norch 0.0.5

A deep learning framework

PyNorch

Recreating PyTorch from scratch (C/C++, CUDA and Python, with multi-GPU support and automatic differentiation!)

Project details explanations can also be found on medium.

1 - About

PyNorch is a deep learning framework constructed using C/C++, CUDA and Python. This is a personal project with educational purpose only! Norch means NOT PyTorch, and we have NO claims to rivaling the already established PyTorch. The main objective of PyNorch was to give a brief understanding of how a deep learning framework works internally. It implements the Tensor object, multi-GPU support and an automatic differentiation system.

2 - Installation

Install this package from PyPi (you can test on Colab! Also tested on AWS g4dn.12xlarge instance with image ami-061debf863768593d)

$ pip install norch

or from cloning this repository

$ git clone https://github.com/lucasdelimanogueira/PyNorch.git
$ cd PyNorch
$ pip install . -v

3 - Get started

3.1 - Tensor operations

import norch

x1 = norch.Tensor([[1, 2], 
                  [3, 4]], requires_grad=True).to("cuda")

x2 = norch.Tensor([[4, 3], 
                  [2, 1]], requires_grad=True).to("cuda)

x3 = x1 @ x2
result = x3.sum()
result.backward

print(x1.grad)

3.2 - Create a model

import norch
import norch.nn as nn
import norch.optim as optim

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(1, 10)
        self.sigmoid = nn.Sigmoid()
        self.fc2 = nn.Linear(10, 1)

    def forward(self, x):
        out = self.fc1(x)
        out = self.sigmoid(out)
        out = self.fc2(out)
        
        return out

3.3 - Example single GPU training

# examples/train_singlegpu.py

import norch
from norch.utils.data.dataloader import DataLoader
from norch.norchvision import transforms as T
import norch
import norch.nn as nn
import norch.optim as optim
import random
random.seed(1)

BATCH_SIZE = 32
device = "cuda" #cpu
epochs = 10

transform = T.Compose(
    [
        T.ToTensor(),
        T.Reshape([-1, 784, 1])
    ]
)

target_transform = T.Compose(
    [
        T.ToTensor()
    ]
)

train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
train_loader = DataLoader(train_data, batch_size = BATCH_SIZE)

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(784, 30)
        self.sigmoid1 = nn.Sigmoid()
        self.fc2 = nn.Linear(30, 10)
        self.sigmoid2 = nn.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.sigmoid1(out)
        out = self.fc2(out)
        out = self.sigmoid2(out)
        
        return out

model = MyModel().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
loss_list = []

for epoch in range(epochs):    
    for idx, batch in enumerate(train_loader):

        inputs, target = batch

        inputs = inputs.to(device)
        target = target.to(device)

        outputs = model(inputs)
        
        loss = criterion(outputs, target)
        
        optimizer.zero_grad()
        
        loss.backward()

        optimizer.step()

    print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')
    loss_list.append(loss[0])

3.4 - Example multi-GPU training

First create a file .py as the example below

# examples/train_multigpu.py

import os
import norch
import norch.distributed as dist
import norch.distributed
import norch.nn as nn
import norch.optim as optim
from norch.nn.parallel import DistributedDataParallel
from norch.utils.data.distributed import DistributedSampler
from norch.norchvision import transforms as T
import random
random.seed(1)

local_rank = int(os.getenv('OMPI_COMM_WORLD_LOCAL_RANK', -1))
rank = int(os.getenv('OMPI_COMM_WORLD_RANK', -1))
world_size = int(os.getenv('OMPI_COMM_WORLD_SIZE', -1))

dist.init_process_group(
    rank, 
    world_size
)

BATCH_SIZE = 32
device = local_rank
epochs = 10

transform = T.Compose(
    [
        T.ToTensor(),
        T.Reshape([-1, 784, 1])
    ]
)

target_transform = T.Compose(
    [
        T.ToTensor()
    ]
)

train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
distributed_sampler = DistributedSampler(dataset=train_data, num_replicas=world_size, rank=rank)
train_loader = norch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE, sampler=distributed_sampler)

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(784, 30)
        self.sigmoid1 = nn.Sigmoid()
        self.fc2 = nn.Linear(30, 10)
        self.sigmoid2 = nn.Sigmoid()

    def forward(self, x):
        out = self.fc1(x)
        out = self.sigmoid1(out)
        out = self.fc2(out)
        out = self.sigmoid2(out)
        
        return out

model = MyModel().to(device)
model = DistributedDataParallel(model)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
loss_list = []

print(f"Starting training on Rank {rank}/{world_size}\n\n")

for epoch in range(epochs):    
    for idx, batch in enumerate(train_loader):

        inputs, target = batch

        inputs = inputs.to(device)
        target = target.to(device)

        outputs = model(inputs)
        
        loss = criterion(outputs, target)
        
        optimizer.zero_grad()
        
        loss.backward()

        optimizer.step()
    
    if rank == 0:
        print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')
        loss_list.append(loss[0])

Then you can run using

$ python3 -m norch.distributed.run --nproc_per_node 4 examples/train_multigpu.py

4 - Progress

Development	Status	Feature
Operations	in progress	[X] GPU Support [X] Autograd [X] Broadcasting [ ] Memory Management
Loss	in progress	[x] MSE [X] Cross Entropy
Data	in progress	[X] Dataset [X] Batch [X] Iterator
Convolutional Neural Network	in progress	[ ] Conv2d [ ] MaxPool2d [ ] Dropout
Distributed	in progress	[X] All-reduce [X] Broadcast [X] DistributedSampler [X] DistributedDataParallel