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|
| import argparse |
| import copy |
| import unittest |
|
|
| import torch |
| from torch.cuda.amp import GradScaler, autocast |
|
|
| from fairseq.optim import build_optimizer |
|
|
|
|
| @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") |
| class TestGradientScalingAMP(unittest.TestCase): |
| def setUp(self): |
| self.x = torch.tensor([2.0]).cuda().half() |
| weight = 3.0 |
| bias = 5.0 |
| self.error = 1.0 |
| self.target = torch.tensor([self.x * weight + bias + self.error]).cuda() |
| self.loss_fn = torch.nn.L1Loss() |
|
|
| self.model = torch.nn.Linear(1, 1) |
| self.model.weight.data = torch.tensor([[weight]]) |
| self.model.bias.data = torch.tensor([bias]) |
| self.model.cuda() |
| self.params = list(self.model.parameters()) |
|
|
| self.namespace_dls = argparse.Namespace( |
| optimizer="adam", |
| lr=[0.1], |
| adam_betas="(0.9, 0.999)", |
| adam_eps=1e-8, |
| weight_decay=0.0, |
| threshold_loss_scale=1, |
| min_loss_scale=1e-4, |
| ) |
| self.scaler = GradScaler( |
| init_scale=1, |
| growth_interval=1, |
| ) |
|
|
| def run_iter(self, model, params, optimizer): |
| optimizer.zero_grad() |
| with autocast(): |
| y = model(self.x) |
| loss = self.loss_fn(y, self.target) |
| self.scaler.scale(loss).backward() |
| self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) |
|
|
| self.scaler.unscale_(optimizer) |
| grad_norm = optimizer.clip_grad_norm(0) |
| self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) |
|
|
| self.scaler.step(optimizer) |
| self.scaler.update() |
| self.assertEqual( |
| model.weight, |
| torch.tensor([[3.1]], device="cuda:0", requires_grad=True), |
| ) |
| self.assertEqual( |
| model.bias, |
| torch.tensor([5.1], device="cuda:0", requires_grad=True), |
| ) |
| self.assertEqual(self.scaler.get_scale(), 2.0) |
|
|
| def test_automatic_mixed_precision(self): |
| model = copy.deepcopy(self.model) |
| params = list(model.parameters()) |
| optimizer = build_optimizer(self.namespace_dls, params) |
|
|
| self.run_iter(model, params, optimizer) |
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|
