mirror of https://github.com/hpcaitech/ColossalAI
[NVIDIA] Add FP8 example using TE (#3080)
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>pull/3098/head
Kirthi Shankar Sivamani
GitHub
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# Basic MNIST Example with optional FP8
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```bash
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python main.py
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python main.py --use-te # Linear layers from TransformerEngine
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python main.py --use-fp8 # FP8 + TransformerEngine for Linear layers
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```
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# Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# See LICENSE for license information.
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import argparse
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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from torch.optim.lr_scheduler import StepLR
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try:
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from transformer_engine import pytorch as te
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HAVE_TE = True
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except (ImportError, ModuleNotFoundError):
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HAVE_TE = False
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class Net(nn.Module):
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def __init__(self, use_te=False):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 32, 3, 1)
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self.conv2 = nn.Conv2d(32, 64, 3, 1)
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self.dropout1 = nn.Dropout(0.25)
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self.dropout2 = nn.Dropout(0.5)
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if use_te:
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self.fc1 = te.Linear(9216, 128)
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self.fc2 = te.Linear(128, 16)
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else:
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self.fc1 = nn.Linear(9216, 128)
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self.fc2 = nn.Linear(128, 16)
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self.fc3 = nn.Linear(16, 10)
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def forward(self, x):
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"""FWD"""
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x = self.conv1(x)
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x = F.relu(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = F.max_pool2d(x, 2)
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x = self.dropout1(x)
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x = torch.flatten(x, 1)
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x = self.fc1(x)
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x = F.relu(x)
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x = self.dropout2(x)
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x = self.fc2(x)
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x = self.fc3(x)
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output = F.log_softmax(x, dim=1)
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return output
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def train(args, model, device, train_loader, optimizer, epoch, use_fp8):
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"""Training function."""
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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with te.fp8_autocast(enabled=use_fp8):
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print(
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f"Train Epoch: {epoch} "
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f"[{batch_idx * len(data)}/{len(train_loader.dataset)} "
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f"({100. * batch_idx / len(train_loader):.0f}%)]\t"
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f"Loss: {loss.item():.6f}"
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)
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if args.dry_run:
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break
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def calibrate(model, device, test_loader):
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"""Calibration function."""
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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with te.fp8_autocast(enabled=False, calibrating=True):
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output = model(data)
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def test(model, device, test_loader, use_fp8):
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"""Testing function."""
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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with te.fp8_autocast(enabled=use_fp8):
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output = model(data)
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test_loss += F.nll_loss(
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output, target, reduction="sum"
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).item() # sum up batch loss
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pred = output.argmax(
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dim=1, keepdim=True
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) # get the index of the max log-probability
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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print(
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f"\nTest set: Average loss: {test_loss:.4f}, "
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f"Accuracy: {correct}/{len(test_loader.dataset)} "
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f"({100. * correct / len(test_loader.dataset):.0f}%)\n"
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)
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def main():
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# Training settings
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parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
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parser.add_argument(
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"--batch-size",
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type=int,
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default=64,
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metavar="N",
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help="input batch size for training (default: 64)",
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)
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parser.add_argument(
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"--test-batch-size",
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type=int,
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default=1000,
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metavar="N",
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help="input batch size for testing (default: 1000)",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=14,
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metavar="N",
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help="number of epochs to train (default: 14)",
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)
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parser.add_argument(
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"--lr",
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type=float,
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default=1.0,
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metavar="LR",
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help="learning rate (default: 1.0)",
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)
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parser.add_argument(
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"--gamma",
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type=float,
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default=0.7,
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metavar="M",
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help="Learning rate step gamma (default: 0.7)",
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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default=False,
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help="quickly check a single pass",
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)
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parser.add_argument(
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"--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
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)
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parser.add_argument(
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"--log-interval",
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type=int,
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default=10,
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metavar="N",
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help="how many batches to wait before logging training status",
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)
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parser.add_argument(
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"--save-model",
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action="store_true",
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default=False,
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help="For Saving the current Model",
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)
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parser.add_argument(
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"--use-fp8", action="store_true", default=False, help="Use FP8 for inference and training without recalibration"
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)
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parser.add_argument(
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"--use-fp8-infer", action="store_true", default=False, help="Use FP8 inference only"
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)
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parser.add_argument(
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"--use-te", action="store_true", default=False, help="Use Transformer Engine"
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)
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args = parser.parse_args()
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use_cuda = torch.cuda.is_available()
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if args.use_te or args.use_fp8 or args.use_fp8_infer:
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assert HAVE_TE, "TransformerEngine not installed."
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if args.use_fp8 or args.use_fp8_infer:
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args.use_te = True
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if args.use_te:
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assert use_cuda, "CUDA needed for FP8 execution."
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if args.use_fp8_infer:
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assert not args.use_fp8, "fp8-infer path currently only supports calibration from a bfloat checkpoint"
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torch.manual_seed(args.seed)
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device = torch.device("cuda" if use_cuda else "cpu")
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train_kwargs = {"batch_size": args.batch_size}
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test_kwargs = {"batch_size": args.test_batch_size}
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if use_cuda:
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cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
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train_kwargs.update(cuda_kwargs)
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test_kwargs.update(cuda_kwargs)
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transform = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
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)
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dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
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dataset2 = datasets.MNIST("../data", train=False, transform=transform)
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train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
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test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
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model = Net(use_te=args.use_te).to(device)
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optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
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scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch, args.use_fp8)
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test(model, device, test_loader, args.use_fp8)
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scheduler.step()
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if args.use_fp8_infer:
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calibrate(model, device, test_loader)
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if args.save_model or args.use_fp8_infer:
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torch.save(model.state_dict(), "mnist_cnn.pt")
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print('Eval with reloaded checkpoint : fp8='+str(args.use_fp8_infer))
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weights = torch.load("mnist_cnn.pt")
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model.load_state_dict(weights)
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test(model, device, test_loader, args.use_fp8_infer)
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if __name__ == "__main__":
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main()
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