Providers

Model Provider

def deepview_model_provider() -> torch.nn.Module:
    pass

The model provider must take no arguments and return an instance of your model (a torch.nn.Module) that is on the GPU (i.e. you need to call .cuda() on the module before returning it).

Input Provider

def deepview_input_provider(batch_size: int = 32) -> Tuple:
    pass

The input provider must take a single batch_size argument that has a default value (the batch size you want to profile with). It must return an iterable (does not have to be a tuple) that contains the arguments that you would normally pass to your model’s forward method. Any Tensors in the returned iterable must be on the GPU (i.e. you need to call .cuda() on them before returning them).

Iteration Provider

def deepview_iteration_provider(model: torch.nn.Module) -> Callable:
    pass

The iteration provider must take a single model argument, which will be an instance of your model. This provider must return a callable (e.g., a function) that, when invoked, runs a single training iteration.

Example

Suppose that your project code is kept under a my_project directory:

my_project
├── __init__.py
└── model.py

and your model is defined in model.py:

import torch.nn as nn


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3)
        self.linear = nn.Linear(in_features=387096, out_features=10)

    def forward(self, input):
        out = self.conv(input)
        return self.linear(out.view(-1, 387096))

One way to write the entry_point.py file would be:

import torch
import torch.nn as nn

from my_project.model import Model


class ModelWithLoss(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = Model()
        self.loss_fn = nn.CrossEntropyLoss()

    def forward(self, input, target):
        output = self.model(input)
        return self.loss_fn(output, target)


def deepview_model_provider():
    # Return a GPU-based instance of our model (that returns a loss)
    return ModelWithLoss().cuda()


def deepview_input_provider(batch_size=32):
    # Return GPU-based inputs for our model
    return (
      torch.randn((batch_size, 3, 256, 256)).cuda(),
      torch.randint(low=0, high=9, size=(batch_size,)).cuda(),
    )


def deepview_iteration_provider(model):
    # Return a function that executes one training iteration
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
    def iteration(*inputs):
        optimizer.zero_grad()
        out = model(*inputs)
        out.backward()
        optimizer.step()
    return iteration

One important thing to highlight is our use of a wrapper ModelWithLoss module. DeepView.Profile only provides breakdowns for operations that run inside the module returned by the model provider. We included the loss function in this wrapper module to have DeepView.Profile include it in the breakdown. We could have also placed the loss function call in the iteration function.

You can place these provider functions either in a new file or directly in model.py. Whichever file contains the providers will be your project’s entry point file. In this example, we defined the providers in a separate file called entry_point.py inside my_project.