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from itertools import product
import random
import unittest
import torch
import apex
class TestFusedOptimizer(unittest.TestCase):
def setUp(self, max_abs_diff=1e-3, max_rel_diff=1, iters=7):
self.max_abs_diff = max_abs_diff
self.max_rel_diff = max_rel_diff
self.iters = iters
torch.manual_seed(9876)
def tearDown(self):
pass
def gen_param_optim(self, tensors, options, tst_options=None):
# Adding this to make backward compatible with existing tests. Just in
# case "tst_options" are not provided, it gets a copy of options
# which contains the parameters for the reference optimizer
if tst_options == None:
tst_options = options
ref_param = []
tst_param = []
for tensor in tensors:
ref_param.append(torch.nn.Parameter(tensor.clone()))
tst_param.append(torch.nn.Parameter(tensor.clone()))
ref_optim = self.ref_optim(ref_param, **options)
tst_optim = self.fused_optim(tst_param, **tst_options)
return (ref_param, tst_param, ref_optim, tst_optim)
def gen_grad(self, ref_param, tst_param):
for p_ref, p_tst in zip(ref_param, tst_param):
p_ref.grad = torch.rand_like(p_ref)
p_tst.grad = p_ref.grad
def gen_mixed_grad(self, ref_param, tst_param, scale=1.0):
half_grads = []
for p_ref, p_tst in zip(ref_param, tst_param):
half_grads.append(torch.rand_like(p_ref).half())
p_ref.grad = half_grads[-1].float() / scale
return half_grads
def get_max_diff(self, ref_param, tst_param):
max_abs_diff = max_rel_diff = 0
for p_ref, p_tst in zip(ref_param, tst_param):
max_abs_diff_p = (p_ref - p_tst).abs().max().item()
max_rel_diff_p = ((p_ref - p_tst) / p_ref).abs().max().item()
if max_abs_diff_p > max_abs_diff: max_abs_diff = max_abs_diff_p
if max_rel_diff_p > max_rel_diff: max_rel_diff = max_rel_diff_p
return max_abs_diff, max_rel_diff
def gen_single_type_test(self, param_type=torch.float, device='cuda', *, skip_assert: bool = False):
nelem = 278011
# Some ref and test optimizers may require different set of options.
# This is a quick workaround to add that functionality while making
# minimum changes in existing code.
# If there is no "tst_options" field provided, safe to initialize
# the test optimizer with the parameters of reference optimizer.
if not hasattr(self, 'tst_options'):
self.tst_options = self.options
tensor = torch.rand(nelem, dtype=param_type, device=device)
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim([tensor], self.options, self.tst_options)
for i in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
if skip_assert:
return
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
class TestFusedAdam(TestFusedOptimizer):
def setUp(self):
super().setUp()
self.options = {'lr':5e-4, 'betas':(0.9, 0.999), 'eps':1e-08,
'weight_decay': 0, 'amsgrad': False}
self.ref_optim = torch.optim.Adam
self.fused_optim = apex.optimizers.FusedAdam
def test_float(self):
self.gen_single_type_test(param_type=torch.float)
# NOTE(mkozuki): Current threshold values look too small for BFloat16.
# TODO(mkozuki): Refactor `TestFusedOptimizer`
def test_half(self):
self.gen_single_type_test(param_type=torch.float16, skip_assert=True)
def test_bfloat16(self):
self.gen_single_type_test(param_type=torch.bfloat16, skip_assert=True)
@unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
def test_multi_device(self):
devices = ("cuda:0", "cuda:1")
for current_dev, tensor_dev in product(devices, devices):
with torch.cuda.device(current_dev):
self.gen_single_type_test(param_type=torch.float, device=tensor_dev)
@unittest.skip('Disable until 8/1/2019 adam/adamw upstream picked')
def test_multi_params(self):
sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
tensors = []
for size in sizes:
tensors.append(torch.rand(size, dtype=torch.float, device='cuda'))
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim(tensors, self.options)
for i in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
@unittest.skip('No longer support fuse scaling')
def test_scale(self):
nelem = 278011
tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim([tensor], self.options)
for i in range(self.iters):
scale = random.random() * 1000
half_grads = self.gen_mixed_grad(ref_param, tst_param, scale)
ref_optim.step()
tst_optim.step(grads=half_grads, scale=scale)
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
@unittest.skip('No longer support output fp16 param')
def test_fp16_output(self):
nelem = 278011
tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim([tensor], self.options)
fp16_param = torch.nn.Parameter(tensor.clone().half())
for i in range(self.iters):
half_grads = self.gen_mixed_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step(grads=half_grads, output_params=[fp16_param])
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
max_abs_diff, max_rel_diff = self.get_max_diff(tst_param, \
[fp16_param.float()])
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
def test_adam_option(self):
nelem = 1
adam_option = {'lr':0.01, 'betas':(0.6, 0.9), 'eps':3e-06,
'weight_decay':0, 'amsgrad':False}
tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim([tensor], adam_option)
for i in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
def test_frozen_model(self):
nelem = 1
adam_option = {'lr':0.01, 'betas':(0.6, 0.9), 'eps':3e-06,
'weight_decay':0, 'amsgrad':False}
tensor = torch.rand(nelem, dtype=torch.float, device='cuda')
ref_param, tst_param, ref_optim, tst_optim = \
self.gen_param_optim([tensor], adam_option)
#Add an empty param group which may occur for pipeline parallel p-tuning
tst_optim.add_param_group({"params": []})
for i in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
class TestFusedAdagrad(TestFusedOptimizer):
def __init__(self, *args, **kwargs):
super(TestFusedAdagrad, self).__init__(*args, **kwargs)
self.options = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 1.0e-5}
self.ref_optim = torch.optim.Adagrad
self.fused_optim = apex.optimizers.FusedAdagrad
def test_float(self):
self.gen_single_type_test(param_type=torch.float)
@unittest.skip("PyTorch optimizer is not numerically correct for fp16")
def test_half(self):
self.gen_single_type_test(param_type=torch.float16)
@unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
def test_multi_device(self):
devices = ("cuda:0", "cuda:1")
for current_dev, tensor_dev in product(devices, devices):
with torch.cuda.device(current_dev):
self.gen_single_type_test(param_type=torch.float, device=tensor_dev)
def test_multi_params(self):
sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
adagrad_option = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 0}
tensors = []
for size in sizes:
tensors.append(torch.rand(size, dtype=torch.float, device="cuda"))
ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
tensors, adagrad_option
)
for _ in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
@unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
def test_multi_params_different_devices_throws(self):
sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]]
adagrad_option = {"lr": 5e-4, "eps": 1e-08, "weight_decay": 0}
tensors = []
for i, size in enumerate(sizes):
tensors.append(torch.rand(size, dtype=torch.float, device="cuda:"+str(i % 2)))
ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
tensors, adagrad_option
)
self.gen_grad(ref_param, tst_param)
with self.assertRaisesRegex(RuntimeError, "not on the same device"):
tst_optim.step()
def test_adagrad_option(self):
nelem = 1
adagrad_option = {"lr": 0.01, "eps": 3e-06, "weight_decay": 0}
tensor = torch.rand(nelem, dtype=torch.float, device="cuda")
ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim(
[tensor], adagrad_option
)
for _ in range(self.iters):
self.gen_grad(ref_param, tst_param)
ref_optim.step()
tst_optim.step()
max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param)
self.assertLessEqual(max_abs_diff, self.max_abs_diff)
self.assertLessEqual(max_rel_diff, self.max_rel_diff)
class TestFusedSGD(TestFusedOptimizer):
def __init__(self, *args, **kwargs):
super(TestFusedSGD, self).__init__(*args, **kwargs)
self.options = {"lr": .25, "momentum": .125}
self.ref_optim = torch.optim.SGD
self.fused_optim = apex.optimizers.FusedSGD
def test_float(self):
self.gen_single_type_test(param_type=torch.float)
def test_half(self):
self.gen_single_type_test(param_type=torch.float16)
@unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
def test_multi_device(self):
devices = ("cuda:0", "cuda:1")
for current_dev, tensor_dev in product(devices, devices):
with torch.cuda.device(current_dev):
self.gen_single_type_test(param_type=torch.float, device=tensor_dev)
if __name__ == '__main__':
unittest.main()
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