Inputs: `inputs[0]`: required: the input activation tensor `inputs[1]`: required: the filter weight tensor `inputs[2]`: optional: the bias tensor Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `TFL::AccumulatorUniformScale<2, 0, 1>`, `TFL::AffineOpCoefficient<3, 1>` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TFL_SparseOp`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `dilation_h_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `dilation_w_factor` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `depth_multiplier` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | | `filter` | tensor of 32-bit float or QI4 type or QI8 type or QUI8 type values | | `bias` | tensor of any non-token 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.dequantize` (TFL::DequantizeOp) *Dequantize operator* Converts quantized array of integers to floating-points according to the quantization parameters. The input may also be blockwise quantized (`!tfl.blockwise_quant`), in which case each block of the input is dequantized with its own scale and zero point. Interfaces: `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of QI2 type or QI4 type or QUI4 type or QI8 type or QUI8 type or QI16 type or 16-bit float or f8E4M3FN type or f8E5M2 type or blockwise quantized type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float values | ### `tfl.dilate` (TFL::DilateOp) *Dilation operator* Extends a tensor by adding new elements between the existing ones. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 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 16-bit unsigned integer or 32-bit unsigned integer or 64-bit unsigned integer or 32-bit float or 64-bit float values | | `dilations` | tensor of 32-bit signless integer values | | `padding_value` | 0D tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or 16-bit unsigned integer or 32-bit unsigned integer or 64-bit unsigned integer or 32-bit float or 64-bit float values | ### `tfl.div` (TFL::DivOp) *Division operator* Element-wise division operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or QUI8 type or QI8 type or QI16 type values | | `rhs` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or QUI8 type or QI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or QUI8 type or QI8 type or QI16 type values | ### `tfl.dynamic_update_slice` (TFL::DynamicUpdateSliceOp) *DynamicUpdateSlice.* DynamicUpdateSlice op that have the same semantics with XLA DynamicUpdateSlice. Generates a result which is the value of the input array operand, with a slice update overwritten at start_indices. See Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `operand` | tensor of 1-bit signless integer or 4-bit signless integer or QI4 type or QI8 type or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float or 16-bit float or bfloat16 type values | | `update` | tensor of 1-bit signless integer or 4-bit signless integer or QI4 type or QI8 type or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float or 16-bit float or bfloat16 type values | | `start_indices` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer or 4-bit signless integer or QI4 type or QI8 type or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float or 16-bit float or bfloat16 type values | ### `tfl.elu` (TFL::EluOp) *Exponential Linear Unit operator* Computes the exponential linear f(x) -\> exp(x) - 1 for x \< 0, x for x \>= 0. element-wise. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 8-bit signless integer values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or 8-bit signless integer values | ### `tfl.embedding_lookup` (TFL::EmbeddingLookupOp) *Embedding lookup operator* Looks up ids in a list of embedding tensors. A `!tfl.blockwise_quant` table is dequantized row by row into a float output. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lookup` | tensor of 32-bit signless integer values | | `value` | tensor of 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI4 type or QI2 type or blockwise quantized type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer or 8-bit unsigned integer values | ### `tfl.equal` (TFL::EqualOp) *Equal operator* Returns the truth element of x == y element-wise Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 1-bit signless integer or 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 8-bit unsigned integer or TFLite string type values | | `y` | tensor of 1-bit signless integer or 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 8-bit unsigned integer or TFLite string type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.exp` (TFL::ExpOp) *Natural exponentiation operator* Performs element-wise natural exponentiation operation on input. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-bit float or QI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or 16-bit float or QI8 type or QI16 type values | ### `tfl.expand_dims` (TFL::ExpandDimsOp) *Inserts a dimension of 1 into a tensor's shape.* Given a tensor `input`, this operation inserts a dimension of 1 at the dimension index `axis` of `input`'s shape. The dimension index `axis` starts at zero; if you specify a negative number for `axis` it is counted backward from the end. This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape `[height, width, channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, which will make the shape `[1, height, width, channels]`. Other examples: # 't' is a tensor of shape [2] shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] shape(expand_dims(t, -1)) ==> [2, 1] # 't2' is a tensor of shape [2, 3, 5] shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] This operation requires that: `-1-input.dims() <= dim <= input.dims()` This operation is related to `squeeze()`, which removes dimensions of size 1. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | | `dim` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.external_const` (TFL::ExternalConstOp) *External const op.* External const op that can hold : - `buffer_index` which points to a constant in the flatbuffer. - `external_buffer` which contains metadata for external buffer outside flatbuffer. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `buffer_index` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `external_buffer` | ::mlir::TFL::ExternalBufferAttr | Flatbuffer external buffer metadata. | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.fake_quant` (TFL::FakeQuantOp) *FakeQuant operator* Fake-quantize the 'inputs' tensor of type float via float scalars min and max to 'outputs' tensor of same shape as inputs. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `min` | ::mlir::FloatAttr | 32-bit float attribute | | `max` | ::mlir::FloatAttr | 32-bit float attribute | | `num_bits` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose minimum value is 2 whose maximum value is 16 | | `narrow_range` | ::mlir::BoolAttr | bool attribute whose value is false | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.fill` (TFL::FillOp) *Fill the tensor with given value.* Fill the tensor with given value. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `dims` | tensor of 32/64-bit signless integer values | | `input` | tensor of 32-bit float or 16-bit float or f8E4M3FN type or f8E5M2 type or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values | #### Results: | Result | Description | |---|---| | `result` | tensor of 32-bit float or 16-bit float or f8E4M3FN type or f8E5M2 type or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values | ### `tfl.floor` (TFL::FloorOp) *Floor operator* Returns element-wise floor value of the input. Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float values | ### `tfl.floor_div` (TFL::FloorDivOp) *Floor div operator* Element-wise floor div operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values | | `rhs` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer values | ### `tfl.floor_mod` (TFL::FloorModOp) *Division reminder* Element-wise division reminder operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values | | `rhs` | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values | ### `tfl.fully_connected` (TFL::FullyConnectedOp) *Fully connected op* Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `TFL::AccumulatorUniformScale<2, 0, 1>`, `TFL::AffineOpCoefficient<0, 1>` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TFL_SparseOp`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `weights_format` | ::mlir::StringAttr | string attribute whose value is DEFAULT, or SHUFFLED4x16INT8 | | `keep_num_dims` | ::mlir::BoolAttr | bool attribute | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | | `quant_spec` | ::mlir::Attribute | derived attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or QI8 type or QUI8 type or QI16 type or QUI16 type values | | `filter` | tensor of 32-bit float or 16-bit float or QI2 type or QI4 type or QI8 type or QUI8 type or QI16 type or blockwise quantized type values | | `bias` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | variadic of tensor of any non-token type values | ### `tfl.gather` (TFL::GatherOp) *Gather operator* Gather slices from `params` axis `axis` according to `indices`. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `axis` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `batch_dims` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `params` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values | | `indices` | tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 4-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or TFLite string type or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.gather_nd` (TFL::GatherNdOp) _Gather*nd operator* Gather slices from `params` into a Tensor with shape specified by `indices`. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `params` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or QI8 type or TFLite string type values | | `indices` | tensor of 16-bit signless integer or 32-bit signless integer or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or QI8 type or TFLite string type values | ### `tfl.gelu` (TFL::GeluOp) *GELU activation function.* Computes GELU activation function element-wise. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `approximate` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or QI8 type or QUI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or QI8 type or QUI8 type values | ### `tfl.greater` (TFL::GreaterOp) *Greater operator* Element-wise greater operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-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 16-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.greater_equal` (TFL::GreaterEqualOp) _Greater*equal operator* Element-wise greater_equal operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values | | `rhs` | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.hard_swish` (TFL::HardSwishOp) *Hardswish activation function.* Computes hard-swish activation function f(x) -\> (x \* relu6(x+3))/6 element-wise. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QUI8 type or QI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QUI8 type or QI8 type values | ### `tfl.hashtable` (TFL::HashtableOp) *Creates a non-initialized hash table.* This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `table_id` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `key_dtype` | ::mlir::TypeAttr | any type attribute | | `value_dtype` | ::mlir::TypeAttr | any type attribute | #### Results: | Result | Description | |---|---| | `out` | tensor of resource values | ### `tfl.hashtable_find` (TFL::HashtableFindOp) *Looks up keys in a table, outputs the corresponding values.* The tensor `keys` must of the same type as the keys of the table. The output `values` is of the type of the table values. The scalar `default_value` is the value output for keys not present in the table. It must also be of the same type as the table values. Interfaces: `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `hash_table` | tensor of resource values | | `keys` | tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values | | `default_value` | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `out` | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values | ### `tfl.hashtable_import` (TFL::HashtableImportOp) *Replaces the contents of the table with the specified keys and values.* The tensor `keys` must be of the same type as the keys of the table. The tensor `values` must be of the type of the table values. Interfaces: `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `hash_table` | tensor of resource values | | `keys` | tensor of 32-bit signless integer or TFLite string type or 64-bit signless integer values | | `values` | tensor of 32-bit float or 32-bit signless integer or TFLite string type or 64-bit signless integer values | ### `tfl.hashtable_size` (TFL::HashtableSizeOp) *Computes the number of elements in the given table.* Interfaces: `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `hash_table` | tensor of resource values | #### Results: | Result | Description | |---|---| | `out` | tensor of 64-bit signless integer values | ### `tfl.if` (TFL::IfOp) *If-then-else operation* The `tfl.if` operation represents an if-then-else construct for conditionally executing two regions of code. The operand to an if operation is a boolean value. For example: tfl.if %b { ... } else { ... } `tfl.if` may also return results that are defined in its regions. The values defined are determined by which execution path is taken. Example: %x, %y = tfl.if %b -> (tensor, tensor) { %x_true = ... %y_true = ... tfl.yield %x_true, %y_true : tensor, tensor } else { %x_false = ... %y_false = ... tfl.yield %x_false, %y_false : tensor, tensor } `tfl.if` regions are always terminated with "tfl.yield". If "tfl.if" defines no values, the "tfl.yield" can be left out, and will be inserted implicitly. Otherwise, it must be explicit. Also, if "tfl.if" defines one or more values, the 'else' block cannot be omitted. Example: tfl.if %b { ... } Traits: `NoRegionArguments`, `RecursiveMemoryEffects`, `SingleBlockImplicitTerminator`, `SingleBlock` Interfaces: `RegionBranchOpInterface`, `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `cond` | tensor of 1-bit signless integer values | #### Results: | Result | Description | |---|---| | `results` | variadic of tensor of any non-token type values | ### `tfl.imag` (TFL::ImagOp) *Returns the imaginary part of a complex number.* Given a tensor `input` of complex numbers, this operation returns a tensor of type `float` that is the imaginary part of each element in `input`. All elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real part and *b* is the imaginary part returned by this operation. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 64-bit float values | ### `tfl.l2_normalization` (TFL::L2NormalizationOp) *L2 Normalize Operator* L2Normalization Op Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `FixedOutputRangeInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QUI8 type or QI8 type or QUI16 type or QI16 type or 8-bit signless integer values | ### `tfl.leaky_relu` (TFL::LeakyReluOp) *Leaky Relu operator* Element-wise Leaky ReLU operator x -\> x \>= 0 ? x : (alpha \* x) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `alpha` | ::mlir::FloatAttr | 32-bit float attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type or QI16 type values | ### `tfl.less` (TFL::LessOp) *Less operator* Element-wise less operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values | | `rhs` | tensor of 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.less_equal` (TFL::LessEqualOp) _Less*equal operator* Element-wise less_equal operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values | | `rhs` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.local_response_normalization` (TFL::LocalResponseNormalizationOp) *Local Response Normalization.* The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) output = input / (bias + alpha * sqr_sum) ** beta For details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `radius` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `bias` | ::mlir::FloatAttr | 32-bit float attribute | | `alpha` | ::mlir::FloatAttr | 32-bit float attribute | | `beta` | ::mlir::FloatAttr | 32-bit float attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.log` (TFL::LogOp) *Natural logarithm operator* Performs element-wise natural logarithm operation on input. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QI8 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QI8 type values | ### `tfl.log_softmax` (TFL::LogSoftmaxOp) *Log softmax operator* Computes element-wise log softmax activations with the following formula input - log(reduce_sum(exp(input), dim)) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `FixedOutputRangeInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QUI8 type or QI8 type or TFLite quint8 type values | ### `tfl.logical_and` (TFL::LogicalAndOp) *Logical AND operator* Element-wise logical AND operation. Traits: `AlwaysSpeculatableImplTrait`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 1-bit signless integer values | | `rhs` | tensor of 1-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.logical_not` (TFL::LogicalNotOp) *Logical NOT operator* Element-wise logical NOT operation. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultType` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 1-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.logical_or` (TFL::LogicalOrOp) *Logical OR operator* Element-wise logical OR operation. Traits: `AlwaysSpeculatableImplTrait`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 1-bit signless integer values | | `rhs` | tensor of 1-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.logistic` (TFL::LogisticOp) *Logistic operator* Computes element-wise Sigmoid of input Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `FixedOutputRangeInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-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 16-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.lstm` (TFL::LSTMOp) *The full lstm operator* Long short-term memory unit (LSTM) recurrent network layer. The default non-peephole implementation is based on: S. Hochreiter and J. Schmidhuber. 'Long Short-Term Memory'. Neural Computation, 9(8):1735-1780, 1997. The peephole implementation is based on: 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: Greff et al. 'LSTM: A Search Space Odyssey' The layer normalization is based on: Ba et al. 'Layer Normalization' Traits: `QuantizableResult` Interfaces: `DynamicRangeQuantizedOpInterface`, `TFL_StatefulOp`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `cell_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `proj_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `kernel_type` | ::mlir::TFL::LSTMKernelTypeAttr | lstm_kernel_type whose value is mlir::TFL::LSTMKernelType::FULL | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | | `input_to_input_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_forget_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_cell_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_output_intermediate` | ::mlir::TypeAttr | any type attribute | | `effective_hidden_scale_intermediate` | ::mlir::TypeAttr | any type attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type or QI16 type values | | `input_to_input_weights` | tensor of any non-token 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 non-token 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 non-token type values or none type | | `cell_to_forget_weights` | tensor of any non-token type values or none type | | `cell_to_output_weights` | tensor of any non-token type values or none type | | `input_gate_bias` | tensor of any non-token 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 non-token type values or none type | | `projection_bias` | tensor of any non-token type values or none type | | `input_activation_state` | stateful tensor | | `input_cell_state` | stateful tensor | | `input_layer_norm_coefficients` | tensor of any non-token type values or none type | | `forget_layer_norm_coefficients` | tensor of any non-token type values or none type | | `cell_layer_norm_coefficients` | tensor of any non-token type values or none type | | `output_layer_norm_coefficients` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.matrix_diag` (TFL::MatrixDiagOp) *Returns a tensor with the provided diagonal and everything else padded with zeros.* Given a diagonal, returns a tensor with the diagonal and everything else padded with zeros. Assume diagonal has k dimensions `[I, J, K, ..., N]`, then the output is a tensor of rank `k+1` with dimensions `[I, J, K, ..., N, N]` where: `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n].` Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `diagonal` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QUI8 type or QI8 type or TFLite quint8 type values | ### `tfl.matrix_set_diag` (TFL::MatrixSetDiagOp) *Returns a batched matrix tensor with new batched diagonal values.* Given `input` and `diagonal`, this operation returns a tensor with the same shape and values as `input`, except for the main diagonal of the innermost matrices. These will be overwritten by the values in `diagonal`. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values | | `diagonal` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `result` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QI16 type or QUI8 type or TFLite quint8 type values | ### `tfl.max_pool_2d` (TFL::MaxPool2DOp) *Max Pool 2D op* Performs max pool 2D on input. Inputs: `inputs[0]`: required: the input tensor Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `filter_width` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `filter_height` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values | ### `tfl.maximum` (TFL::MaximumOp) *Max operator* Element-wise max operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-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 16-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 16-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.mean` (TFL::MeanOp) *Mean operator* Computes the mean of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values | | `axis` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or QI16 type values | ### `tfl.minimum` (TFL::MinimumOp) *Min operator* Element-wise min operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values | | `rhs` | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `min` | tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.mirror_pad` (TFL::MirrorPadOp) *MirrorPad Operator. Pads a tensor with mirrored values.* This operation pads a input with mirrored values according to the paddings you specify. paddings is an integer tensor with shape \[n, 2\], where n is the rank of input. For each dimension D of input, paddings\[D, 0\] indicates how many values to add before the contents of input in that dimension, and paddings\[D, 1\] indicates how many values to add after the contents of input in that dimension. Both paddings\[D, 0\] and paddings\[D, 1\] must be no greater than input.dim_size(D) (or input.dim_size(D) - 1) if copy_border is true (if false, respectively). The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `mode` | ::mlir::TFL::MirrorPaddingTypeAttr | mirror_pad_enum | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values | | `pad` | tensor of 32-bit signless integer or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.mul` (TFL::MulOp) *Multiplication operator* Element-wise multiplication operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values | | `rhs` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values | ### `tfl.multinomial` (TFL::MultinomialOp) *Draws samples from a categorical distribution.* The generated values will have a categorical distribution based on the `logits` or unnormalized log-probabilities provided for all classes. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `seed` | ::mlir::IntegerAttr | 64-bit signless integer attribute | | `seed2` | ::mlir::IntegerAttr | 64-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `logits` | tensor of 32-bit float values | | `num_samples` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `out` | tensor of 32-bit signless integer or 64-bit signless integer values | ### `tfl.neg` (TFL::NegOp) *Negation operator* Computes element-wise negation of input Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer values | ### `tfl.no_value` (TFL::NoValueOp) *Constant representing no value.* No value constant op. Traits: `AlwaysSpeculatableImplTrait`, `ConstantLike` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `value` | ::mlir::UnitAttr | unit attribute | #### Results: | Result | Description | |---|---| | `none_val` | none type | ### `tfl.non_max_suppression_v4` (TFL::NonMaxSuppressionV4Op) *Greedily selects a subset of bounding boxes in descending order of score,* pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than `score_threshold` are removed. Bounding boxes are supplied as \[y1, x1, y2, x2\], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval \[0, 1\]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the `tf.gather operation`. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices) Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `boxes` | tensor of 32-bit float values | | `scores` | tensor of 32-bit float values | | `max_output_size` | tensor of 32-bit signless integer values | | `iou_threshold` | tensor of 32-bit float values | | `score_threshold` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `selected_indices` | tensor of 32-bit signless integer values | | `valid_outputs` | tensor of 32-bit signless integer values | ### `tfl.non_max_suppression_v5` (TFL::NonMaxSuppressionV5Op) *Greedily selects a subset of bounding boxes in descending order of score,* pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than `score_threshold` are removed. Bounding boxes are supplied as \[y1, x1, y2, x2\], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., 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 (c.f. Bodla et al, ) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. To enable this Soft-NMS mode, set the `soft_nms_sigma` parameter to be larger than 0. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `boxes` | tensor of 32-bit float values | | `scores` | tensor of 32-bit float values | | `max_output_size` | tensor of 32-bit signless integer values | | `iou_threshold` | tensor of 32-bit float values | | `score_threshold` | tensor of 32-bit float values | | `soft_nms_sigma` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `selected_indices` | tensor of 32-bit signless integer values | | `selected_scores` | tensor of 32-bit float values | | `valid_outputs` | tensor of 32-bit signless integer values | ### `tfl.not_equal` (TFL::NotEqualOp) _Not*equal operator* Element-wise not_equal operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `Commutative`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or QI16 type or TFLite quint8 type or TFLite string type values | | `rhs` | tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or QI16 type or TFLite quint8 type or TFLite string type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.NumericVerify` (TFL::NumericVerifyOp) *Verifies the numericals of the two operands* The NumericVerify op is a debugging op to verify the numericals of the two activations. It is a custom op in TFLite. If log_if_failed is true, the NumericVerify op calculates statistics on differences between float and quantized activations, output logs, set differences to the output tensors, and throws an error if errors above tolerance exist. If log_if_failed = false, then it doesn't care about errors. Traits: `QuantizableResult`, `SameOperandsShape` Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `tolerance` | ::mlir::FloatAttr | 32-bit float attribute | | `log_if_failed` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of QI8 type or QUI8 type or QI16 type or 16-bit float or TFLite quint8 type values | | `ref` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.one_hot` (TFL::OneHotOp) *OneHot operator* Returns a one-hot tensor.The locations represented by indices in `indices` take value `on_value`, while all other locations take value `off_value`. If the input `indices` is rank `N`, the output will have rank `N+1`, The new axis is created at dimension `axis` (default: the new axis is appended at the end). Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `axis` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `indices` | tensor of 32-bit signless integer or 64-bit signless integer values | | `depth` | tensor of 32-bit signless integer values | | `on_value` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values | | `off_value` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or 8-bit signless integer or 8-bit unsigned integer values | ### `tfl.pack` (TFL::PackOp) *Packs a list of tensors along a dimension into one tensor* Packs a list of `values_count` rank-`R` tensors into one rank-`(R+1)` tensor. Packs the `values_count` tensors in `values` into a tensor with rank one higher than each tensor in `values`, by packing them along the `axis` dimension. Given a list of tensors of shape `(A, B, C)`; if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc. For example: # 'x' is [1, 4] # 'y' is [2, 5] # 'z' is [3, 6] pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] This is the opposite of `unpack`. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `values_count` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | | `axis` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `values` | variadic of tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.pad` (TFL::PadOp) *Padding operator* This operation pads a `input` with zeros according to the `paddings` you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many zeros to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many zeros to add after the contents of `input` in that dimension. The padded size of each dimension D of the output is: `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` For example: # 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1], [2, 2]] # rank of 't' is 2 pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] [0, 0, 1, 1, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0, 0, 0]] Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | | `padding` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.padv2` (TFL::PadV2Op) *Padding operator v2* This operation pads a `input` according to the `paddings` and `constant_values` you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many zeros to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many zeros to add after the contents of `input` in that dimension. `constant_values` is a scalar tensor of the same type as `input` that indicates the value to use for padding `input`. The padded size of each dimension D of the output is: `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` For example: # 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1], [2, 2]] # rank of 't' is 2 pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] [0, 0, 1, 1, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0, 0, 0]] Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values | | `padding` | tensor of 32/64-bit signless integer values | | `constant_values` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values | ### `tfl.poly_call` (TFL::PolyCallOp) *Poly call* Have multiple function bodies for the same computation. This allows a program compiler/interpreter to choose one of the available options to execute the program based on which one is most suitable for the target backend. input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. call: Multiple regions, each of which encapsulates the same semantic computation but in different forms. Traits: `SingleBlockImplicitTerminator`, `SingleBlock` Interfaces: `RegionBranchOpInterface` #### Operands: | Operand | Description | |---|---| | `input` | variadic of tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | variadic of tensor of any non-token type values | ### `tfl.pow` (TFL::PowOp) *Power operator* Element-wise power operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 32-bit signless integer values | | `rhs` | tensor of 32-bit float or 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.prelu` (TFL::PReluOp) *Parameterized Relu operator* Parameterized Relu operator x -\> x \>= 0 ? x : (alpha \* x) where alpha is a trainable tensor. input and alpha should be the same size as input or be broadcastable. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape`, `TFL::AffineOpCoefficient<-1, 1>` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values | | `alpha` | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type values | ### `tfl.pseudo_const` (TFL::ConstOp) *Constant pseudo op.* Represents a constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. The op is allowed to have all the same type of attributes as tf.Const does (e.g., opaque TF attributes are allowed). Traits: `AlwaysSpeculatableImplTrait`, `ConstantLike`, `FirstAttrDerivedResultType`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `value` | ::mlir::ElementsAttr | constant vector/tensor attribute | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.pseudo_qconst` (TFL::QConstOp) *Quantized constant pseudo op* Represents a quantized constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. The quantization parameters are stored as a type attribute in this constant. Traits: `AlwaysSpeculatableImplTrait`, `FirstAttrDerivedResultType` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `qtype` | ::mlir::TypeAttr | Tensor type attribute | | `value` | ::mlir::ElementsAttr | constant vector/tensor attribute | #### Results: | Result | Description | |---|---| | `output` | tensor of QUI8 type or QI8 type or QUI4 type or QI4 type or QI16 type or QUI16 type or TFLite quint8 type or blockwise quantized type values | ### `tfl.pseudo_sparse_const` (TFL::SparseConstOp) *Sparse constant pseudo op.* Represents a sparse constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. Traits: `AlwaysSpeculatableImplTrait`, `FirstAttrDerivedResultType`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `value` | ::mlir::ElementsAttr | constant vector/tensor attribute | | `s_param` | ::mlir::TFL::SparsityParameterAttr | Sparsity parameter. | | `compressed_data` | ::mlir::ElementsAttr | constant vector/tensor attribute | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.pseudo_sparse_qconst` (TFL::SparseQConstOp) *Sparse quantized constant pseudo op* Represents a sparse quantized constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead. The quantization parameters are stored as a type attribute in this constant. Traits: `AlwaysSpeculatableImplTrait`, `FirstAttrDerivedResultType` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `qtype` | ::mlir::TypeAttr | Tensor type attribute | | `value` | ::mlir::ElementsAttr | constant vector/tensor attribute | | `s_param` | ::mlir::TFL::SparsityParameterAttr | Sparsity parameter. | | `compressed_data` | ::mlir::ElementsAttr | constant vector/tensor attribute | #### Results: | Result | Description | |---|---| | `output` | tensor of QUI8 type or QI8 type or QUI4 type or QI4 type or QI16 type or QUI16 type or TFLite quint8 type values | ### `tfl.quantize` (TFL::QuantizeOp) *Quantize operator* Converts floating point tensors to quantized integer tensors according to the quantization parameters defined in the type attribute. Traits: `FirstAttrDerivedResultType`, `SameOperandsAndResultShape` Interfaces: `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `qtype` | ::mlir::TypeAttr | Tensor type attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or QI4 type or QUI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of QI4 type or QUI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.random_standard_normal` (TFL::RandomStandardNormalOp) *Outputs random values from a normal distribution.* The generated values will have mean 0 and standard deviation 1. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `seed` | ::mlir::IntegerAttr | 64-bit signless integer attribute | | `seed2` | ::mlir::IntegerAttr | 64-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `shape` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `out` | tensor of 32-bit float values | ### `tfl.random_uniform` (TFL::RandomUniformOp) *Outputs random values from a uniform distribution.* The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `seed` | ::mlir::IntegerAttr | 64-bit signless integer attribute | | `seed2` | ::mlir::IntegerAttr | 64-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `shape` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `out` | tensor of 32-bit float values | ### `tfl.range` (TFL::RangeOp) *Range operator* Returns a 1D tensor defined by a sequence from `start` to `limit` with a given `delta`. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `start` | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values | | `limit` | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values | | `delta` | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values | #### Results: | Result | Description | |---|---| | `result` | tensor of 32-bit signless integer or 32-bit float or 64-bit signless integer values | ### `tfl.rank` (TFL::RankOp) *Rank operator.* Returns the rank of a tensor. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of any integer type | ### `tfl.read_variable` (TFL::ReadVariableOp) *Reads variable value.* Read variable data identified by 'resource_id'. Interfaces: `TflRuntimeVerifyOpInterface` #### Operands: | Operand | Description | |---|---| | `resource_id` | tensor of resource values | #### Results: | Result | Description | |---|---| | `result` | tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values | ### `tfl.real` (TFL::RealOp) *Returns the real part of a complex number.* Given a tensor `input` of complex numbers, this operation returns a tensor of type `float` that is the real part of each element in `input`. All elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real part returned by this operation and *b* is the imaginary part. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of complex type with 32-bit float elements or complex type with 64-bit float elements values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 64-bit float values | ### `tfl.reduce_all` (TFL::ReduceAllOp) *Computes the "logical and" of elements across dimensions of a tensor.* Reduces `input` along the dimensions given in `axis`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `axis`. If `keep_dims` is true, the reduced dimensions are retained with length 1. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 1-bit signless integer values | | `reduction_indices` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.reduce_any` (TFL::ReduceAnyOp) *Computes the "logical or" of elements across dimensions of a tensor.* Reduces `input` along the dimensions given in `axis`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `axis`. If `keep_dims` is true, the reduced dimensions are retained with length 1. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 1-bit signless integer values | | `reduction_indices` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 1-bit signless integer values | ### `tfl.reduce_max` (TFL::ReduceMaxOp) *Max-reduction operator* Computes the max reduction along the specified axes Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-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 16-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.reduce_min` (TFL::ReduceMinOp) *Min-reduction operator* Computes the min reduction along the specified axes Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | | `axes` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.reduce_prod` (TFL::ReduceProdOp) *Prod-reduction operator* Computes the product along the specified axes Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | | `axes` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.relu` (TFL::ReluOp) *Relu operator* Element-wise Relu operator x -\> max(0, x) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QUI8 type or QI8 type or QI16 type values | ### `tfl.relu6` (TFL::Relu6Op) *Relu6 operator* Element-wise Relu6 operator x -\> max(0, min(6, x)) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QUI8 type or QI8 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QUI8 type or QI8 type values | ### `tfl.relu_0_to_1` (TFL::Relu0To1Op) *Relu0To1 operator* Element-wise Relu0To1 operator x -\> max(0, min(1, x)) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QUI8 type or QI8 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QUI8 type or QI8 type values | ### `tfl.relu_n1_to_1` (TFL::Relu1Op) *Relu1 operator* Element-wise Relu1 operator x -\> max(-1, min(1, x)) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QUI8 type or QI8 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QUI8 type or QI8 type values | ### `tfl.reshape` (TFL::ReshapeOp) *Reshape operator* Produces a tensor with the same values but different static shape defined by the output type. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | | `shape` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.resize_bilinear` (TFL::ResizeBilinearOp) *ResizeBilinear Op* Resize `images` to `size` using bilinear interpolation. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `align_corners` | ::mlir::BoolAttr | bool attribute | | `half_pixel_centers` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values | | `size` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values | ### `tfl.resize_nearest_neighbor` (TFL::ResizeNearestNeighborOp) *ResizeNearestNeighbor Op* Resize `images` to `size` using nearest neighbor interpolation. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `align_corners` | ::mlir::BoolAttr | bool attribute | | `half_pixel_centers` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values | | `size` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or TFLite quint8 type or QUI8 type or QI8 type or QI16 type values | ### `tfl.reverse_sequence` (TFL::ReverseSequenceOp) *Reverses variable length slices.* This op first slices `input` along the dimension `batch_dim`, and for each slice `i`, reverses the first `seq_lengths[i]` elements along the dimension `seq_dim`. The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`, and `seq_lengths` must be a vector of length `input.dims[batch_dim]`. The output slice `i` along dimension `batch_dim` is then given by input slice `i`, with the first `seq_lengths[i]` slices along dimension `seq_dim` reversed. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `seq_dim` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative | | `batch_dim` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values | | `seq_lengths` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or TFLite quint8 type values | ### `tfl.reverse_v2` (TFL::ReverseV2Op) *ReverseV2 Operator* Reverses specific dimensions of a tensor. Given a tensor, and a int32/int64 tensor axis representing the set of dimensions of tensor to reverse. This operation reverses each dimension i for which there exists j s.t. axis\[j\] == i. Args: tensor: A Tensor. Must be one of the following types: uint8, int8, int16, int32, int64, float32, bool Up to 8-D. axis: A Tensor. Must be one of the following types: int32, int64. with only 1 element which is the axis index. TODO: Add support for multiple elements. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type 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 f8E4M3FN type or f8E5M2 type or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values | ### `tfl.rfft2d` (TFL::RFFT2dOp) *2D real-valued fast Fourier transform.* Computes the 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of `input`. Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the `fft_length / 2 + 1` unique components of the FFT for the inner-most dimension of `output`: the zero-frequency term, followed by the `fft_length / 2` positive-frequency terms. Along each axis `RFFT2D` is computed on, if `fft_length` is smaller than the corresponding dimension of `input`, the dimension is cropped. If it is larger, the dimension is padded with zeros. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | | `fft_length` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of complex type with 32-bit float elements values | ### `tfl.right_shift` (TFL::RightShiftOp) *Right Shift operator* Elementwise computes the bitwise right-shift of `lhs` by `rhs`. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultElementType` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | | `rhs` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 8-bit signless integer or 8-bit unsigned integer or 16-bit signless integer or 16-bit unsigned integer or 32-bit signless integer or 32-bit unsigned integer values | ### `tfl.round` (TFL::RoundOp) *Round operator* Rounds the values of a tensor to the nearest integer, element-wise. Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float values | ### `tfl.rsqrt` (TFL::RsqrtOp) *Reciprocal of square root operator* Computes element-wise reverse square root of input Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or QI8 type or QI16 type values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or QI8 type or QI16 type values | ### `tfl.scatter_nd` (TFL::ScatterNdOp) _Scatter*nd operator* Scatter `updates` into a new tensor according to `indices` Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `indices` | tensor of 32-bit signless integer values | | `updates` | tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or 1-bit signless integer values | | `shape` | 1D tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 8-bit signless integer or 64-bit signless integer or 32-bit signless integer or 8-bit unsigned integer or 1-bit signless integer values | ### `tfl.segment_sum` (TFL::SegmentSumOp) *SegmentSum operator* Computes the sum along segments of a tensor. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer values | | `segment_ids` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.select` (TFL::SelectOp) *Select operator* Select values of 'x' if the corresponding value of 'condition' is true or the value of 'y' if false. There are valid condition input sizes: 1. Either the same shape (in which case the select is elementwise), or 2. condition must be Rank 1 and match over the first dimension. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `condition` | tensor of 1-bit signless integer values | | `x` | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | | `y` | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.select_v2` (TFL::SelectV2Op) *SelectV2 operator* Select values of 'x' if the corresponding value of 'condition' is true or the value of 'y' if false. There are valid condition input sizes: 1. Either the same shape (in which case the select is elementwise), or 2. Broadcastable shapes between 'condition', 'x' and 'y'. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `condition` | tensor of 1-bit signless integer values | | `x` | tensor of 32-bit float or 16-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | | `y` | tensor of 32-bit float or 16-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 1-bit signless integer or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit unsigned integer or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.shape` (TFL::ShapeOp) *Shape operator* Returns the shape of a tensor. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `out_type` | ::mlir::Attribute | derived attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit signless integer or 64-bit signless integer values | ### `tfl.sign` (TFL::SignOp) *Sign operation* Returns NaN if x is NaN, 0 if x is 0, -1 if x \< 0 and 1 if x \> 0. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultType` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 64-bit float or 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 64-bit float or 32-bit signless integer values | ### `tfl.sin` (TFL::SinOp) *Sine operator* Computes element-wise Sine of input Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float or 16-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float or 16-bit float values | ### `tfl.slice` (TFL::SliceOp) *Return a slice from 'input'.* The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'. `begin` is zero-based; `size` is one-based. If size\[i\] is -1, all remaining elements in dimension i are included in the slice. In other words, this is equivalent to setting: size\[i\] = input.dim_size(i) - begin\[i\] *Requirements*: 0 \<= begin\[i\] \<= begin\[i\] + size\[i\] \<= Di for i in \[0, n) Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or QI4 type or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | | `begin` | tensor of 32/64-bit signless integer values | | `size` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or QI4 type or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.softmax` (TFL::SoftmaxOp) *Softmax operator* Computes element-wise softmax activations with the following formula exp(input \* beta) / tf.reduce_sum(exp(input \* beta), dim) Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `FixedOutputRangeInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` 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 16-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 16-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.space_to_batch_nd` (TFL::SpaceToBatchNdOp) *SpaceToBatchNd operator* This operation reshapes space dimensions into the "batch" dimension 0 Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | | `block_shape` | tensor of 32-bit signless integer values | | `paddings` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.space_to_depth` (TFL::SpaceToDepthOp) *SpaceToDepth operator* Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the `height` and `width` dimensions are moved to the `depth` dimension. `block_size` indicates the input block size. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `block_size` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type values | ### `tfl.sparse_to_dense` (TFL::SparseToDenseOp) *Converts a sparse representation into a dense tensor.* Builds an array `dense` with shape `output_shape` such that # If sparse_indices is scalar dense[i] = (i == sparse_indices ? sparse_values : default_value) # If sparse_indices is a vector, then for each i dense[sparse_indices[i]] = sparse_values[i] # If sparse_indices is an n by d matrix, then for each i in [0, n) dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] All other values in `dense` are set to `default_value`. If `sparse_values` is a scalar, all sparse indices are set to this single value. Indices should be sorted in lexicographic order, and indices must not contain any repeats. If `validate_indices` is true, these properties are checked during execution. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `sparse_indices` | tensor of 32/64-bit signless integer values | | `output_shape` | tensor of 32/64-bit signless integer values | | `sparse_values` | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values | | `default_value` | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values | #### Results: | Result | Description | |---|---| | `dense` | tensor of 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or TFLite quint8 type or 32-bit float values | ### `tfl.split` (TFL::SplitOp) *Splits a tensor into `num_split` tensors along one dimension.* Splits the `value` tensor along `split_dim` into a number of sub-tensors with same shape as the original one, except for `split_dim`. Same as tf.Split. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `num_splits` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | #### Operands: | Operand | Description | |---|---| | `split_dim` | tensor of 32-bit signless integer values | | `value` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type 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` | variadic of tensor of any non-token type values | ### `tfl.split_v` (TFL::SplitVOp) *Splits a tensor into `num_split` tensors along one dimension.* Splits the `value` tensor along `split_dim` into a number of sub-tensors with same shape as the original one, except for `split_dim`. The grouping of the resultant sub-tensors is decided by `size-splits`. Same as tf.SplitV. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `num_splits` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | #### Operands: | Operand | Description | |---|---| | `value` | tensor of 32-bit float or f8E4M3FN type or f8E5M2 type 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` | variadic of tensor of any non-token type values | ### `tfl.sqrt` (TFL::SqrtOp) *Square root operator* Computes element-wise Square root of input Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` 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.square` (TFL::SquareOp) *Square operator* Computes element-wise Square of input Traits: `AlwaysSpeculatableImplTrait`, `InferTensorType`, `TFL::SameOperandsAndResultTypeResolveRef` Interfaces: `ConditionallySpeculatable`, `InferShapedTypeOpInterface`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `x` | tensor of 32-bit float values | #### Results: | Result | Description | |---|---| | `y` | tensor of 32-bit float values | ### `tfl.squared_difference` (TFL::SquaredDifferenceOp) *Squared difference operator* Element-wise squared difference operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 32-bit signless integer or QI8 type values | | `rhs` | tensor of 32-bit float or 32-bit signless integer or QI8 type values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer or QI8 type values | ### `tfl.squeeze` (TFL::SqueezeOp) *Removes dimensions of size 1 from the shape of a tensor.* Given a tensor `input`, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying `squeeze_dims`. For example: # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t)) ==> [2, 3] Or, to remove specific size 1 dimensions: # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `squeeze_dims` | ::mlir::ArrayAttr | 64-bit integer array attribute whose size is at most 8 | #### Operands: | Operand | Description | |---|---| | `input` | tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | tensor of any non-token type values | ### `tfl.strided_slice` (TFL::StridedSliceOp) *StridedSlice Op* Return a strided slice from `input`. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `begin_mask` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `end_mask` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `ellipsis_mask` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `new_axis_mask` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `shrink_axis_mask` | ::mlir::IntegerAttr | 32-bit signless integer attribute | | `offset` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or 1-bit signless integer or 16-bit signless integer or QI16 type or TFLite quint8 type or TFLite string type values | | `begin` | tensor of 32-bit signless integer values | | `end` | tensor of 32-bit signless integer values | | `strides` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 16-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or QI8 type or QUI8 type or 1-bit signless integer or 16-bit signless integer or QI16 type or TFLite quint8 type or TFLite string type values | ### `tfl.sub` (TFL::SubOp) *Subtraction operator* Element-wise subtraction operation. Traits: `::mlir::OpTrait::TFLRuntimeOpTrait`, `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `ResultsBroadcastableShape` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `lhs` | tensor of 32-bit float or 16-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 16-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 16-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values | ### `tfl.sum` (TFL::SumOp) *Sum operator* Computes the sum reduction along the specified axes Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `keep_dims` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-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 16-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values | ### `tfl.svdf` (TFL::SVDFOp) *Single value decomposition filter operator* The SVDF op is a decomposition of a densely connected op into low rank filters. For details: Traits: `QuantizableResult`, `TFL::AccumulatorUniformScale<3, 2, 4>` Interfaces: `DynamicRangeQuantizedOpInterface`, `RequiresQuantizedBiasInterface`, `TFL_StatefulOp`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `rank` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or QI8 type values | | `feature_weights` | tensor of 32-bit float or QI8 type or QUI8 type values | | `time_weights` | tensor of 32-bit float or QI16 type values | | `input_gate_bias` | tensor of any non-token type values or none type | | `activation_state` | stateful tensor | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QI8 type values | ### `tfl.tanh` (TFL::TanhOp) *Hyperbolic tangent operator* Computes element-wise Hyperbolic tangent of input Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultShape` Interfaces: `ConditionallySpeculatable`, `FixedOutputRangeInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-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 16-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values | ### `tfl.tile` (TFL::TileOp) *Tile operator.* Constructs a tensor by tiling a given tensor. This operation creates a new tensor by replicating input multiples times. The output tensor's i'th dimension has input.dims(i) \* multiples\[i\] elements, and the values of input are replicated multiples\[i\] times along the 'i'th dimension. For example, tiling \[a b c d\] by \[2\] produces \[a b c d a b c d\]. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values | | `multiples` | tensor of 32/64-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 1-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite string type values | ### `tfl.topk_v2` (TFL::TopKV2Op) *TopK operator* Returns the top `k` largest element along each last dimensional slice of `input` and the indices of values within the last dimension of the input tensor. Results are always sorted in the descending order. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values | | `k` | tensor of 16-bit signless integer or 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `values` | tensor of 32-bit float or 8-bit signless integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type values | | `indices` | tensor of 16-bit signless integer or 32-bit signless integer values | ### `tfl.transpose` (TFL::TransposeOp) *Transpose operator* Returns the Transpose of x Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit signless integer or 32-bit float or 16-bit float or bfloat16 type or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or 4-bit signless integer or QI4 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values | | `perm` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit signless integer or 32-bit float or 16-bit float or bfloat16 type or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or 4-bit signless integer or QI4 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values | ### `tfl.transpose_conv` (TFL::TransposeConvOp) *Transpose convolution operator* Performs transpose convolution operation on input. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `TFL::AccumulatorUniformScale<3, 1, 2>`, `TFL::AffineOpCoefficient<0, 1>` Interfaces: `AffineQuantizedOpInterface`, `ConditionallySpeculatable`, `DynamicRangeQuantizedOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `RequiresQuantizedBiasInterface`, `TFL_SparseOp`, `TflArithmeticCountOpInterface`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `padding` | ::mlir::StringAttr | string attribute whose value is SAME, or VALID | | `stride_h` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | | `stride_w` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is positive | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | #### Operands: | Operand | Description | |---|---| | `output_shape` | tensor of 32-bit signless integer values | | `weights` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | | `input` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | | `bias` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QI8 type or QUI8 type or QI16 type values | ### `tfl.unidirectional_sequence_lstm` (TFL::UnidirectionalSequenceLSTMOp) *Unidirectional sequence lstm operator* A recurrent neural network specified by an LSTM cell. This Op supports unrolling the input along the time or batch dimensions, and implements the following operation for each element in the sequence s = 1...sequence_length: outputs\[s\] = state = activation(LSTMOp(inputs\[s\])) where LSTMOp is LSTM TF Lite Op and the "activation" is the function passed as the "fused_activation_function" argument (if not "NONE"). Traits: `QuantizableResult` Interfaces: `DynamicRangeQuantizedOpInterface`, `InferTypeOpInterface`, `TFL_StatefulOp`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `cell_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `proj_clip` | ::mlir::FloatAttr | 32-bit float attribute whose value is non-negative | | `time_major` | ::mlir::BoolAttr | bool attribute | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | | `diagonal_recurrent_tensors` | ::mlir::BoolAttr | bool attribute | | `input_to_input_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_forget_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_cell_intermediate` | ::mlir::TypeAttr | any type attribute | | `input_to_output_intermediate` | ::mlir::TypeAttr | any type attribute | | `effective_hidden_scale_intermediate` | ::mlir::TypeAttr | any type attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | | `input_to_input_weights` | tensor of any non-token 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 non-token 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 non-token type values or none type | | `cell_to_forget_weights` | tensor of any non-token type values or none type | | `cell_to_output_weights` | tensor of any non-token type values or none type | | `input_gate_bias` | tensor of any non-token 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 non-token type values or none type | | `projection_bias` | tensor of any non-token type values or none type | | `input_activation_state` | stateful tensor | | `input_cell_state` | stateful tensor | | `input_layer_norm_coefficients` | tensor of any non-token type values or none type | | `forget_layer_norm_coefficients` | tensor of any non-token type values or none type | | `cell_layer_norm_coefficients` | tensor of any non-token type values or none type | | `output_layer_norm_coefficients` | tensor of any non-token type values or none type | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or QI8 type values | ### `tfl.unidirectional_sequence_rnn` (TFL::UnidirectionalSequenceRNNOp) *Unidirectional sequence rnn operator* A recurrent neural network specified by an RNN cell. This Op takes in input in a format {batch_size, seq_len, input_size} or {seq_len, batch_size, input_size} if it's time-majored. It implements the following operation for each element in the sequence s = 1...sequence_length: outputs\[s\] = state = activation(RNNOp(inputs\[s\])) where RNNOp is RNNOp TF Lite Op and the "activation" is the function passed as the "fused_activation_function" argument (if not "NONE"). Traits: `QuantizableResult` Interfaces: `DynamicRangeQuantizedOpInterface`, `TFL_StatefulOp`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `time_major` | ::mlir::BoolAttr | bool attribute | | `fused_activation_function` | ::mlir::StringAttr | string attribute whose value is NONE, or RELU, or RELU_N1_TO_1, or RELU6, or TANH, or SIGN_BIT | | `asymmetric_quantize_inputs` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float values | | `input_to_input_weights` | tensor of 32-bit float or QI8 type values | | `recurrent_to_input_weights` | tensor of 32-bit float or QI8 type values | | `input_gate_bias` | tensor of 32-bit float values | | `hidden_state` | stateful tensor | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float values | ### `tfl.unique` (TFL::UniqueOp) *Unique Op.* This operation returns a tensor `output` containing all of the unique elements of `input` sorted in the same order that they occur in `input`. This operation also returns a tensor `idx` the same size as `x` that contains the index of each value of `input` in the unique output `output`. In other words: Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `idx_out_type` | ::mlir::Attribute | derived attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 8-bit signless integer or QI8 type or 8-bit unsigned integer or QUI8 type or 16-bit signless integer or QI16 type or 32-bit signless integer or 64-bit signless integer or 32-bit float values | | `idx` | tensor of 32/64-bit signless integer values | ### `tfl.unpack` (TFL::UnpackOp) *Unpacks a tensor along a dimension into multiple tensors* Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. For example, given a tensor of shape `(A, B, C, D)`; If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`. (Note that the dimension unpacked along is gone, unlike `split`). If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`. Etc. This is the opposite of `pack`. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `SameOperandsAndResultElementType` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `SameOperandsAndResultsScale`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `num` | ::mlir::IntegerAttr | 32-bit signless integer attribute whose value is non-negative | | `axis` | ::mlir::IntegerAttr | 32-bit signless integer attribute | #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 16-bit float or bfloat16 type or f8E4M3FN type or f8E5M2 type 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` | variadic of tensor of any non-token type values | ### `tfl.unsorted_segment_max` (TFL::UnsortedSegmentMaxOp) *UnsortedSegmentMax operator* Computes the maximum value along segments of a tensor such that output\[i\] = max(data\[j....\]) where segment_ids\[j...\] = i if the maximum is empty for a given segment ID i, it outputs the smallest possible value for the specific numeric type, output\[i\] = numeric_limits::lowest(). Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer values | | `segment_ids` | tensor of 32-bit signless integer values | | `num_segments` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.unsorted_segment_min` (TFL::UnsortedSegmentMinOp) *UnsortedSegmentMin operator* Computes the minimum value along segments of a tensor such that output\[i\] = min(data\[j....\]) where segment_ids\[j...\] = i if the minimum is empty for a given segment ID i, it outputs the largest possible value for the specific numeric type, output\[i\] = numeric_limits::max(). Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer values | | `segment_ids` | tensor of 32-bit signless integer values | | `num_segments` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.unsorted_segment_prod` (TFL::UnsortedSegmentProdOp) *UnsortedSegmentProd operator* Computes the product along segments of a tensor. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer values | | `segment_ids` | tensor of 32-bit signless integer values | | `num_segments` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.unsorted_segment_sum` (TFL::UnsortedSegmentSumOp) *UnsortedSegmentSum operator* From a tensor segmentation, computes the `output` resulting from summing together elements mapped to the same segment_id. I.e. `output[i]` is equal to the tensor sum of all elements from the input tensor mapped to segment_id `i`. If no tensors are mapped to a particular included segment_id, the output at that indice will be a zero tensor with the appropriate shape. Note the values of segment_ids are always validated to be less than num_segments and an error is thrown for out-of-bound indices Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 32-bit float or 32-bit signless integer values | | `segment_ids` | tensor of 32-bit signless integer values | | `num_segments` | tensor of 32-bit signless integer values | #### Results: | Result | Description | |---|---| | `output` | tensor of 32-bit float or 32-bit signless integer values | ### `tfl.var_handle` (TFL::VarHandleOp) *Returns a handle to a variable resource from its name.* Returns a handle for a variable resource from its name. container: the container this variable is placed in. shared_name: the name by which this variable is referred to. Interfaces: `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `container` | ::mlir::StringAttr | string attribute | | `shared_name` | ::mlir::StringAttr | string attribute | #### Results: | Result | Description | |---|---| | `resource_handle` | tensor of resource values | ### `tfl.where` (TFL::WhereOp) *Returns locations of nonzero / true values in a tensor.* This operation returns the coordinates of true elements in `condition`. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in `condition`. Indices are output in row-major order. Traits: `AlwaysSpeculatableImplTrait` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `condition` | tensor of 1-bit signless integer or 32-bit float or 32/64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer values | #### Results: | Result | Description | |---|---| | `index` | tensor of 64-bit signless integer values | ### `tfl.while` (TFL::WhileOp) *While loop* output = input; while (cond(output)) { output = body(output) } While loop where all values are passes through arguments with implicit capture. input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. cond: A region that takes 'input' and returns a boolean scalar tensor. body: A region that takes a list of tensors and returns another list of tensors. Both lists have the same types. Traits: `SingleBlockImplicitTerminator`, `SingleBlock` Interfaces: `LoopLikeOpInterface`, `TflRuntimeVerifyOpInterface` #### Attributes: | Attribute | MLIR Type | Description | |---|---|---| | `is_stateless` | ::mlir::BoolAttr | bool attribute | #### Operands: | Operand | Description | |---|---| | `input` | variadic of tensor of any non-token type values | #### Results: | Result | Description | |---|---| | `output` | variadic of tensor of any non-token type values | ### `tfl.yield` (TFL::YieldOp) *Yield operation* The "yield" operation represents a return operation within the conditional and body of structured control flow (e.g., while), and a terminator for ControlNodeOp. The operation takes a variable number of operands and produces no results. The operand number and types must match the signature of the region that contains the operation. Traits: `AlwaysSpeculatableImplTrait`, `QuantizableResult`, `Terminator` Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | \<\\> | variadic of any non-token type | ### `tfl.zeros_like` (TFL::ZerosLikeOp) *ZerosLike operator* Returns a tensor of zeros with the same shape and type as the input tensor. Traits: `AlwaysSpeculatableImplTrait`, `SameOperandsAndResultType` Interfaces: `ConditionallySpeculatable`, `InferTypeOpInterface`, `NoMemoryEffect (MemoryEffectOpInterface)`, `TflRuntimeVerifyOpInterface` Effects: `MemoryEffects::Effect{}` #### Operands: | Operand | Description | |---|---| | `input` | tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values | #### Results: | Result | Description | |---|---| | `output` | tensor of 64-bit signless integer or 32-bit signless integer or 32-bit float values | ## Attributes ### DimensionMetadataAttr *Dimension metadata.* Syntax: #tfl.dimension_metadata< ::mlir::TFL::DimensionTypeAttr, # format int32_t, # dense_size ::llvm::ArrayRef, # segments ::llvm::ArrayRef # indices > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | format | `::mlir::TFL::DimensionTypeAttr` | dimension_type | | dense_size | `int32_t` | | | segments | `::llvm::ArrayRef` | | | indices | `::llvm::ArrayRef` | | ### SparsityParameterAttr *Sparsity parameter.* Syntax: #tfl.sparsity_parameter< ::llvm::ArrayRef, # traversal_order ::llvm::ArrayRef, # block_map ::llvm::ArrayRef # dim_metadata > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | traversal_order | `::llvm::ArrayRef` | | | block_map | `::llvm::ArrayRef` | | | dim_metadata | `::llvm::ArrayRef` | | ### ConstBytesAttr *A string attribute representation of compiled bytes* Syntax Examples: #tfl #### Parameters: | Parameter | C++ type | Description | |---|---|---| | value | `::llvm::StringRef` | | ### DimensionTypeAttr _Dimension*type* Syntax: #tfl.dimension_type_attr< `DENSE` | `SPARSE_CSR` # value > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | value | `::mlir::TFL::DimensionType` | an enum of type DimensionType | ### ExternalBufferAttr *Flatbuffer external buffer metadata.* Syntax: #tfl.external_buffer< ::mlir::StringAttr, # group_name uint64_t, # offset uint64_t, # length ::mlir::StringAttr # packing > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | group_name | `::mlir::StringAttr` | | | offset | `uint64_t` | | | length | `uint64_t` | | | packing | `::mlir::StringAttr` | | ### LSTMKernelTypeAttr _Lstm_kernel*type* Syntax: #tfl.lstm_kernel_type_attr< `FULL` | `BASIC` # value > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | value | `::mlir::TFL::LSTMKernelType` | an enum of type LSTMKernelType | ### MirrorPaddingTypeAttr _Mirror_pad*enum* Syntax: #tfl.mirror_pad_attr< `REFLECT` | `SYMMETRIC` # value > #### Parameters: | Parameter | C++ type | Description | |---|---|---| | value | `::mlir::TFL::MirrorPaddingType` | an enum of type MirrorPaddingType | ## Enums ### DimensionType _Dimension*type* #### Cases: | Symbol | Value | String | |---|---|---| | DENSE | `0` | DENSE | | SPARSE_CSR | `1` | SPARSE_CSR | ### LSTMKernelType _Lstm_kernel*type* #### Cases: | Symbol | Value | String | |---|---|---| | FULL | `0` | FULL | | BASIC | `1` | BASIC | ### MirrorPaddingType _Mirror_pad*enum* #### Cases: | Symbol | Value | String | |---|---|---| | REFLECT | `0` | REFLECT | | SYMMETRIC | `1` | SYMMETRIC |