MLPY/Lib/site-packages/onnx/defs/controlflow/defs.cc

// Copyright (c) ONNX Project Contributors

/*

 * SPDX-License-Identifier: Apache-2.0

 */

#include <assert.h>

#include "onnx/defs/controlflow/utils.h"
#include "onnx/defs/schema.h"

namespace ONNX_NAMESPACE {
using SupportType = OpSchema::SupportType;

static std::vector<std::string> control_flow_types_ir10() {
  auto t = OpSchema::all_tensor_types_ir10();
  auto s = OpSchema::all_tensor_sequence_types_ir10();
  auto o = OpSchema::all_optional_types_ir10();
  t.insert(t.end(), s.begin(), s.end());
  t.insert(t.end(), o.begin(), o.end());
  return t;
}

ONNX_OPERATOR_SET_SCHEMA(
    If,
    21,
    OpSchema()
        .SetDoc("If conditional")
        .Input(0, "cond", "Condition for the if. The tensor must contain a single element.", "B")
        .Output(
            0,
            "outputs",
            "Values that are live-out to the enclosing scope. The return values in "
            "the `then_branch` and `else_branch` must be of the same data type. "
            "The `then_branch` and `else_branch` may produce tensors with the same "
            "element type and different shapes. "
            "If corresponding outputs from the then-branch and the else-branch have "
            "static shapes S1 and S2, then the shape of the corresponding output "
            "variable of the if-node (if present) must be compatible with both S1 "
            "and S2 as it represents the union of both possible shapes."
            "For example, if in a model file, the first "
            "output of `then_branch` is typed float tensor with shape [2] and the "
            "first output of `else_branch` is another float tensor with shape [3], "
            "If's first output should have (a) no shape set, or (b) "
            "a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) "
            "a shape of rank 1 with a unique `dim_param`. "
            "In contrast, the first output cannot have the shape [2] since [2] and "
            "[3] are not compatible.",
            "V",
            OpSchema::Variadic,
            false)
        .Attr(
            "then_branch",
            "Graph to run if condition is true. Has N outputs: values you wish to "
            "be live-out to the enclosing scope. The number of outputs must match"
            " the number of outputs in the else_branch.",
            AttributeProto::GRAPH)
        .Attr(
            "else_branch",
            "Graph to run if condition is false. Has N outputs: values you wish to"
            " be live-out to the enclosing scope. The number of outputs must match"
            " the number of outputs in the then_branch.",
            AttributeProto::GRAPH)
        .TypeConstraint(
            "V",
            control_flow_types_ir10(),
            "All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.")
        .TypeConstraint("B", {"tensor(bool)"}, "Only bool")
        .TypeAndShapeInferenceFunction(IfInferenceFunction));

static const char* Loop_ver16_doc = R"DOC(

Generic Looping construct. This loop has multiple termination conditions:



1) Trip count. Iteration count specified at runtime. Set by

   specifying the input M. Optional. Set to empty string to omit.

   Note that a static trip count (specified at graph construction time) can be

   specified by passing in a constant node for input M.

2) Loop termination condition. This is an input to the op that determines

   whether to run the first iteration and also a loop-carried dependency for

   the body graph. The body graph must yield a value for the condition variable,

   whether this input is provided or not.



This table summarizes the operating modes of this operator with equivalent

C-style code:



Operator inputs defined as (max_trip_count, condition_var).



* input ("", ""):

        for (int i=0; ; ++i) {

          cond = ... // Note this value is ignored, but is required in the body

        }



* input ("", cond) // Note this is analogous to a while loop

        bool cond = ...;

        for (int i=0; cond; ++i) {

          cond = ...;

        }



* input ("", 1) // Note this is analogous to a do-while loop

        bool cond = true

        for (int i=0; cond; ++i) {

          cond = ...;

        }



* input (trip_count, "") // Note this is analogous to a for loop

        int trip_count = ...

        for (int i=0; i < trip_count; ++i) {

          cond = ...; // ignored

        }



* input (trip_count, cond)

        int trip_count = ...;

        bool cond = ...;

        for (int i=0; i < trip_count && cond; ++i) {

          cond = ...;

        }





*Sample usage - cond as well as trip count*



    graph predict-net {

      %a = Constant[value = <Scalar Tensor [3]>]()

      %b = Constant[value = <Scalar Tensor [6]>]()

      %keepgoing = Constant[value = <Scalar Tensor [1]>]()

      %max_trip_count = Constant[value = <Scalar Tensor [10]>]()

      %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)

      return

    }



    graph body-net (

      %i[INT32, scalar]           // iteration number

      %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used

      %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b

    ) {

      %my_local = Add(%a, %b_in)

      %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b

      %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition

      %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated

      return %keepgoing_out, %b_out, %user_defined_val

    }



*Sample equivalent C code*



    {

      /* User-defined code (enclosing scope) */

      int a = 3, b = 6;

      bool keepgoing = true; // Analogous to input cond

      /* End user-defined code */



      /* Implicitly-defined code */

      const int max_trip_count = 10; // Analogous to input M

      int user_defined_vals[]; // Imagine this is resizable

      /* End implicitly-defined code */

      /* initialize loop-carried variables and scan-output variables */

      bool keepgoing_out = keepgoing

      int b_out = b



      for (int i=0; i < max_trip_count && keepgoing_out; ++i) {

        /* Implicitly-defined code: bind actual parameter values

           to formal parameter variables of loop-body */

        bool keepgoing_in = keepgoing_out;

        bool b_in = b_out;



        /* User-defined code (loop body) */

        int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine

        b_out = a - b_in;

        keepgoing_out = my_local > b_out;

        user_defined_val = b_in + b_in; // b_in and b_out are different variables

        /* End user-defined code */



        /* Implicitly defined-code */

        user_defined_vals[i] = user_defined_val // accumulate scan-output values

      }

      // int t = my_local; // Can't do this. my_local is not accessible here.



      // The values below are bound to the output variables of the loop and therefore accessible

      // b_out; user_defined_vals; keepgoing_out;

    }



There are several things of note in this code snippet:



1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can

   be referenced in the inputs of the loop.

2) Any values computed in the loop body that needs to be used in a subsequent

   iteration or after the loop are modelled using a pair of variables in the loop-body,

   consisting of an input variable (eg., b_in) and an output variable (eg., b_out).

   These are referred to as loop-carried dependences. The loop operation node

   supplies the input value of the input variable for the first iteration, and

   returns the output value of the output variable produced by the final

   iteration.

3) Scan_output variables are used to implicitly concatenate values computed across

   all the iterations. In the above example, the value of user_defined_val computed

   over all iterations are concatenated and returned as the value of user_defined_vals

   after the loop.

4) Values created in the body cannot be accessed in the enclosing scope,

   except using the mechanism described above.



Note that the semantics of this op support "diagonal" or "wavefront" execution.

(See Step 3 here for an example:

https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/).

Frontends should emit multi-layer RNNs as a series of While operators (with

time being the inner looping dimension), with each successive layer consuming

the scan_outputs from the previous layer, possibly going through several

point-wise operators (e.g. dropout, residual connections, linear layer).



The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.

)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Loop,
    21,
    OpSchema()
        .SetDoc(Loop_ver16_doc)
        .Input(
            0,
            "M",
            "A maximum trip-count for the loop specified at runtime. Optional."
            " Pass empty string to skip.",
            "I",
            OpSchema::Optional)
        .Input(
            1,
            "cond",
            "A boolean termination condition. Optional. Pass empty string to skip.",
            "B",
            OpSchema::Optional)
        .Input(
            2,
            "v_initial",
            "The initial values of any loop-carried dependencies (values that "
            "change across loop iterations)",
            "V",
            OpSchema::Variadic,
            false,
            0)
        .Output(
            0,
            "v_final_and_scan_outputs",
            "Final N loop carried dependency values then K scan_outputs. "
            "Scan outputs must be Tensors.",
            "V",
            OpSchema::Variadic,
            false)
        .Attr(
            "body",
            "The graph run each iteration. It has 2+N inputs: (iteration_num, "
            "condition, loop carried dependencies...). It has 1+N+K outputs: "
            "(condition, loop carried dependencies..., scan_outputs...). Each "
            "scan_output is created by concatenating the value of the specified "
            "output value at the end of each iteration of the loop. It is an error"
            " if the dimensions or data type of these scan_outputs change across loop"
            " iterations.",
            AttributeProto::GRAPH)
        .TypeConstraint(
            "V",
            control_flow_types_ir10(),
            "All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.")
        .TypeConstraint("I", {"tensor(int64)"}, "tensor of int64, which should be a scalar.")
        .TypeConstraint("B", {"tensor(bool)"}, "tensor of bool, which should be a scalar.")
        .TypeAndShapeInferenceFunction(LoopInferenceFunction));

static const char* scan_16_doc = R"DOC(

Scan can be used to iterate over one or more scan_input tensors,

constructing zero or more scan_output tensors. It combines ideas from general recurrences,

functional programming constructs such as scan, fold, map, and zip, and is intended to enable

generalizations of RNN-like constructs for sequence-to-sequence processing.

Other tensors (referred to as state_variables here) can be used to carry a state

when iterating from one element to another (similar to hidden-state in RNNs, also referred

to as loop-carried dependences in the context of loops).

Many common usages involve a single scan_input tensor (where functionality

similar to scan, fold and map can be obtained). When more than one scan_input is used,

a behavior similar to zip is obtained.



The attribute body must be a graph, specifying the computation to be performed in

every iteration. It takes as input the current values of the state_variables and

the current iterated element of the scan_inputs. It must return the (updated) values

of the state_variables and zero or more scan_output_element tensors. The values of the

scan_output_element tensors are concatenated over all the iterations to produce the

scan_output values of the scan construct (similar to the concatenated intermediate

hidden-state values of RNN-like constructs). All the output tensors (state_variables as

well as scan_output_element tensors) are required to have the same shape in each iteration

of the loop (a restriction imposed to enable efficient memory allocation).



Note that the iterated element passed to the body subgraph does not have a sequence

axis. It will have a rank one less than the rank of the corresponding scan_input.



The scan operation returns the final values of the state_variables as well as the

scan_outputs.



The optional attribute scan_input_directions specifies the direction (forward or backward)

for each scan input. If this attribute is omitted, all sequences are scanned in the forward

direction. A bidirectional scan may be performed by specifying the same tensor input twice

in the scan_inputs, once with a forward direction, and once with a backward direction.



The scan_output of the operation is produced by concatenating the scan_output_element

values produced by the body in each iteration.  The optional attribute scan_output_directions

specifies the direction in which scan_output is constructed (by appending or prepending the

scan_output_element to scan_output in each iteration) for each scan_output. If this attribute

is omitted, the scan_output_element is appended to the scan_output in each iteration.



The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input.

If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the

batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1.

Note that scanning a non-zero axis may be less efficient than scanning axis zero.



The optional attribute scan_output_axes specifies the axis along which the scan_outputs

are accumulated for each scan_output. For example, if axis 1 is the time axis (to be

scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis

value of 1.



Note that because of the ONNX restriction that only the last parameter of an operator can

be variadic, the initial-states and scan-inputs are listed together as one input parameter.

Similarly, the final-states and scan-outputs are listed together as one output parameter.

The attribute num_scan_inputs indicates the number M of scan-inputs.



The behavior of



    Scan <

        num_scan_inputs = m,

        body = loop-body,

        scan_input_axes = [axis_1, ..., axis_m]

    > (init_1, ..., init_n, scan_1, ..., scan_m)



is equivalent to the following pseudo-code:



    // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i

    // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j.

    sequence_length = scan_1.shape[axis_1];



    // initialize state-variables

    st_1 = init_1; ... st_n = init_n;

    // initialize scan-output variables: [] denotes an empty tensor

    scan_out_1 = []; ...; scan_out_k = [];

    // identify number of iterations:



    // execute loop

    for (int t = 0; t < sequence_length; ++t) {

        // generate the scan-input elements: the notation T<axis=k>[t] indicates the sub-tensor

        // of rank one less than T obtained by indexing T at position t along axis k.

        si_1 = scan_1<axis=axis_1>[t];

        ... ;

        si_m = scan_m<axis=axis_m>[t];

        // execute loop-body

        st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m)

        // accumulate the scan-output elements

        scan_out_1 = Concat<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(scan_out_k, so_k);

    }



    return st_1, ..., st_n, scan_out_1, ..., scan_out_k;



*Sample usage: Encoding RNN using a Scan*



The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi,

recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can

be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes

%Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these

values are computed in the outer graph, they need to be passed in as extra state_variables.



    graph rnn-encoding {

      %H_0 = ...

      %X = ...

      %Y_h, %Y = Scan[body = <graph rnn-cell-1>, num_scan_inputs=1](%H_0, %X)

      return %Y, %Y_h

    }



    graph rnn-cell-1 (

      %H_tminus1[FLOAT, tensor]

      %X_t[FLOAT, tensor]

    ) {

      %Wi = ...

      %Ri = ...

      %Wbi = ...

      %Rbi = ...

      %t1 = X_t * (Wi^T)

      %t2 = H_tminus1*(Ri^T)

      %t3 = Add(%t1, %t2)

      %t4 = Add(%t3, %Wbi)

      %t5 = Add(%t4, %Rbi)

      %Ht = Tanh(%t5)

      %Accumulate = Identity(%Ht)

      return %Ht, %Accumulate

    }



)DOC";

ONNX_OPERATOR_SET_SCHEMA(
    Scan,
    21,
    OpSchema()
        .SetDoc(scan_16_doc)
        .Input(
            0,
            "initial_state_and_scan_inputs",
            "Initial values of the loop's N state variables followed by M scan_inputs",
            "V",
            OpSchema::Variadic,
            false)
        .Output(
            0,
            "final_state_and_scan_outputs",
            "Final values of the loop's N state variables followed by K scan_outputs",
            "V",
            OpSchema::Variadic,
            false)
        .Attr(
            "body",
            "The graph run each iteration. It has N+M inputs: "
            "(loop state variables..., scan_input_elts...). It has N+K outputs: "
            "(loop state variables..., scan_output_elts...). Each "
            "scan_output is created by concatenating the value of the specified "
            "scan_output_elt value at the end of each iteration of the loop. It is an error"
            " if the dimensions of these values change across loop iterations.",
            AttributeProto::GRAPH,
            true)
        .Attr("num_scan_inputs", "An attribute specifying the number of scan_inputs M. ", AttributeProto::INT, true)
        .Attr(
            "scan_input_directions",
            "An optional list of M flags. The i-th element of the list specifies the direction "
            "to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 "
            "indicates reverse direction. "
            "If omitted, all scan_input tensors will be scanned in the forward direction.",
            AttributeProto::INTS,
            false)
        .Attr(
            "scan_output_directions",
            "An optional list of K flags, one for each scan_output. The i-th element of the list "
            "specifies whether the i-th scan_output should be constructed by appending or "
            "prepending a new value in each iteration: 0 indicates appending and 1 "
            "indicates prepending. "
            "If omitted, all scan_output tensors will be produced by appending a value "
            "in each iteration.",
            AttributeProto::INTS,
            false)
        .Attr(
            "scan_input_axes",
            "An optional list of M flags. The i-th element of the list specifies the axis "
            "to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will "
            "be used as the scan axis for every scan_input. Negative value for an axis means "
            "counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).",
            AttributeProto::INTS,
            false)
        .Attr(
            "scan_output_axes",
            "An optional list of K flags. The i-th element of the list specifies the axis "
            "for the i-th scan_output. The scan outputs are accumulated along the specified "
            "axis. If omitted, 0 will be used as the scan axis for every scan_output. "
            "Negative value for an axis means counting dimensions from the back. Accepted "
            "range is [-r, r-1].",
            AttributeProto::INTS,
            false)
        .TypeConstraint("V", OpSchema::all_tensor_types_ir10(), "All Tensor types up to IRv10.")
        .TypeAndShapeInferenceFunction(ScanInferenceFunction)); // Shares same shape inference as opset 11

} // namespace ONNX_NAMESPACE