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|
| import copy |
| import logging |
| import unittest |
|
|
| import torch |
| from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer |
| from omegaconf import OmegaConf |
|
|
|
|
| @unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU") |
| class TestGradientScaling(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().half() |
| 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().half() |
| self.params = list(self.model.parameters()) |
|
|
| self.cfg_dls = OmegaConf.create( |
| { |
| "optimization": { |
| "lr": [0.1], |
| }, |
| "optimizer": { |
| "_name": "adam", |
| "lr": [0.1], |
| "adam_betas": "(0.9, 0.999)", |
| "adam_eps": 1e-8, |
| "weight_decay": 0.0, |
| }, |
| "common": { |
| "fp16_init_scale": 1, |
| "fp16_scale_window": 1, |
| "fp16_scale_tolerance": 1, |
| "threshold_loss_scale": 1, |
| "min_loss_scale": 1e-4, |
| "tpu": False, |
| }, |
| } |
| ) |
| logging.disable(logging.CRITICAL) |
|
|
| def tearDown(self): |
| logging.disable(logging.NOTSET) |
|
|
| def run_iter(self, model, params, optimizer): |
| optimizer.zero_grad() |
| y = model(self.x) |
| loss = self.loss_fn(y, self.target) |
| optimizer.backward(loss) |
| self.assertEqual(loss, torch.tensor(1.0, device="cuda:0", dtype=torch.float16)) |
|
|
| grad_norm = optimizer.clip_grad_norm(0) |
| self.assertAlmostEqual(grad_norm.item(), 2.2361, 4) |
|
|
| optimizer.step() |
| self.assertEqual( |
| model.weight, |
| torch.tensor( |
| [[3.0996]], device="cuda:0", dtype=torch.float16, requires_grad=True |
| ), |
| ) |
| self.assertEqual( |
| model.bias, |
| torch.tensor( |
| [5.1016], device="cuda:0", dtype=torch.float16, requires_grad=True |
| ), |
| ) |
| self.assertEqual(optimizer.scaler.loss_scale, 2.0) |
|
|
| def test_mixed_precision(self): |
| model = copy.deepcopy(self.model) |
| params = list(model.parameters()) |
| optimizer = FP16Optimizer.build_optimizer(self.cfg_dls, params) |
|
|
| self.run_iter(model, params, optimizer) |
| self.assertTrue( |
| all( |
| torch.all( |
| fp32_params.eq( |
| torch.tensor( |
| [3.1000, 5.1000], device="cuda:0", requires_grad=True |
| ) |
| ) |
| ) |
| for fp32_params in optimizer.fp32_params.values() |
| ) |
| ) |
|
|
| def test_memory_efficient(self): |
| model = copy.deepcopy(self.model) |
| params = list(model.parameters()) |
| optimizer = MemoryEfficientFP16Optimizer.build_optimizer(self.cfg_dls, params) |
|
|
| self.run_iter(model, params, optimizer) |
|
|
|
|
| if __name__ == "__main__": |
| unittest.main() |
|
|
