# 6. Grouping graph replicas

This section details how to use the popart.VariableSettings class for the purpose of grouping tensor weights across replicas. For a detailed description of what a replica is, refer to the Replication section in the IPU Programmer’s Guide.

## 6.1. Concept

When using graph replication, variables by default contain the same value on all replicas. With the help of VariableSettings, we can assign distinct tensor values to and retrieve tensor values from groups of replicas, removing the limitation of assigning the same value to all replicas.

## 6.2. VariableSettings

The VariableSettings object is initialized with two values: a CommGroup and a VariableRetrievalMode. CommGroup is used to set the communication groups this tensor is divided into across replicas, and VariableRetrievalMode lets you specify how to retrieve variables from the replicas.

The CommGroup class is composed of the CommGroupType enum, and the size of each group. Possible values for CommGroupType are:

• popart.CommGroupType.All: This is the default group type in which all replicas are considered to be a part of one group. This means that all replicas use the same variable values. This CommGroupType ignores group size. An example of such a grouping is shown in Table 6.1 and in Fig. 6.1.

Table 6.1 Replication factor 16, CommGroupType = All

Group

Replicas

0

0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

• popart.CommGroupType.Consecutive: Adjacent replicas (based on replica index) are grouped together. Each group has a size equal to the size CommGroup is instantiated with. For example, for 16 replicas (replica index = 0 to 15) and a group size of 4, the groups are assigned as shown in Table 6.2 and in Fig. 6.1.

Table 6.2 Replication factor 16, CommGroupType = Consecutive, CommGroup size = 4

Group

Replicas

0

0, 1, 2, 3

1

4, 5, 6, 7

2

8, 9, 10, 11

3

12, 13, 14, 15

• popart.CommGroupType.Orthogonal: Replicas are assigned to groups with a stride defined by the number of groups num_groups where $$num_groups = \frac{number of replicas}{group size}$$. For example, for 16 replicas (replica index = 0 to 15) and a group size of 4, there will be four groups and they are assigned as shown in Table 6.3 and in Fig. 6.1.

Table 6.3 Replication factor 16, CommGroupType = Orthogonal, CommGroup size = 4

Group

Replicas

0

0, 4, 8, 12

1

1, 5, 9, 13

2

2, 6, 10, 14

3

3, 7, 11, 15

• popart.CommGroupType.None: Each replica is in its own group. For example, for 16 replicas (replica index = 0 to 15), the groups are as shown in Table 6.4 and in Fig. 6.1.

Table 6.4 Replication factor 16, CommGroupType = None

Group

Replicas

0

0

1

1

2

2

14

14

15

15

## 6.3. Instantiating variables with VariableSettings

The number of replicas, the replication factor, is needed to create variables with popart.VariableSettings because the number of communication groups requiring initialization, and thus the size of the instantiating buffer, depends on the replication factor.

popart.VariableSettings can be added to the call to the builder function addInitializedInputTensor() when initiating your variable.

The instantiating buffers used for creating these variables have to be sized such that they initialize each group individually. This is done by adding an outer dimension to the instantiating buffer equal to the number of groups, and the graph builder will handle the rest. For example, a tensor with shape [2, 3, 4] and a replication factor that results in four groups must be instantiated with a shape of [4, 2, 3, 4], where [r, ...] instantiates the variable on replica r.

## 6.4. Weight input/output

When using PyWeightsIO to read the value of the weights, the instantiating buffer size must match the size of the initializing data, and if VariableRetrievalMode is AllReplicas, the outer dimension must match the replication factor.

For example, using VariableSettings with a tensor of shape [2, 3, 4], a replication factor of 4 and CommGroup instantiated with Consecutive and a group size of 2, we need a buffer for PyWeightsIO with one of the following shapes:

• [2, 2, 3, 4] if we use popart.VariableRetrievalMode.OnePerGroup.

• [4, 2, 3, 4] if we use popart.VariableRetrievalMode.AllReplicas.

The on-device buffer is populated by the session readWeights() function for training or for inference.

Listing 6.1 shows an example of creating buffers for replicas.

Listing 6.1 Creating buffers for replicas.
# Copyright (c) 2022 Graphcore Ltd. All rights reserved.
import popart
import numpy
from popart import CommGroup, CommGroupType
from popart import VariableRetrievalMode, VariableSettings

builder = popart.Builder()

# replication factor
repl_factor = 4

# Simple base shape of variable on replica
base_shape = [3, 5]

# size of each group
group_size = 2

# The CommGroup we plan to use
communication_group = CommGroup(CommGroupType.Consecutive, group_size)

# VariableSettings to read from groups
settings_grouped = VariableSettings(
communication_group, VariableRetrievalMode.OnePerGroup
)

# VariableSettings to read from all replicas
settings_individual = VariableSettings(
communication_group, VariableRetrievalMode.AllReplicas
)

# get init buffer:
num_groups = settings_grouped.getGroupCount(repl_factor)
shape = [int(repl_factor / num_groups)] + base_shape
initializer = numpy.zeros(shape).astype(numpy.float32)  # example

print(initializer.dtype)

# Creating Variables

# get IO buffer shapes
shape_a = [settings_grouped.numReplicasReturningVariable(repl_factor)] + base_shape
shape_b = [settings_individual.numReplicasReturningVariable(repl_factor)] + base_shape

# get IO buffers
buffer_a = numpy.ndarray(shape_a)
buffer_b = numpy.ndarray(shape_b)

# finalize IO buffers
weightsIo = popart.PyWeightsIO({a: buffer_a, b: buffer_b})


## 6.5. ONNX checkpoints

ONNX is not, by default, aware of the replication factor. Therefore, the ONNX model will attempt to interpret the outermost dimension as a part of each replica. This will usually break the logic of the model.

To address this, the builder function embedReplicationFactor() writes the replication factor into the ONNX model as an attribute of the graph.

The builder does not need the replication factor embedded when using resetHostWeights() for training or for inference to write an ONNX file into a new model.