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Loop-5 operation specification (openvinotoolkit#2291)
The Loop-5 operation specification
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## Loop <a name="Loop"></a> {#openvino_docs_ops_infrastructure_Loop_5} | ||
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**Versioned name**: *Loop-5* | ||
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**Category**: Infrastructure | ||
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**Short description**: *Loop* operation performs recurrent execution of the network, which is described in the `body`, iterating through the data. | ||
The operation has similar semantic to the ONNX* Loop [operation](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Loop-13). | ||
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**Detailed description** | ||
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The body of the Loop can be executed 0 or more times depending on the values passed to the Loop operation inputs called "trip count", "execution condition" and input of the Loop body called "current iteration". | ||
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These Loop operation inputs have the following meaning: | ||
1. Trip count is an integer scalar or 1D tensor with 1 element input specifying maximum number of iterations. To simulate infinite loop Constant `-1` can be provided as input. | ||
2. Loop execution condition input is a boolean scalar or 1D tensor with 1 element input specifying whether to run the first loop iteration or not. Note, that the body of the Loop must yield the condition value for the consecutive iterations. | ||
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There are several combinations of these two inputs `(trip_count, execution condition)` which are described in the following code snippet: | ||
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``` | ||
input (-1, true) // infinite loop | ||
bool cond = true; | ||
for (int i = 0; cond; ++i) | ||
{ | ||
cond = true; // sub-graph calculating condition must always return "true"! | ||
} | ||
input (-1, cond) // while loop | ||
bool cond = ...; | ||
for (int i = 0; cond; ++i) | ||
{ | ||
cond = ...; | ||
} | ||
input (-1, true) // do-while loop | ||
bool cond = true; | ||
for (int i = 0; cond; ++i) | ||
{ | ||
cond = ...; | ||
} | ||
input (trip_count, true) // for loop | ||
int trip_count = ...; | ||
bool cond = true; | ||
for (int i = 0; i < trip_count; ++i) | ||
{ | ||
cond = true; // sub-graph calculating condition must always return "true"! | ||
} | ||
input (trip_count, cond) // for with condition | ||
int trip_count = ...; | ||
bool cond = ...; | ||
for (int i = 0; i < trip_count && cond; ++i) | ||
{ | ||
cond = ...; | ||
} | ||
``` | ||
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1. One of the body graph inputs called "current iteration" is an integer scalar or 1D integer tensor with 1 number specifying current iteration number. The iteration number starts from 0 and incremented by one for each iteration. This input is optional and may not exist if the iteration number value is not used in the body. | ||
2. One of the body graph outputs is called "condition" is a boolean scalar or 1D tensor with 1 element. This value is used to decide whenever to perform the next iteration or not. | ||
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Loop operation description in the IR has regular sections: `input` and `output`. They connect Loop body to the outer graph and specify condition(s). | ||
Loop operation description in the IR also has several special sections: `body`, `port_map` and `back_edges` similar to the ones from the TensorIterator operation but having some important features described below. | ||
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1. The body operation getting an input from the main graph should have an entry in the `port_map` section of the Loop operation. These edges connect input ports of the Loop with the body `Parameter`s. | ||
2. The body operation producing tensor to be used in the subsequent iterations (like in RNN models) should have a back edge described in the `back_edges` section of the operation. The back edge connects the respective body `Parameter` and `Result` operations. For such a case the Loop operation node provides input for the first iteration, while corresponding Loop operation output produces the tensor computed during the last iteration. | ||
3. Output tensors produced by a particular body operation across all iterations can be concatenated and returned as a Loop operation output (this is a "scan output" according to the ONNX* Loop operation [specification](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Loop-13)). The corresponding `output` entry in the `port_map` should have `axis` attribute specifying the axis to concatenate. Therefore, outputs from operations corresponding to `output` entries in the `port_map` without `axis` attribute are returned "as is" (without concatenation). | ||
4. There is one body `Parameter` operation not connected through the `port_map`. This is a "current iteration" input. The Loop operation is responsible for providing the appropriate value for each iteration. | ||
5. Connection of nodes inside the Loop body with the main graph should be done through `Parameter` and `Result` body operations. No other ways to connect graphs are allowed. | ||
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**Loop attributes**: | ||
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* **Body**: | ||
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`body` is a network that will be recurrently executed. The network is described operation by operation as a typical IR network. | ||
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* **Body attributes**: | ||
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No attributes available. | ||
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* **Port map**: | ||
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*port_map* is a set of rules to map input or output data tensors of `Loop` operation onto `body` data tensors. The `port_map` entries can be` input` and `output`. Each entry describes a corresponding mapping rule. | ||
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* **Port map attributes**: | ||
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* *external_port_id* | ||
* **Description**: *external_port_id* is a port ID of the `Loop` operation. | ||
* **Range of values**: IDs of the *Loop* outputs | ||
* **Type**: `int` | ||
* **Default value**: None | ||
* **Required**: *yes* | ||
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* *internal_layer_id* | ||
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* **Description**: *internal_layer_id* is a `Parameter` or `Result` operation ID inside the `body` network to map to. | ||
* **Range of values**: IDs of the `Parameter` operations inside in the *Loop* operation | ||
* **Type**: `int` | ||
* **Default value**: None | ||
* **Required**: *yes* | ||
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* *axis* | ||
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* **Description**: *axis* is an axis to concatenate the body `Result` output across all iterations. Can be specified for `output` entry only. | ||
* **Range of values**: an integer. Negative value means counting dimension from the end. | ||
* **Type**: `int` | ||
* **Default value**: None | ||
* **Required**: *no* | ||
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* **Back edges**: | ||
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*back_edges* is a set of rules to transfer tensor values from `body` outputs at one iteration to `body` parameters at the next iteration. Back edge connects some `Result` operation in the `body` to `Parameter` operation in the same `body`. | ||
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* **Back edge attributes**: | ||
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* *from-layer* | ||
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* **Description**: *from-layer* is a `Result` operation ID inside the `body` network. | ||
* **Range of values**: IDs of the `Result` operations inside the *Loop* | ||
* **Type**: `int` | ||
* **Default value**: None | ||
* **Required**: *yes* | ||
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* *to-layer* | ||
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* **Description**: *to-layer* is a `Parameter` operation ID inside the `body` network to end mapping. | ||
* **Range of values**: IDs of the `Parameter` operations inside the *Loop* | ||
* **Type**: `int` | ||
* **Default value**: None | ||
* **Required**: *yes* | ||
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**Loop Inputs** | ||
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* **Trip count**: A scalar or 1D tensor with 1 element of `int64` or `int32` type specifying maximum number of iterations. *Required*. | ||
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* **ExecutionCondition**: A scalar or 1D tensor with 1 element of `boolean` type specifying whether to execute the first iteration or not. `True` value means to execute the 1st iteration. *Required*. | ||
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* **Multiple other inputs**: tensors of different types and shapes. *Optional*. | ||
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**Loop Outputs** | ||
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* **Multiple outputs**: Results of execution of the `body`. Tensors of any type and shape. | ||
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**Body Inputs** | ||
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* **Multiple inputs**: tensors of different types and shapes except the one corresponding to the current iteration number. This input is marked in the port_map with attribute `purpose = "current_iteration"` and produces a scalar or 1D tensor with 1 element of `int64` or `int32` type. *Optional*. | ||
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**Body Outputs** | ||
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* **Multiple outputs**: Results of execution of the `body`. Tensors of any type and shape except the one corresponding to the output with execution condition. This output is marked in the port_map with attribute `purpose = "execution_condition"` and is mandatory and produces a scalar or 1D tensor with 1 element of `boolean` type. Other outputs are optional. | ||
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**Examples** | ||
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*Example 1: a typical Loop structure* | ||
```xml | ||
<layer type="Loop" ... > | ||
<input> ... </input> | ||
<output> ... </output> | ||
<port_map> | ||
<input external_port_id="0" internal_layer_id="0"/> | ||
<input external_port_id="1" internal_layer_id="1"/> | ||
<input external_port_id="-1" internal_layer_id="2" purpose="current_iteration"/> | ||
... | ||
<output external_port_id="3" internal_layer_id="4"/> | ||
<output external_port_id="4" internal_layer_id="10" axis="1"/> | ||
<output external_port_id="-1" internal_layer_id="22" purpose="execution_condition"/> | ||
... | ||
</port_map> | ||
<back_edges> | ||
<edge from-layer="1" to-layer="5"/> | ||
... | ||
</back_edges> | ||
<body> | ||
<layers> ... </layers> | ||
<edges> ... </edges> | ||
</body> | ||
</layer> | ||
``` |
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