Example 3: Multiple Models in a Vollo Program

Vollo supports putting multiple models on a single accelerator.

Multiple NNIRs can be compiled into a single program:

import torch
import torch.nn as nn
import torch.nn.functional as F
import vollo_torch

class MLP(nn.Module):
    def __init__(self, input_size, output_size, hidden_size):
        super().__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        residual = x
        x = F.relu(self.fc2(x)) + residual
        return self.out(x)

# Instantiate an MLP
input_size = 784
output_size = 10
hidden_size = 128
mlp_model = MLP(input_size, output_size, hidden_size)
mlp_input = torch.randn(input_size)
(mlp_model, mlp_expected_output) = vollo_torch.fx.prepare_shape(mlp_model, mlp_input)
mlp_nnir = vollo_torch.fx.nnir.to_nnir(mlp_model)

class CNN(nn.Module):
    def __init__(self, in_channels, out_channels, hidden_channels, kernel_size=3):
        super().__init__()
        self.conv1 = vollo_torch.nn.PaddedConv1d(in_channels, hidden_channels, kernel_size)
        self.conv2 = vollo_torch.nn.PaddedConv1d(hidden_channels, out_channels, kernel_size)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        return x

# Instantiate a CNN
in_channels = 32
out_channels = 1
hidden_channels = 128
cnn_model = CNN(in_channels, out_channels, hidden_channels)

batch_size = 1
sequence_length = 5
cnn_input = torch.randn(batch_size, in_channels, sequence_length)
(cnn_model, cnn_expected_output) = vollo_torch.fx.prepare_shape(cnn_model, cnn_input)
cnn_nnir = vollo_torch.fx.nnir.to_nnir(cnn_model)
(cnn_nnir, output_axis) = cnn_nnir.streaming_transform(2)

# Compile the multi-model program
import vollo_compiler
program_builder = vollo_compiler.ProgramBuilder(vollo_compiler.Config.ia_420f_c6b32())
program_builder.add_nnir(mlp_nnir)
program_builder.add_nnir(cnn_nnir)
multi_model_program = program_builder.to_program()

The vollo_compiler.ProgramBuilder allows you to create a multi-model program. Building a multi-model program may give an allocation error if the models can't fit on the given Config. Generally each individual model will only have a small latency overhead compared to running it as an individual program. This overhead comes from selecting which model to run.

A model_index can be provided when running inferences on the accelerator or on the VM. The models appear in the order in which they were added to the ProgramBuilder. For example on the VM:

vm = multi_model_program.to_vm()

mlp_vm_output = vm.run(mlp_input.detach().numpy(), model_index = 0)
torch.testing.assert_close(mlp_expected_output, torch.from_numpy(mlp_vm_output))

cnn_vm_outputs = []
for i in range(5):
    cnn_vm_outputs.append(vm.run(cnn_input[:, :, i].detach().numpy(), model_index = 1))

torch.testing.assert_close(
    cnn_expected_output,
    torch.stack(
        [torch.from_numpy(output) for output in cnn_vm_outputs],
        axis=output_axis,
    ),
)