# Serving Stateful Models¶

## Stateless vs Stateful Models¶

### Stateless model¶

A stateless model treats every inference request independently and does not recognize dependencies between consecutive inference requests. Therefore, it does not maintain a state between inference requests. Examples of stateless models could be image classification and object detection Convolutional Neural Networks (CNN).

### Stateful model¶

A stateful model recognizes dependencies between consecutive inference requests. It maintains a state between inference requests so that the next inference depends on the results of previous ones. Examples of stateful models could be online speech recognition models like Long Short Term Memory (LSTM).

Note that in the context of the Model Server, a model is considered stateful if it maintains state between inference requests.

Some models might take the whole sequence of data as an input and iterate over the elements of that sequence internally, keeping the state between iterations. Such models are considered stateless since they perform inference on the whole sequence in just one inference request.

## Load and Serve Stateful Model¶

### Run Model Server with Stateful Model¶

Serving stateful model in OpenVINO Model Server is very similar to serving stateless models. The only difference is that for stateful models you need to set stateful flag in the model configuration.

• Starting OVMS with stateful model via command line:

docker run -d -u $(id -u):$(id -g) -v <host_model_path>:/models/stateful_model -p 9000:9000 openvino/model_server:latest \
--port 9000 --model_path /models/stateful_model --model_name stateful_model --stateful
• Starting OVMS with stateful model via config file:

{
"model_config_list":[
{
"config": {
"name":"stateful_model",
"base_path":"/models/stateful_model",
"stateful": true
}
}
]
}
docker run -d -u $(id -u):$(id -g) -v <host_model_path>:/models/stateful_model -v <host_config_path>:/models/config.json -p 9000:9000 openvino/model_server:latest \
--port 9000 --config_path /models/config.json

Optionally, you can also set additional parameters specific for stateful models.

### Configuration Options for Stateful Models¶

Model configuration :

Option

Value format

Description

Default value

stateful

bool

If set to true, model is loaded as stateful.

false

idle_sequence_cleanup

bool

If set to true, model will be subject to periodic sequence cleaner scans. See idle sequence cleanup .

true

max_sequence_number

uint32

Determines how many sequences can be handled concurrently by a model instance.

500

low_latency_transformation

bool

If set to true, model server will apply low latency transformation on model load.

false

Note: Setting idle_sequence_cleanup, max_sequence_number and low_latency_transformation require setting stateful to true.

Server configuration :

Option

Value format

Description

Default value

sequence_cleaner_poll_wait_minutes

uint32

Time interval (in minutes) between next sequence cleaner scans. Sequences of the models that are subjects to idle sequence cleanup that have been inactive since the last scan are removed. Zero value disables sequence cleaner. See idle sequence cleanup .

5

See also all server and model configuration options to have a complete setup.

## Run Inference on Stateful Model¶

### Special Inputs for Sequence Handling¶

Stateful model works on consecutive inference requests that are associated with each other and form a sequence of requests. A single stateful model can handle multiple independent sequences at a time. When the model server receives requests for the stateful model, it maps each request to the proper sequence and its memory state. OVMS also tracks the beginning and the end of the sequence to properly manage system resources.

Requests to stateful models must contain additional inputs besides the data for prediction:

• sequence_id - which is a 64-bit unsigned integer identifying the sequence (unique in the scope of the model instance). Value 0 is equivalent to not providing this input at all.

• sequence_control_input - which is 32-bit unsigned integer indicating sequence start and end. Accepted values are:

• 0 - no control input (has no effect - equivalent to not providing this input at all)

• 1 - indicates the beginning of the sequence

• 2 - indicates the end of the sequence

Note : Model server also appends sequence_id to every response - the name and format of sequence_id output is the same as in sequence_id input.

Both sequence_id and sequence_control_input shall be provided as tensors with 1 element array (shape:[1]) and appropriate precision.

See examples for gRPC and HTTP below.

In order to successfully infer the sequence, perform these actions:

1. Send the first request in the sequence and signal sequence start.

To start the sequence you need to add sequence_control_input with the value of 1 to your request’s inputs. You can also:

• add sequence_id with the value of your choice or

• add sequence_id with 0 or do not add sequence_id at all - in this case, the Model Server will provide a unique id for the sequence and since it will be appended to the outputs, you will be able to read it and use with the next requests.

If the provided sequence_id is already occupied, OVMS will return an error to avoid conflicts.

2. Send remaining requests except the last one.

To send requests in the middle of the sequence you need to add sequence_id of your sequence. In this case, sequence_id is mandatory and not providing this input or setting its value to 0 is not allowed.

In this case sequence_control_input must be empty or 0.

3. Send the last request in the sequence and signal sequence end.

To end the sequence you need to add sequence_control_input with the value of 2 to your request’s inputs. You also need to add sequence_id of your sequence. In this case, sequence_id is mandatory and not providing this input or setting its value to 0 is not allowed.

### Inference via gRPC¶

Inference on stateful models via gRPC is very similar to inference on stateless models (see gRPC API for reference). The difference is that requests to stateful models must containt additional inputs with information necessary for proper sequence handling.

sequence_id and sequence_control_input must be added to gRPC request inputs as TensorProtos.

• For sequence_id model server expects one value in tensor proto uint64_val field.

• For sequence_control_input model server expects one value in tensor proto uint32_val field.

Both inputs must have TensorShape set to [1] and appropriate DataType :

• DT_UINT64 for sequence_id

• DT_UINT32 for sequence_control_input

Example: (using Python tensorflow and tensorflow-serving-api packages):

...
import grpc

from tensorflow_serving.apis import prediction_service_pb2_grpc
from tensorflow import make_tensor_proto, make_ndarray, expand_dims
from tensorflow_serving.apis import predict_pb2

...

SEQUENCE_START = 1
SEQUENCE_END = 2
sequence_id = 10

channel = grpc.insecure_channel("localhost:9000")
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

request = predict_pb2.PredictRequest()
request.model_spec.name = "stateful_model"

"""
Add inputs with data to infer
"""

################   Add stateful specific inputs   #################

################ Starting sequence with custom ID #################

request.inputs['sequence_control_input'].CopyFrom(
make_tensor_proto([SEQUENCE_START], dtype="uint32"))
request.inputs['sequence_id'].CopyFrom(
make_tensor_proto([sequence_id], dtype="uint64"))

################   Starting sequence without ID   #################

request.inputs['sequence_control_input'].CopyFrom(
make_tensor_proto([SEQUENCE_START], dtype="uint32"))

################       Non control requests       #################

request.inputs['sequence_id'].CopyFrom(
make_tensor_proto([sequence_id], dtype="uint64"))

################         Ending sequence          #################

request.inputs['sequence_control_input'].CopyFrom(
make_tensor_proto([SEQUENCE_END], dtype="uint32"))
request.inputs['sequence_id'].CopyFrom(
make_tensor_proto([sequence_id], dtype="uint64"))

###################################################################

# Send request to OVMS and get response
response = stub.Predict(request, 10.0)

# response variable now contains model outputs (inference results) as well as sequence_id in response.outputs

# Fetch sequence id from the response
sequence_id = response.outputs['sequence_id'].uint64_val[0]

See grpc_stateful_client.py example client for reference.

### Inference via HTTP¶

Inference on stateful models via HTTP is very similar to inference on stateless models (see REST API for reference). The difference is that requests to stateful models must containt additional inputs with information necessary for proper sequence handling.

sequence_id and sequence_control_input must be added to HTTP request by adding new key:value pair in inputs field of JSON body.

For both inputs, the value must be a single number in a 1-dimensional array.

Example: (using Python requests package):

...
import json
import requests
...

SEQUENCE_START = 1
SEQUENCE_END = 2
sequence_id = 10

inputs = {}

"""
Add inputs with data to infer
"""

################   Add stateful specific inputs   #################

################ Starting sequence with custom ID #################

inputs['sequence_control_input'] = [int(SEQUENCE_START)]
inputs['sequence_id'] = [int(sequence_id)]

################   Starting sequence without ID   #################

inputs['sequence_control_input'] = [int(SEQUENCE_START)]

################       Non control requests       #################

inputs['sequence_id'] = [int(sequence_id)]

################         Ending sequence          #################

inputs['sequence_control_input'] = [int(SEQUENCE_END)]
inputs['sequence_id'] = [int(sequence_id)]

###################################################################

# Prepare request
signature = "serving_default"
request_body = json.dumps({"signature_name": signature,'inputs': inputs})

# Send request to OVMS and get response
response = requests.post("localhost:5555/v1/models/stateful_model:predict", data=request_body)

# Parse response
response_body = json.loads(response.text)

# response_body variable now contains model outputs (inference results) as well as sequence_id in response_body["outputs"]

# Fetch sequence id from the response
sequence_id = response_body["outputs"]["sequence_id"]

See rest_stateful_client.py example client for reference.

### Error Codes¶

When a request is invalid or could not be processed, you can expect following errors specific to inference on stateful models:

Description

gRPC

HTTP

Sequence with a provided ID does not exist.

NOT_FOUND

404 NOT FOUND

Sequence with a provided ID already exists.

ALREADY_EXISTS

409 CONFLICT

Server received SEQUENCE START request with ID of the sequence that is set for termination, but the last request of that sequence is still being processed.

FAILED_PRECONDITION

412 PRECONDITION FAILED

Max sequence number has been reached. Could not create a new sequence.

UNAVAILABLE

503 SERVICE UNAVAILABLE

Sequence ID has not been provided in request inputs.

INVALID_ARGUMENT

400 BAD REQUEST

Unexpected value of sequence control input.

INVALID_ARGUMENT

400 BAD REQUEST

Could not find sequence id in expected tensor proto field uint64_val.

INVALID_ARGUMENT

N/A

Could not find sequence control input in expected tensor proto field uint32_val.

INVALID_ARGUMENT

N/A

Special input proto does not contain tensor shape information.

INVALID_ARGUMENT

N/A

## Idle Sequence Cleanup¶

Once started sequence might get dropped for some reason like lost connection etc. In this case model server will not receive SEQUENCE_END signal and will not free sequence resources. To prevent keeping idle sequences indefinitely, mthe Model Server launches a sequence cleaner thread that periodically scans stateful models and checks if their sequences received any valid inference request recently. If not, such sequences are removed, their resources are freed and their ids can be reused.

Two parameters regulate sequence cleanup. One is sequence_cleaner_poll_wait_minutes which holds the value of the time interval between the next scans. If there has been not a single valid request with a particular sequence id between two consecutive checks, the sequence is considered idle and gets deleted.

sequence_cleaner_poll_wait_minutes is a server parameter and is common for all models. By default, the time between two consecutive cleaner scans is set to 5 minutes. Setting this value to 0 disables sequence cleaner.

Stateful models can either be subject to idle sequence cleanup or not. You can set this per model with idle_sequence_cleanup parameter. If set to true sequence cleaner will check that model. Otherwise, sequence cleaner will skip that model, and its inactive sequences will not get removed. By default, this value is set to true.

## Known Limitations¶

There are limitations for using stateful models with OVMS:

• Support inference execution only using CPU as the target device.

• Support Kaldi models with memory layers and non-Kaldi models with Tensor Iterator. See this docs about stateful networks to learn about stateful networks representation in OpenVINO.

• Auto batch size and shape are not available in stateful models.

• Stateful model instances cannot be used in DAGs.

• Requests ordering is guaranteed only when a single client sends subsequent requests in a synchronous manner. Concurrent interaction with the same sequence might negatively affect the accuracy of the results.

• When stateful model instance gets reloaded due to change in model configuration, all ongoing sequences are dropped.

• Model type cannot be changed in the runtime - switching stateful flag will be rejected.