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 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 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 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)
_Addn 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 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
, SameOperandsAndResultElementType
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, 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_pool2d 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 type values |
state_output |
2D tensor of any type values |
concat_temp |
2D tensor of any type values |
activ_temp |
2D tensor of any 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_lhs`: optional: Transpose LHS (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 QI8 type or QI16 type or 8-bit signless integer values |
y |
tensor of 32-bit float or QI8 type or QI16 type or 8-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 32-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 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 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 type values or none type |
fw_cell_to_forget_weights |
tensor of any type values or none type |
fw_cell_to_output_weights |
tensor of any type values or none type |
fw_input_gate_bias |
tensor of any 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 type values or none type |
fw_projection_bias |
tensor of any type values or none type |
bw_input_to_input_weights |
tensor of any 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 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 type values or none type |
bw_cell_to_forget_weights |
tensor of any type values or none type |
bw_cell_to_output_weights |
tensor of any type values or none type |
bw_input_gate_bias |
tensor of any 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 type values or none type |
bw_projection_bias |
tensor of any 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 type values or none type |
fw_aux_input_to_input_weights |
tensor of any type values or none type |
fw_aux_input_to_forget_weights |
tensor of any type values or none type |
fw_aux_input_to_cell_weights |
tensor of any type values or none type |
fw_aux_input_to_output_weights |
tensor of any type values or none type |
bw_aux_input_to_input_weights |
tensor of any type values or none type |
bw_aux_input_to_forget_weights |
tensor of any type values or none type |
bw_aux_input_to_cell_weights |
tensor of any type values or none type |
bw_aux_input_to_output_weights |
tensor of any type values or none type |
Results:
Result | Description |
---|---|
fw_output |
tensor of any type values |
bw_output |
tensor of any 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 type values |
Results:
Result | Description |
---|---|
output |
tensor of any 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.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)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
input |
tensor of 32-bit float 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 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 32-bit float or 64-bit float or 1-bit signless integer or 4-bit signless 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 32-bit float or 64-bit float or 1-bit signless 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
, TF::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 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 64-bit signless integer or 32-bit signless integer or 16-bit signless integer 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<mlir::func::FuncOp>
, RecursiveMemoryEffects
, SingleBlockImplicitTerminator<YieldOp>
, SingleBlock
Operands:
Operand | Description |
---|---|
controlInputs |
variadic of control |
Results:
Result | Description |
---|---|
outputs |
variadic of tensor of any 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
, quant::AccumulatorUniformScale<2, 0, 1>
, quant::AffineOpCoefficient<0, 1>
Interfaces: AffineQuantizedOpInterface
, ConditionallySpeculatable
, DynamicRangeQuantizedOpInterface
, InferTypeOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, 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 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
, quant::AccumulatorUniformScale<2, 0, 1>
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, 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 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
, quant::AccumulatorUniformScale<2, 0, 1>
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, 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 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
, TF::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.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 type values or none type |
Results:
Result | Description |
---|---|
output |
variadic of tensor of any 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<YieldOp>
, SingleBlock
Interfaces: InferTypeOpInterface
, TflRuntimeVerifyOpInterface
Operands:
Operand | Description |
---|---|
input |
variadic of tensor of any type values or none type |
Results:
Result | Description |
---|---|
output |
variadic of tensor of any 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
, quant::AccumulatorUniformScale<2, 0, 1>
, quant::AffineOpCoefficient<3, 1>
Interfaces: AffineQuantizedOpInterface
, ConditionallySpeculatable
, DynamicRangeQuantizedOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, 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 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.
Interfaces: NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
input |
tensor of QI4 type or QI8 type or QUI8 type or QI16 type or 16-bit float values |
Results:
Result | Description |
---|---|
output |
tensor of 32-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 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
, 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 32-bit signless integer or QUI8 type values |
rhs |
tensor of 32-bit float or 32-bit signless integer or QUI8 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or QUI8 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 https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
operand |
tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
update |
tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float values |
start_indices |
tensor of 32/64-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 1-bit signless integer or 8-bit signless integer or 32-bit signless integer or 64-bit signless integer or 32-bit float 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.
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 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 8-bit unsigned integer or TFLite string type values |
y |
tensor of 1-bit signless integer or 32-bit float or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or 8-bit unsigned integer or TFLite string type values |
Results:
Result | 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 QI8 type or QI16 type values |
Results:
Result | Description |
---|---|
y |
tensor of 32-bit float or QI8 type or QI16 type values |
tfl.expand_dims
(TFL::ExpandDimsOp)
Inserts a dimension of 1 into a tensor's shape.
Given a tensor input
, this operation inserts a dimension of 1 at the
dimension index axis
of input
's shape. The dimension index axis
starts at
zero; if you specify a negative number for axis
it is counted backward from
the end.
This operation is useful if you want to add a batch dimension to a single
element. For example, if you have a single image of shape [height, width,
channels]
, you can make it a batch of 1 image with expand_dims(image, 0)
,
which will make the shape [1, height, width, channels]
.
Other examples:
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
This operation requires that:
-1-input.dims() <= dim <= input.dims()
This operation is related to squeeze()
, which removes dimensions of
size 1.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
input |
tensor of any type values |
dim |
tensor of 32/64-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of any type values |
tfl.external_const
(TFL::ExternalConstOp)
External const op.
External const op holds a buffer_index
which points to a constant
in the flatbuffer.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | Description |
---|---|---|
buffer_index | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Results:
Result | Description |
---|---|
output |
tensor of any type values |
tfl.fake_quant
(TFL::FakeQuantOp)
FakeQuant operator
Fake-quantize the 'inputs' tensor of type float via float scalars min and max to 'outputs' tensor of same shape as inputs.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | 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 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 32-bit signless integer or 64-bit signless integer or 1-bit signless integer or QI8 type or QI16 type or TFLite string type values |
tfl.floor
(TFL::FloorOp)
Floor operator
Returns element-wise floor value of the input.
Traits: AlwaysSpeculatableImplTrait
, InferTensorType
, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable
, InferShapedTypeOpInterface
, InferTypeOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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
, quant::AccumulatorUniformScale<2, 0, 1>
, quant::AffineOpCoefficient<0, 1>
Interfaces: AffineQuantizedOpInterface
, ConditionallySpeculatable
, DynamicRangeQuantizedOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, 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 |
Operands:
Operand | Description |
---|---|
input |
tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or QUI16 type values |
filter |
tensor of 32-bit float or QI4 type or QI8 type or QUI8 type or QI16 type values |
bias |
tensor of any type values or none type |
Results:
Result | Description |
---|---|
output |
variadic of tensor of any type values |
tfl.gather
(TFL::GatherOp)
Gather operator
Gather slices from params
axis axis
according to indices
.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, DynamicRangeQuantizedOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 32-bit signless integer attribute |
batch_dims | ::mlir::IntegerAttr | 32-bit signless integer attribute |
Operands:
Operand | Description |
---|---|
params |
tensor of 32-bit float or 1-bit signless integer or 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 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)
_Gathernd 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 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 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 QI8 type or QUI8 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or QI8 type or QUI8 type values |
tfl.greater
(TFL::GreaterOp)
Greater operator
Element-wise greater operation.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait
, AlwaysSpeculatableImplTrait
, QuantizableResult
, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
lhs |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
rhs |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type values |
Results:
Result | Description |
---|---|
output |
tensor of 1-bit signless integer values |
tfl.greater_equal
(TFL::GreaterEqualOp)
_Greaterequal 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<f32>, tensor<f32>) {
%x_true = ...
%y_true = ...
tfl.yield %x_true, %y_true : tensor<f32>, tensor<f32>
} else {
%x_false = ...
%y_false = ...
tfl.yield %x_false, %y_false : tensor<f32>, tensor<f32>
}
tfl.if
regions are always terminated with "tfl.yield". If "tfl.if"
defines no values, the "tfl.yield" can be left out, and will be inserted
implicitly. Otherwise, it must be explicit.
Also, if "tfl.if" defines one or more values, the 'else' block cannot be
omitted.
Example:
tfl.if %b {
...
}
Traits: NoRegionArguments
, RecursiveMemoryEffects
, SingleBlockImplicitTerminator<YieldOp>
, SingleBlock
Interfaces: RegionBranchOpInterface
, TflRuntimeVerifyOpInterface
Operands:
Operand | Description |
---|---|
cond |
tensor of 1-bit signless integer values |
Results:
Result | Description |
---|---|
results |
variadic of tensor of any type values |
tfl.imag
(TFL::ImagOp)
Returns the imaginary part of a complex number.
Given a tensor input
of complex numbers, this operation returns a tensor of
type float
that is the imaginary part of each element in input
. All
elements in input
must be complex numbers of the form \(a + bj\), where a
is the real part and b is the imaginary part returned by this operation.
Traits: AlwaysSpeculatableImplTrait
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
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)
_Lessequal 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).
Traits: AlwaysSpeculatableImplTrait
, InferTensorType
, TF::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
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, 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 QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | Description |
---|---|
y |
tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
tfl.lstm
(TFL::LSTMOp)
The full lstm operator
Long short-term memory unit (LSTM) recurrent network layer. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. 'Long Short-Term Memory'. Neural Computation, 9(8):1735-1780, 1997. The peephole implementation is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. 'Long short-term memory recurrent neural network architectures for large scale acoustic modeling.' INTERSPEECH, 2014. The coupling of input and forget gate (CIFG) is based on: http://arxiv.org/pdf/1503.04069.pdf Greff et al. 'LSTM: A Search Space Odyssey' The layer normalization is based on: https://arxiv.org/pdf/1607.06450.pdf Ba et al. 'Layer Normalization'
Traits: QuantizableResult
Interfaces: DynamicRangeQuantizedOpInterface
, TFL_StatefulOp
, TflRuntimeVerifyOpInterface
Attributes:
Attribute | MLIR Type | 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 type values or none type |
input_to_forget_weights |
tensor of 32-bit float or QI8 type values |
input_to_cell_weights |
tensor of 32-bit float or QI8 type values |
input_to_output_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_input_weights |
tensor of any type values or none type |
recurrent_to_forget_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_cell_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_output_weights |
tensor of 32-bit float or QI8 type values |
cell_to_input_weights |
tensor of any type values or none type |
cell_to_forget_weights |
tensor of any type values or none type |
cell_to_output_weights |
tensor of any type values or none type |
input_gate_bias |
tensor of any type values or none type |
forget_gate_bias |
tensor of 32-bit float or QI32 type values |
cell_bias |
tensor of 32-bit float or QI32 type values |
output_gate_bias |
tensor of 32-bit float or QI32 type values |
projection_weights |
tensor of any type values or none type |
projection_bias |
tensor of any type values or none type |
input_activation_state |
stateful tensor |
input_cell_state |
stateful tensor |
input_layer_norm_coefficients |
tensor of any type values or none type |
forget_layer_norm_coefficients |
tensor of any type values or none type |
cell_layer_norm_coefficients |
tensor of any type values or none type |
output_layer_norm_coefficients |
tensor of any type values or none type |
Results:
Result | Description |
---|---|
output |
tensor of any type values |
tfl.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 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
rhs |
tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
Results:
Result | Description |
---|---|
max |
tensor of 32-bit float or 32/64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
tfl.mean
(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 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 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 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
rhs |
tensor of 32-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or 32-bit unsigned integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type or 16-bit signless integer or complex type with 32-bit float elements values |
tfl.multinomial
(TFL::MultinomialOp)
Draws samples from a categorical distribution.
The generated values will have a categorical distribution based on the logits
or unnormalized log-probabilities provided for all classes.
Interfaces: TflRuntimeVerifyOpInterface
Attributes:
Attribute | MLIR Type | 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
, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable
, InferShapedTypeOpInterface
, InferTypeOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
x |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values |
Results:
Result | Description |
---|---|
y |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer values |
tfl.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, https://arxiv.org/abs/1704.04503) where boxes reduce the score
of other overlapping boxes instead of directly causing them to be pruned.
To enable this Soft-NMS mode, set the soft_nms_sigma
parameter to be
larger than 0.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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)
_Notequal 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 TFLite quint8 type or TFLite string type values |
rhs |
tensor of 1-bit signless integer or 32-bit float or 32-bit signless integer or 64-bit signless integer or QUI8 type or QI8 type or TFLite quint8 type or TFLite string type values |
Results:
Result | Description |
---|---|
output |
tensor of 1-bit signless integer values |
tfl.NumericVerify
(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 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 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 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
padding |
tensor of 32/64-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
tfl.padv2
(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 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
padding |
tensor of 32/64-bit signless integer values |
constant_values |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type values |
tfl.poly_call
(TFL::PolyCallOp)
Poly call
Have multiple function bodies for the same computation. This allows a program compiler/interpreter to choose one of the available options to execute the program based on which one is most suitable for the target backend.
input: A list of input tensors whose types are T. output: A list of output tensors whose types are T.
call: Multiple regions, each of which encapsulates the same semantic computation but in different forms.
Traits: SingleBlockImplicitTerminator<YieldOp>
, SingleBlock
Interfaces: RegionBranchOpInterface
Operands:
Operand | Description |
---|---|
input |
variadic of tensor of any type values |
Results:
Result | Description |
---|---|
output |
variadic of tensor of any type values |
tfl.pow
(TFL::PowOp)
Power operator
Element-wise power operation.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait
, AlwaysSpeculatableImplTrait
, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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
, quant::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 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 QI16 type or QUI16 type or TFLite quint8 type values |
tfl.pseudo_sparse_const
(TFL::SparseConstOp)
Sparse constant pseudo op.
Represents a sparse constant value in TensorFlow Lite dialect. This is not an actual operation and it will be lowered to buffer instead.
Traits: AlwaysSpeculatableImplTrait
, FirstAttrDerivedResultType
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | 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 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 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 QI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | Description |
---|---|
output |
tensor of QI4 type or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
tfl.random_standard_normal
(TFL::RandomStandardNormalOp)
Outputs random values from a normal distribution.
The generated values will have mean 0 and standard deviation 1.
Interfaces: TflRuntimeVerifyOpInterface
Attributes:
Attribute | MLIR Type | 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 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 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
axes |
tensor of 32-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
tfl.reduce_min
(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 type values |
shape |
tensor of 32-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of any type values |
tfl.resize_bilinear
(TFL::ResizeBilinearOp)
ResizeBilinear Op
Resize images
to size
using bilinear interpolation.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | 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 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values |
axis |
tensor of 32-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 8-bit unsigned integer or 16-bit signless integer or 32-bit signless integer or 64-bit signless integer or QI16 type or QUI8 type or QI8 type or TFLite quint8 type or 1-bit signless integer values |
tfl.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
, TF::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)
_Scatternd 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 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:
- Either the same shape (in which case the select is elementwise), or
- condition must be Rank 1 and match over the first dimension.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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:
- Either the same shape (in which case the select is elementwise), or
- Broadcastable shapes between 'condition', 'x' and 'y'.
Traits: ::mlir::OpTrait::TFLRuntimeOpTrait
, AlwaysSpeculatableImplTrait
, QuantizableResult
, ResultsBroadcastableShape
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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.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 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
, SameOperandsAndResultElementType
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, 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
, TF::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.slice
(TFL::SliceOp)
Return a slice from 'input'.
The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'.
begin
is zero-based; size
is one-based. If size[i] is -1, all remaining
elements in dimension i are included in the slice. In other words, this is
equivalent to setting:
size[i] = input.dim_size(i) - begin[i]
Requirements: 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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 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 32-bit signless integer or 64-bit signless integer or 8-bit signless integer or 8-bit unsigned integer or 32-bit unsigned integer or 1-bit signless integer or TFLite string type or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
tfl.softmax
(TFL::SoftmaxOp)
Softmax operator
Computes element-wise softmax activations with the following formula
exp(input) / tf.reduce_sum(exp(input * beta), dim)
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, FixedOutputRangeInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TflArithmeticCountOpInterface
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | Description |
---|---|---|
beta | ::mlir::FloatAttr | 32-bit float attribute |
Operands:
Operand | Description |
---|---|
input |
tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or QI8 type or QUI8 type or TFLite quint8 type or QI16 type values |
tfl.space_to_batch_nd
(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 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 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 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 type values |
tfl.sqrt
(TFL::SqrtOp)
Square root operator
Computes element-wise Square root of input
Traits: AlwaysSpeculatableImplTrait
, InferTensorType
, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable
, InferShapedTypeOpInterface
, InferTypeOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | Description |
---|---|
x |
tensor of 32-bit float values |
Results:
Result | Description |
---|---|
y |
tensor of 32-bit float values |
tfl.square
(TFL::SquareOp)
Square operator
Computes element-wise Square of input
Traits: AlwaysSpeculatableImplTrait
, InferTensorType
, TF::SameOperandsAndResultTypeResolveRef
Interfaces: ConditionallySpeculatable
, InferShapedTypeOpInterface
, InferTypeOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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 type values |
Results:
Result | Description |
---|---|
output |
tensor of any type values |
tfl.strided_slice
(TFL::StridedSliceOp)
StridedSlice Op
Return a strided slice from input
.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, SameOperandsAndResultsScale
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | 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 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 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 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
rhs |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or 32-bit signless integer or 64-bit signless integer or QI8 type or QUI8 type or QI16 type values |
tfl.sum
(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 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.svdf
(TFL::SVDFOp)
Single value decomposition filter operator
The SVDF op is a decomposition of a densely connected op into low rank filters. For details: https://research.google.com/pubs/pub43813.html https://arxiv.org/abs/1812.02802
Traits: QuantizableResult
, quant::AccumulatorUniformScale<3, 2, 4>
Interfaces: DynamicRangeQuantizedOpInterface
, TFL_StatefulOp
, TflRuntimeVerifyOpInterface
Attributes:
Attribute | MLIR Type | 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 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 QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or QI8 type or QUI8 type or QI16 type or TFLite quint8 type values |
tfl.tile
(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 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values |
perm |
tensor of 32-bit signless integer values |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit signless integer or 32-bit float or 8-bit signless integer or 8-bit unsigned integer or QI8 type or QUI8 type or TFLite quint8 type or 1-bit signless integer or 64-bit signless integer or QI16 type values |
tfl.transpose_conv
(TFL::TransposeConvOp)
Transpose convolution operator
Performs transpose convolution operation on input.
Traits: AlwaysSpeculatableImplTrait
, QuantizableResult
, quant::AccumulatorUniformScale<3, 1, 2>
, quant::AffineOpCoefficient<0, 1>
Interfaces: AffineQuantizedOpInterface
, ConditionallySpeculatable
, DynamicRangeQuantizedOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, TFL_SparseOp
, TflArithmeticCountOpInterface
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Attributes:
Attribute | MLIR Type | 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 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 type values or none type |
input_to_forget_weights |
tensor of 32-bit float or QI8 type values |
input_to_cell_weights |
tensor of 32-bit float or QI8 type values |
input_to_output_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_input_weights |
tensor of any type values or none type |
recurrent_to_forget_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_cell_weights |
tensor of 32-bit float or QI8 type values |
recurrent_to_output_weights |
tensor of 32-bit float or QI8 type values |
cell_to_input_weights |
tensor of any type values or none type |
cell_to_forget_weights |
tensor of any type values or none type |
cell_to_output_weights |
tensor of any type values or none type |
input_gate_bias |
tensor of any type values or none type |
forget_gate_bias |
tensor of 32-bit float values |
cell_bias |
tensor of 32-bit float values |
output_gate_bias |
tensor of 32-bit float values |
projection_weights |
tensor of any type values or none type |
projection_bias |
tensor of any type values or none type |
input_activation_state |
stateful tensor |
input_cell_state |
stateful tensor |
input_layer_norm_coefficients |
tensor of any type values or none type |
forget_layer_norm_coefficients |
tensor of any type values or none type |
cell_layer_norm_coefficients |
tensor of any type values or none type |
output_layer_norm_coefficients |
tensor of any type values or none type |
Results:
Result | Description |
---|---|
output |
tensor of 32-bit float or QI8 type values |
tfl.unidirectional_sequence_rnn
(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 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 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<YieldOp>
, SingleBlock
Interfaces: LoopLikeOpInterface
, TflRuntimeVerifyOpInterface
Attributes:
Attribute | MLIR Type | Description |
---|---|---|
is_stateless | ::mlir::BoolAttr | bool attribute |
Operands:
Operand | Description |
---|---|
input |
variadic of tensor of any type values |
Results:
Result | Description |
---|---|
output |
variadic of tensor of any type values |
tfl.yield
(TFL::YieldOp)
Yield operation
The "yield" operation represents a return operation within the conditional and body of structured control flow (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 |
---|---|
«unnamed» | variadic of any type |
tfl.zeros_like
(TFL::ZerosLikeOp)
ZerosLike operator
Returns a tensor of zeros with the same shape and type as the input tensor.
Traits: AlwaysSpeculatableImplTrait
, SameOperandsAndResultShape
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, TflRuntimeVerifyOpInterface
Effects: MemoryEffects::Effect{}
Operands:
Operand | 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<int32_t>, # segments
::llvm::ArrayRef<int32_t> # indices
>
Parameters:
Parameter | C++ type | Description |
---|---|---|
format | ::mlir::TFL::DimensionTypeAttr |
dimension_type |
dense_size | int32_t |
|
segments | ::llvm::ArrayRef<int32_t> |
|
indices | ::llvm::ArrayRef<int32_t> |
SparsityParameterAttr
Sparsity parameter.
Syntax:
#tfl.sparsity_parameter<
::llvm::ArrayRef<int32_t>, # traversal_order
::llvm::ArrayRef<int32_t>, # block_map
::llvm::ArrayRef<DimensionMetadataAttr> # dim_metadata
>
Parameters:
Parameter | C++ type | Description |
---|---|---|
traversal_order | ::llvm::ArrayRef<int32_t> |
|
block_map | ::llvm::ArrayRef<int32_t> |
|
dim_metadata | ::llvm::ArrayRef<DimensionMetadataAttr> |
ConstBytesAttr
A string attribute representation of compiled bytes
Syntax Examples:
#tfl<const_bytes : "0xDEADBEEF">
Parameters:
Parameter | C++ type | Description |
---|---|---|
value | ::llvm::StringRef |
DimensionTypeAttr
dimension_type
Syntax:
#tfl.dimension_type_attr<
::mlir::TFL::DimensionType # value
>
Enum cases:
- DENSE (
DENSE
) - SPARSE_CSR (
SPARSE_CSR
) #### Parameters:
Parameter | C++ type | Description |
---|---|---|
value | ::mlir::TFL::DimensionType |
an enum of type DimensionType |
LSTMKernelTypeAttr
lstm_kernel_type
Syntax:
#tfl.lstm_kernel_type_attr<
::mlir::TFL::LSTMKernelType # value
>
Enum cases:
- FULL (
FULL
) - BASIC (
BASIC
) #### Parameters:
Parameter | C++ type | Description |
---|---|---|
value | ::mlir::TFL::LSTMKernelType |
an enum of type LSTMKernelType |
MirrorPaddingTypeAttr
mirror_pad_enum
Syntax:
#tfl.mirror_pad_attr<
::mlir::TFL::MirrorPaddingType # value
>
Enum cases:
- REFLECT (
REFLECT
) - SYMMETRIC (
SYMMETRIC
) #### 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 |