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