410 lines
18 KiB
Python
410 lines
18 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
|
|
#
|
|
# This source code is licensed under the MIT license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
import os
|
|
import time
|
|
import shutil
|
|
import uuid
|
|
import pytest
|
|
import ctypes
|
|
import torch
|
|
import bitsandbytes as bnb
|
|
import bitsandbytes.functional as F
|
|
|
|
from os.path import join
|
|
from itertools import product
|
|
|
|
import apex
|
|
|
|
def get_temp_dir():
|
|
path = '/tmp/autoswap/{0}'.format(str(uuid.uuid4()))
|
|
os.makedirs(path, exist_ok=True)
|
|
return path
|
|
|
|
def rm_path(path):
|
|
shutil.rmtree(path)
|
|
|
|
str2optimizers = {}
|
|
str2optimizers['adam_pytorch'] = (None, torch.optim.Adam, bnb.optim.Adam)
|
|
str2optimizers['adam_apex'] = (None, apex.optimizers.FusedAdam, bnb.optim.Adam)
|
|
str2optimizers['momentum_apex'] = (None, lambda pxx: apex.optimizers.FusedSGD(pxx, 0.01, 0.9), bnb.optim.Adam)
|
|
str2optimizers['momentum_pytorch'] = (None, lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), bnb.optim.Adam)
|
|
str2optimizers['lamb_apex'] = (None, lambda pxx: apex.optimizers.FusedLAMB(pxx, weight_decay=0.00, use_nvlamb=True), bnb.optim.Adam)
|
|
str2optimizers['lars_apex'] = (None, lambda pxx: apex.parallel.LARC.LARC(apex.optimizers.FusedSGD(pxx, 0.01, 0.9)), bnb.optim.Adam)
|
|
|
|
str2optimizers['adam'] = (torch.optim.Adam, bnb.optim.Adam)
|
|
str2optimizers['adamw'] = (torch.optim.AdamW, bnb.optim.AdamW)
|
|
str2optimizers['fused_adam'] = (apex.optimizers.FusedAdam, bnb.optim.Adam)
|
|
str2optimizers['momentum'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD(pxx, 0.01, 0.9, block_wise=False))
|
|
str2optimizers['lars'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9), lambda pxx: bnb.optim.LARS(pxx, 0.01, 0.9))
|
|
str2optimizers['lamb'] = (lambda pxx: apex.optimizers.FusedLAMB(pxx, weight_decay=0.0, max_grad_norm=10000.0, eps=1e-8, use_nvlamb=True), bnb.optim.LAMB)
|
|
str2optimizers['rmsprop'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop(pxx, 0.01, 0.9, block_wise=False))
|
|
str2optimizers['adagrad'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad(pxx, 0.01, block_wise=False))
|
|
str2optimizers['adam8bit'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=False))
|
|
str2optimizers['momentum8bit'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=False))
|
|
str2optimizers['rmsprop8bit'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=False))
|
|
str2optimizers['lamb8bit'] = (lambda pxx: apex.optimizers.FusedLAMB(pxx, weight_decay=0.0, max_grad_norm=10000.0, eps=1e-8, use_nvlamb=True), bnb.optim.LAMB8bit)
|
|
str2optimizers['lars8bit'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9), lambda pxx: bnb.optim.LARS8bit(pxx, 0.01, 0.9))
|
|
|
|
str2optimizers['adam8bit_blockwise'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=True))
|
|
str2optimizers['adamw8bit_blockwise'] = (torch.optim.Adam, lambda pxx: bnb.optim.AdamW8bit(pxx, block_wise=True))
|
|
str2optimizers['momentum8bit_blockwise'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=True))
|
|
str2optimizers['rmsprop8bit_blockwise'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=True))
|
|
str2optimizers['adagrad8bit_blockwise'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad8bit(pxx, 0.01, block_wise=True))
|
|
|
|
str2statenames = {}
|
|
str2statenames['adam'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')]
|
|
str2statenames['adamw'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')]
|
|
str2statenames['momentum'] = [('momentum_buffer', 'state1')]
|
|
str2statenames['lars'] = [('momentum_buffer', 'state1')]
|
|
str2statenames['lamb'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')]
|
|
str2statenames['rmsprop'] = [('square_avg', 'state1')]
|
|
str2statenames['adagrad'] = [('sum', 'state1')]
|
|
str2statenames['adam8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')]
|
|
str2statenames['lamb8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')]
|
|
str2statenames['adam8bit_blockwise'] = [('exp_avg', 'state1', 'qmap1', 'absmax1'), ('exp_avg_sq', 'state2', 'qmap2', 'absmax2')]
|
|
str2statenames['adamw8bit_blockwise'] = [('exp_avg', 'state1', 'qmap1', 'absmax1'), ('exp_avg_sq', 'state2', 'qmap2', 'absmax2')]
|
|
str2statenames['momentum8bit'] = [('momentum_buffer', 'state1', 'qmap1', 'max1')]
|
|
str2statenames['momentum8bit_blockwise'] = [('momentum_buffer', 'state1', 'qmap1', 'absmax1')]
|
|
str2statenames['lars8bit'] = [('momentum_buffer', 'state1', 'qmap1', 'max1')]
|
|
str2statenames['rmsprop8bit'] = [('square_avg', 'state1', 'qmap1', 'max1')]
|
|
str2statenames['rmsprop8bit_blockwise'] = [('square_avg', 'state1', 'qmap1', 'absmax1')]
|
|
str2statenames['adagrad8bit_blockwise'] = [('sum', 'state1', 'qmap1', 'absmax1')]
|
|
|
|
dim1 = [1024]
|
|
dim2 = [32, 1024, 4097, 1]
|
|
gtype = [torch.float32, torch.float16]
|
|
optimizer_names = ['adam', 'adamw', 'momentum', 'rmsprop', 'lars', 'lamb', 'adagrad']
|
|
values = list(product(dim1,dim2, gtype, optimizer_names))
|
|
names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
|
|
def test_optimizer32bit(dim1, dim2, gtype, optim_name):
|
|
if dim1 == 1 and dim2 == 1: return
|
|
p1 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1
|
|
p2 = p1.clone()
|
|
p1 = p1.float()
|
|
|
|
|
|
torch_optimizer = str2optimizers[optim_name][0]([p1])
|
|
bnb_optimizer = str2optimizers[optim_name][1]([p2])
|
|
|
|
if gtype == torch.float32:
|
|
atol, rtol = 2e-6, 1e-5
|
|
else:
|
|
atol, rtol = 1e-4, 1e-3
|
|
|
|
|
|
for i in range(50):
|
|
g = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.01
|
|
p1.grad = g.clone().float()
|
|
p2.grad = g.clone()
|
|
|
|
bnb_optimizer.step()
|
|
torch_optimizer.step()
|
|
|
|
for name1, name2 in str2statenames[optim_name]:
|
|
torch.testing.assert_allclose(torch_optimizer.state[p1][name1], bnb_optimizer.state[p2][name2], atol=atol, rtol=rtol)
|
|
|
|
torch.testing.assert_allclose(p1, p2.float(), atol=atol, rtol=rtol)
|
|
|
|
if i % 10 == 0 and i > 0:
|
|
path = get_temp_dir()
|
|
torch.save(bnb_optimizer.state_dict(),join(path, 'opt.pt'))
|
|
del bnb_optimizer
|
|
bnb_optimizer = None
|
|
bnb_optimizer = str2optimizers[optim_name][1]([p2])
|
|
bnb_optimizer.load_state_dict(torch.load(join(path, 'opt.pt')))
|
|
rm_path(path)
|
|
torch.testing.assert_allclose(p1, p2.float(), atol=atol, rtol=rtol)
|
|
for name1, name2 in str2statenames[optim_name]:
|
|
torch.testing.assert_allclose(torch_optimizer.state[p1][name1], bnb_optimizer.state[p2][name2], atol=atol, rtol=rtol)
|
|
|
|
if gtype == torch.float16:
|
|
# the adam buffers should also be close because they are 32-bit
|
|
# but the paramters can diverge because they are 16-bit
|
|
# the difference grow larger and larger with each update
|
|
# --> copy the state to keep weights close
|
|
p1.data = p1.data.half().float()
|
|
p2.copy_(p1.data)
|
|
torch.testing.assert_allclose(p1.half(), p2)
|
|
if optim_name in ['lars', 'lamb']:
|
|
assert bnb_optimizer.state[p2]['unorm_vec'] > 0.0
|
|
|
|
dim1 = [1024]
|
|
dim2 = [32, 1024, 4097]
|
|
gtype = [torch.float32, torch.float16]
|
|
values = list(product(dim1,dim2, gtype))
|
|
names = ['dim1_{0}_dim2_{1}_gtype_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, gtype", values, ids=names)
|
|
def test_global_config(dim1, dim2, gtype):
|
|
if dim1 == 1 and dim2 == 1: return
|
|
p1 = torch.randn(dim1,dim2, device='cpu', dtype=gtype)*0.1
|
|
p2 = torch.randn(dim1,dim2, device='cpu', dtype=gtype)*0.1
|
|
p3 = torch.randn(dim1,dim2, device='cpu', dtype=gtype)*0.1
|
|
mask = torch.rand_like(p2) < 0.1
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
lr = 0.001
|
|
eps = 1e-8
|
|
|
|
bnb.optim.GlobalOptimManager.get_instance().initialize()
|
|
bnb.optim.GlobalOptimManager.get_instance().override_config(p2, 'skip_zeros', True)
|
|
bnb.optim.GlobalOptimManager.get_instance().override_config(p3, 'optim_bits', 8)
|
|
|
|
bnb.optim.GlobalOptimManager.get_instance().register_parameters([p1, p2, p3])
|
|
p1 = p1.cuda()
|
|
p2 = p2.cuda()
|
|
p3 = p3.cuda()
|
|
|
|
adam2 = bnb.optim.Adam([p1, p2, p3], lr, (beta1, beta2), eps)
|
|
|
|
if gtype == torch.float32:
|
|
atol, rtol = 1e-6, 1e-5
|
|
else:
|
|
atol, rtol = 1e-4, 1e-3
|
|
|
|
original_p2 = p2[mask].clone()
|
|
|
|
for i in range(50):
|
|
g1 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1 + 0.001
|
|
g2 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1 + 0.001
|
|
g3 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1 + 0.001
|
|
p1.grad = g1
|
|
p2.grad = g2
|
|
p3.grad = g3
|
|
|
|
if i > 30 and i % 10 == 0:
|
|
g1.data[mask] = 0.0
|
|
g2.data[mask] = 0.0
|
|
p1.grad = g1
|
|
p2.grad = g2
|
|
original_p1 = p1[mask].clone()
|
|
original_p2 = p2[mask].clone()
|
|
og_s1 = adam2.state[p2]['state1'][mask].clone()
|
|
og_s2 = adam2.state[p2]['state2'][mask].clone()
|
|
og_s11 = adam2.state[p1]['state1'][mask].clone()
|
|
og_s21 = adam2.state[p1]['state2'][mask].clone()
|
|
|
|
adam2.step()
|
|
|
|
assert adam2.state[p3]['state1'].dtype == torch.uint8
|
|
assert adam2.state[p3]['state2'].dtype == torch.uint8
|
|
|
|
if i > 30 and i % 10 == 0:
|
|
torch.testing.assert_allclose(original_p2, p2[mask])
|
|
torch.testing.assert_allclose(adam2.state[p2]['state1'][mask], og_s1)
|
|
torch.testing.assert_allclose(adam2.state[p2]['state2'][mask], og_s2)
|
|
assert ((p1[mask]- original_p1)==0.0).sum() < p1.numel()
|
|
assert ((adam2.state[p1]['state1'][mask]- og_s11)==0.0).sum() == 0.0
|
|
assert ((adam2.state[p1]['state2'][mask]- og_s21)==0.0).sum() == 0.0
|
|
|
|
|
|
|
|
|
|
dim1 = [1024]
|
|
dim2 = [32, 1024, 4097]
|
|
gtype = [torch.float32, torch.float16]
|
|
optimizer_names = ['adam8bit', 'momentum8bit', 'rmsprop8bit', 'adam8bit_blockwise', 'adamw8bit_blockwise', 'lamb8bit', 'lars8bit', 'momentum8bit_blockwise', 'rmsprop8bit_blockwise', 'adagrad8bit_blockwise']
|
|
values = list(product(dim1,dim2, gtype, optimizer_names))
|
|
names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
|
|
def test_optimizer8bit(dim1, dim2, gtype, optim_name):
|
|
if dim1 == 1 and dim2 == 1: return
|
|
p1 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1
|
|
p2 = p1.clone()
|
|
p1 = p1.float()
|
|
blocksize = 2048
|
|
|
|
torch_optimizer = str2optimizers[optim_name][0]([p1])
|
|
bnb_optimizer = str2optimizers[optim_name][1]([p2])
|
|
|
|
if gtype == torch.float32:
|
|
atol, rtol = 3e-3, 1e-3
|
|
patol, prtol = 1e-5, 1e-3
|
|
|
|
else:
|
|
atol, rtol = 3e-3, 1e-3
|
|
patol, prtol = 1e-5, 1e-3
|
|
|
|
errors = []
|
|
relerrors = []
|
|
|
|
for i in range(50):
|
|
g = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.01
|
|
p1.grad = g.clone().float()
|
|
p2.grad = g.clone()
|
|
|
|
bnb_optimizer.step()
|
|
torch_optimizer.step()
|
|
|
|
torch.testing.assert_allclose(p1, p2.float(), atol=patol, rtol=prtol)
|
|
|
|
dequant_states = []
|
|
for name1, name2, qmap, max_val in str2statenames[optim_name]:
|
|
#print(bnb_optimizer.state[p2][max_val], name1)
|
|
if 'blockwise' in optim_name:
|
|
s1 = F.dequantize_blockwise(code=bnb_optimizer.state[p2][qmap], absmax=bnb_optimizer.state[p2][max_val], A=bnb_optimizer.state[p2][name2], blocksize=blocksize)
|
|
else:
|
|
s1 = F.dequantize(code=bnb_optimizer.state[p2][qmap], absmax=bnb_optimizer.state[p2][max_val], A=bnb_optimizer.state[p2][name2])
|
|
num_not_close = torch.isclose(torch_optimizer.state[p1][name1], s1, atol=atol, rtol=rtol)==0
|
|
assert num_not_close.sum().item() < 20
|
|
dequant_states.append(s1.clone())
|
|
|
|
err = torch.abs(p1-p2)
|
|
relerr = err/torch.abs(p1)
|
|
assert err.mean() < 0.0001
|
|
assert relerr.mean() < 0.001
|
|
|
|
errors.append(err.mean().item())
|
|
relerrors.append(relerr.mean().item())
|
|
|
|
if i % 10 == 0 and i > 0:
|
|
for (name1, name2, qmap, max_val), s in zip(str2statenames[optim_name], dequant_states):
|
|
s1cpy = s.clone()
|
|
raws1cpy = bnb_optimizer.state[p2][name2].clone()
|
|
qmap1 = bnb_optimizer.state[p2][qmap].clone()
|
|
|
|
path = get_temp_dir()
|
|
torch.save(bnb_optimizer.state_dict(),join(path, 'opt.pt'))
|
|
del bnb_optimizer
|
|
bnb_optimizer = None
|
|
bnb_optimizer = str2optimizers[optim_name][1]([p2])
|
|
bnb_optimizer.load_state_dict(torch.load(join(path, 'opt.pt')))
|
|
rm_path(path)
|
|
torch.testing.assert_allclose(raws1cpy, bnb_optimizer.state[p2][name2])
|
|
torch.testing.assert_allclose(qmap1, bnb_optimizer.state[p2][qmap])
|
|
|
|
if 'blockwise' in optim_name:
|
|
s1 = F.dequantize_blockwise(code=bnb_optimizer.state[p2][qmap], absmax=bnb_optimizer.state[p2][max_val], A=bnb_optimizer.state[p2][name2], blocksize=blocksize)
|
|
else:
|
|
s1 = F.dequantize(code=bnb_optimizer.state[p2][qmap], absmax=bnb_optimizer.state[p2][max_val], A=bnb_optimizer.state[p2][name2])
|
|
torch.testing.assert_allclose(s1cpy, s1)
|
|
|
|
num_not_close = torch.isclose(torch_optimizer.state[p1][name1], s1, atol=atol, rtol=rtol)==0
|
|
assert num_not_close.sum().item() < 20
|
|
torch.testing.assert_allclose(p1, p2.float(), atol=patol, rtol=prtol)
|
|
|
|
# the parameters diverge quickly. Here we keep them close
|
|
# together so we can test against the Adam error
|
|
p1.data = p1.data.to(gtype).float()
|
|
p2.copy_(p1.data)
|
|
torch.testing.assert_allclose(p1.to(gtype), p2)
|
|
for (name1, name2, qmap, max_val), s in zip(str2statenames[optim_name], dequant_states):
|
|
torch_optimizer.state[p1][name1].copy_(s.data)
|
|
|
|
#print(sum(errors)/len(errors))
|
|
#print(sum(relerrors)/len(relerrors))
|
|
|
|
|
|
|
|
dim1 = [1024]
|
|
dim2 = [32, 1024, 4097]
|
|
gtype = [torch.float32]
|
|
optim_bits = [32, 8]
|
|
values = list(product(dim1,dim2, gtype, optim_bits))
|
|
names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_bits_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, gtype, optim_bits", values, ids=names)
|
|
def test_adam_percentile_clipping(dim1, dim2, gtype, optim_bits):
|
|
if dim1 == 1 and dim2 == 1: return
|
|
p1 = torch.randn(dim1,dim2, device='cpu', dtype=gtype)*0.1
|
|
beta1 = 0.9
|
|
beta2 = 0.999
|
|
lr = 0.001
|
|
eps = 1e-8
|
|
p1 = p1.cuda()
|
|
p2 = p1.clone()
|
|
adam1 = bnb.optim.Adam([p1], lr, (beta1, beta2), eps, optim_bits=optim_bits)
|
|
adam2 = bnb.optim.Adam([p2], lr, (beta1, beta2), eps, optim_bits=optim_bits, percentile_clipping=5)
|
|
|
|
gnorm_vec = torch.zeros(100).cuda()
|
|
step = 0
|
|
|
|
for i in range(50):
|
|
step += 1
|
|
g1 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1 + (0.01*i)
|
|
g2 = g1.clone()
|
|
p2.grad = g2
|
|
|
|
current_gnorm, clip_val, gnorm_scale = F.percentile_clipping(g1, gnorm_vec, step, 5)
|
|
g1 = (g1.float()*gnorm_scale).to(gtype)
|
|
p1.grad = g1
|
|
|
|
adam1.step()
|
|
adam2.step()
|
|
|
|
# gnorm_scale is not deterministic (warp reductions), as such there can be slight differences in state
|
|
if optim_bits == 32:
|
|
torch.testing.assert_allclose(p1, p2)
|
|
torch.testing.assert_allclose(adam1.state[p1]['state1'], adam2.state[p2]['state1'], atol=5e-5, rtol=1e-4)
|
|
torch.testing.assert_allclose(adam1.state[p1]['state2'], adam2.state[p2]['state2'], atol=5e-5, rtol=1e-4)
|
|
elif optim_bits == 8:
|
|
torch.testing.assert_allclose(p1, p2, atol=1e-4, rtol=1e-3)
|
|
torch.testing.assert_allclose(adam1.state[p1]['state1'], adam2.state[p2]['state1'], atol=2, rtol=1e-3)
|
|
torch.testing.assert_allclose(adam1.state[p1]['state2'], adam2.state[p2]['state2'], atol=2, rtol=1e-3)
|
|
adam1.state[p1]['state1'].copy_(adam2.state[p2]['state1'])
|
|
adam1.state[p1]['state2'].copy_(adam2.state[p2]['state2'])
|
|
if i % 10 == 0 and i > 0:
|
|
path = get_temp_dir()
|
|
torch.save(adam2.state_dict(),join(path, 'opt.pt'))
|
|
del adam2
|
|
adam2 = None
|
|
adam2 = bnb.optim.Adam([p2], lr, (beta1, beta2), eps, optim_bits=optim_bits, percentile_clipping=5)
|
|
adam2.load_state_dict(torch.load(join(path, 'opt.pt')))
|
|
|
|
|
|
|
|
|
|
dim1 = [4096]
|
|
dim2 = [4096]
|
|
gtype = [torch.float32, torch.float16]
|
|
#optimizer_names = ['adam8bit_blockwise', 'adam8bit', 'lamb8bit']
|
|
#optimizer_names = ['adam8bit_blockwise', 'adam_apex', 'adam8bit', 'adam', 'adam_pytorch']
|
|
#optimizer_names = ['momentum_apex', 'momentum8bit', 'momentum_pytorch']
|
|
#optimizer_names = ['lamb_apex', 'lamb8bit']
|
|
#optimizer_names = ['lars_apex', 'lars8bit']
|
|
optimizer_names = ['adam8bit_blockwise']
|
|
values = list(product(dim1,dim2, gtype, optimizer_names))
|
|
names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
|
|
def test_benchmark_blockwise(dim1, dim2, gtype, optim_name):
|
|
if dim1 == 1 and dim2 == 1: return
|
|
p1 = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.1
|
|
|
|
|
|
bnb_optimizer = str2optimizers[optim_name][1]([p1])
|
|
|
|
g = torch.randn(dim1,dim2, device='cuda', dtype=gtype)*0.01
|
|
p1.grad = g
|
|
for i in range(5000):
|
|
if i == 500:
|
|
# 100 iterations for burn-in
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
|
|
bnb_optimizer.step()
|
|
|
|
torch.cuda.synchronize()
|
|
s = time.time()-t0
|
|
print('')
|
|
params = 4500*4096*4096
|
|
print(optim_name, gtype, s/params)
|
|
#assert s < 3.9
|
|
|
|
|
|
|
|
def test_str_betas():
|
|
betas = (0.80, 0.95)
|
|
strbetas = '(0.80, 0.95)'
|
|
|
|
layer = torch.nn.Linear(10, 10)
|
|
|
|
base = bnb.optim.Adam(layer.parameters(), betas=betas)
|
|
strbase = bnb.optim.Adam(layer.parameters(), betas=strbetas)
|
|
assert base.defaults['betas'][0] == 0.8
|
|
assert base.defaults['betas'][1] == 0.95
|
|
assert strbase.defaults['betas'][0] == 0.8
|
|
assert strbase.defaults['betas'][1] == 0.95
|
|
|
|
|