mirror of
https://github.com/zebrajr/pytorch.git
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d4336edb05
Summary: According to GitHub issue #1168, YellowFin's accuracy between Caffe2 and Numpy models from tests are not good enough in some environments. Results were very close on my machine. GitHub's Travis failed on some tests which I later disabled. Therefore the difference doesn't come from logical differences but from loss of precision on some machines. It is safe to disable equivalency test if equivalency was already once tested. Reviewed By: akyrola Differential Revision: D5777049 fbshipit-source-id: c249a205d94b52c3928c37481f15227d500aafd0
546 lines
22 KiB
Python
546 lines
22 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from caffe2.proto import caffe2_pb2
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import caffe2.python.optimizer as optimizer
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from caffe2.python.optimizer import (
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build_sgd, build_multi_precision_sgd, build_ftrl, build_adagrad,
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build_adam, build_yellowfin, add_weight_decay, SgdOptimizer)
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from caffe2.python.optimizer_context import UseOptimizer
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from caffe2.python.optimizer_test_util import (
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OptimizerTestBase, LRModificationTestBase
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)
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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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import numpy as np
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from numpy.testing import assert_allclose, assert_equal
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import math
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import unittest
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class TestSgd(OptimizerTestBase, LRModificationTestBase, TestCase):
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def build_optimizer(self, model, **kwargs):
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self._skip_gpu = False
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return build_sgd(model, base_learning_rate=0.1, **kwargs)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertFalse(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().shared:
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tensor = workspace.FetchBlob(param)
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np.testing.assert_allclose(np.array([1.0]), tensor, atol=1e-5)
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class TestMultiPrecisionSgd(
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OptimizerTestBase, LRModificationTestBase, TestCase
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):
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def build_optimizer(self, model, **kwargs):
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self._skip_gpu = False
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return build_multi_precision_sgd(
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model, base_learning_rate=0.1, **kwargs
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)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertFalse(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().shared:
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tensor = workspace.FetchBlob(param)
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np.testing.assert_allclose(np.array([1.0]), tensor, atol=1e-5)
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@unittest.skipIf(not workspace.has_gpu_support, "No GPU support")
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def testGPUDense(self):
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super(TestMultiPrecisionSgd, self).testGPUDense(core.DataType.FLOAT16)
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class TestFtrl(OptimizerTestBase, TestCase):
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def build_optimizer(self, model, **kwargs):
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self._skip_gpu = True
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return build_ftrl(
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model,
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engine=None,
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alpha=1.0,
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beta=0.1,
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lambda1=0.0,
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lambda2=0.0,
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**kwargs
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)
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def check_optimizer(self, optimizer):
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self.assertFalse(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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class TestAdagrad(OptimizerTestBase, LRModificationTestBase, TestCase):
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def build_optimizer(self, model, **kwargs):
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self._skip_gpu = False
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return build_adagrad(model, base_learning_rate=1.0, **kwargs)
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def check_optimizer(self, optimizer):
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self.assertFalse(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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class TestAdam(OptimizerTestBase, LRModificationTestBase, TestCase):
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def build_optimizer(self, model, **kwargs):
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self._skip_gpu = False
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return build_adam(model, base_learning_rate=0.1, **kwargs)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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self.assertTrue(workspace.HasBlob("optimizer_iteration"))
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iteration_tensor = workspace.FetchBlob("optimizer_iteration")
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np.testing.assert_allclose(np.array([2000]),
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iteration_tensor,
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atol=1e-5)
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for param in optimizer.get_auxiliary_parameters().shared:
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workspace.FetchBlob(param)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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class TestYellowFin(OptimizerTestBase, TestCase):
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# YellowFin: An automatic tuner for momentum SGD
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# (https://arxiv.org/abs/1706.03471)
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_yellowfin(model, base_learning_rate=0.1)
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def check_optimizer(self, optimizer):
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self.assertTrue(optimizer.get_auxiliary_parameters().shared)
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self.assertTrue(optimizer.get_auxiliary_parameters().local)
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self.assertTrue(workspace.HasBlob("optimizer_iteration"))
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iteration_tensor = workspace.FetchBlob("optimizer_iteration")
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np.testing.assert_allclose(np.array([2000]),
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iteration_tensor,
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atol=1e-5)
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for param in optimizer.get_auxiliary_parameters().shared:
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workspace.FetchBlob(param)
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for param in optimizer.get_auxiliary_parameters().local:
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workspace.FetchBlob(param)
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def testSparse(self):
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raise unittest.SkipTest("no sparse support")
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def deb(self, val, beta, i, zero_debias):
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if zero_debias:
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return val / (1.0 - beta ** i)
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else:
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return val
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def get_lr_mu(self, distance, grad_var, h_min, h_max):
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# First tune based on dynamic range
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if grad_var == 0:
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dr = h_max / h_min
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mu = ((np.sqrt(dr) - 1) / (np.sqrt(dr) + 1)) ** 2
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lr_min = (1 + np.sqrt(mu)) ** 2 / h_max
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return lr_min, mu
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p = distance ** 2 * h_min ** 2 / 2 / grad_var
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w3 = (-math.sqrt(p * p + 4.0 / 27.0 * p * p * p) - p) / 2.0
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w = (1.0 if w3 > 0.0 else -1.0) * math.pow(math.fabs(w3), 1.0 / 3.0)
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y = w - p / 3.0 / w
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root = y + 1
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root = min(root, 1.0 - 1e-6)
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dr = h_max / h_min
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mu = max(((np.sqrt(dr) - 1) / (np.sqrt(dr) + 1)) ** 2, root**2)
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lr_min = (1 - np.sqrt(mu)) ** 2 / h_min
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return lr_min, mu
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def caffe2_yellowfin(self, zero_debias, grad_coef, n_dim, n_iter, gpu):
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caffe2_res = {}
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alpha = 1.0
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mu = 0.0
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beta = 0.999
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curv_win_width = 20
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epsilon = 1e-6
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net = core.Net("net")
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param_init_net = core.Net("param_init_net")
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workspace.ResetWorkspace()
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with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)):
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iteration = param_init_net.ConstantFill(
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[],
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"iteration",
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shape=[1],
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value=0,
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dtype=core.DataType.INT64)
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iter_mutex = param_init_net.CreateMutex([], ["iteration_mutex"])
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net.AtomicIter([iter_mutex, iteration], [iteration])
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pre_grad = param_init_net.ConstantFill(
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[],
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"pre_grad",
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shape=[n_dim],
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value=grad_coef
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)
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if gpu:
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iteration = net.CopyCPUToGPU(
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[iteration],
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"iteration_cpu"
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)
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iteration_float = net.Cast([iteration], "iteration_float")
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grad = net.Mul([pre_grad, iteration_float], "grad", broadcast=True)
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w = param_init_net.ConstantFill([], "w", shape=[n_dim], value=0.0)
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# a hack to create an object with __dict__
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param_info = lambda: None
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param_info.blob = w
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param_info.grad = grad
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optimizer.YellowFinOptimizer(
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alpha=alpha,
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mu=mu,
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beta=beta,
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curv_win_width=curv_win_width,
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epsilon=epsilon,
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zero_debias=zero_debias
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)._run(
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net,
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param_init_net,
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param_info
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)
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workspace.RunNetOnce(param_init_net)
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workspace.CreateNet(net, overwrite=True)
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for i in range(n_iter):
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workspace.RunNet(net)
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scalars_memory_blob = workspace.FetchBlob("w_scalars_memory")
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g_norm2_avg = scalars_memory_blob[1]
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g_norm2_min_avg = scalars_memory_blob[2]
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g_norm2_max_avg = scalars_memory_blob[3]
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distance_avg = scalars_memory_blob[4]
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g_avg_blob = workspace.FetchBlob("w_g_avg")
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res_lr = workspace.FetchBlob("w_lr_avg")[0]
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res_mu = workspace.FetchBlob("w_mu_avg")[0]
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g_deb = self.deb(g_avg_blob, beta, i + 1, zero_debias)
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variance = max(
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self.deb(g_norm2_avg, beta, i + 1, zero_debias) -
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g_deb.dot(g_deb),
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epsilon
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)
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if i > 0:
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caffe2_res[i] = {
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'h_max': np.exp(self.deb(g_norm2_max_avg,
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beta,
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i + 1,
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zero_debias)),
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'h_min': np.exp(self.deb(g_norm2_min_avg,
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beta,
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i + 1,
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zero_debias)),
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'var': variance,
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'dist': self.deb(distance_avg, beta, i + 1, zero_debias),
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'lr': res_lr,
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'mu': res_mu
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}
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return caffe2_res
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def numpy_yellowfin(self, zero_debias, grad_coef, n_dim, n_iter, gpu):
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numpy_res = {}
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target_h_max = 0.0
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target_h_min = 0.0
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target_g_norm_squared_avg = 0.0
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target_g_norm_avg = 0.0
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target_g_avg = 0.0
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target_dist_avg = 0.0
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target_lr = 1.0
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target_mu = 0.0
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for i in range(n_iter):
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grad_val = (i + 1) * grad_coef
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target_g_norm_squared_avg = 0.999 * target_g_norm_squared_avg + \
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0.001 * np.sum((grad_val * np.ones([n_dim, ])) ** 2)
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target_g_norm_avg = 0.999 * target_g_norm_avg + \
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0.001 * np.linalg.norm(grad_val * np.ones([n_dim, ]))
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target_g_avg = 0.999 * target_g_avg + 0.001 * grad_val
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target_h_max = 0.999 * target_h_max + \
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0.001 * np.log(grad_val ** 2 * n_dim)
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target_h_min = 0.999 * target_h_min + \
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0.001 * np.log((max(1, i + 2 - 20) * grad_coef) ** 2 * n_dim)
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if zero_debias:
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target_var = target_g_norm_squared_avg / \
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(1 - 0.999 ** (i + 1)) - \
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target_g_avg ** 2 * n_dim / (1 - 0.999 ** (i + 1)) ** 2
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else:
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target_var = target_g_norm_squared_avg - \
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target_g_avg ** 2 * n_dim
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target_dist_avg = 0.999 * target_dist_avg + \
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0.001 * target_g_norm_avg / target_g_norm_squared_avg
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if i > 0:
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if zero_debias:
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lr, mu = self.get_lr_mu(
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target_dist_avg / (1.0 - 0.999 ** (i + 1)),
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target_var,
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np.exp(target_h_min / (1.0 - 0.999 ** (i + 1))),
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np.exp(target_h_max / (1.0 - 0.999 ** (i + 1))))
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target_lr = 0.999 * target_lr + 0.001 * lr
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target_mu = 0.999 * target_mu + 0.001 * mu
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numpy_res[i] = {
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'h_max': np.exp(target_h_max / (1 - 0.999 ** (i + 1))),
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'h_min': np.exp(target_h_min / (1 - 0.999 ** (i + 1))),
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'var': target_var,
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'dist': target_dist_avg / (1 - 0.999 ** (i + 1)),
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'lr': target_lr,
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'mu': target_mu
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}
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else:
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lr, mu = self.get_lr_mu(
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target_dist_avg,
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target_var,
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np.exp(target_h_min),
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np.exp(target_h_max))
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target_lr = 0.999 * target_lr + 0.001 * lr
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target_mu = 0.999 * target_mu + 0.001 * mu
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numpy_res[i] = {
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'h_max': np.exp(target_h_max),
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'h_min': np.exp(target_h_min),
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'var': target_var,
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'dist': target_dist_avg,
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'lr': target_lr,
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'mu': target_mu
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}
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return numpy_res
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def compare_yellowfin_models(self,
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model0,
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model1,
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zero_debias,
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grad_coef,
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n_dim,
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n_iter,
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gpu):
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model0_res = model0(zero_debias, grad_coef, n_dim, n_iter, gpu)
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model1_res = model1(zero_debias, grad_coef, n_dim, n_iter, gpu)
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assert_equal(len(model0_res), len(model1_res))
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for i in range(1, len(model0_res)):
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assert_equal(model0_res[i].keys(), model1_res[i].keys())
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for feat in model0_res[i].keys():
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err_msg = \
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'i=' + str(i) + ',\n' + \
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'feat=' + feat + ',\n' + \
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'grad_coef=' + str(grad_coef) + ',\n' + \
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'zero_debias=' + str(zero_debias)
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assert_allclose(model0_res[i][feat],
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model1_res[i][feat],
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rtol=1e-2,
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err_msg=err_msg)
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@unittest.skip("Results might vary too much. Only for individual use.")
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def test_caffe2_cpu_vs_numpy(self):
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n_dim = 1000000
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n_iter = 50
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cpu_device_opt = core.DeviceOption(caffe2_pb2.CPU)
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with core.DeviceScope(cpu_device_opt):
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for zero_debias, grad_coef in [
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(False, 1.0),
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(False, 0.1),
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(False, 0.01),
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(True, 1.0)
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]:
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self.compare_yellowfin_models(
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self.caffe2_yellowfin,
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self.numpy_yellowfin,
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zero_debias,
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grad_coef,
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n_dim,
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n_iter,
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gpu=False
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)
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@unittest.skip("Results might vary too much. Only for individual use.")
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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def test_caffe2_gpu_vs_numpy(self):
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n_dim = 1000000
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n_iter = 50
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gpu_device_opt = core.DeviceOption(caffe2_pb2.CUDA, 0)
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with core.DeviceScope(gpu_device_opt):
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for zero_debias in [False, True]:
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for grad_coef in [1.0, 0.1, 0.01]:
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self.compare_yellowfin_models(
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self.caffe2_yellowfin,
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self.numpy_yellowfin,
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zero_debias,
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grad_coef,
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n_dim,
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n_iter,
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gpu=True
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)
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class TestMultiOptimizers(TestCase):
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def test_multiple_optimizers(self):
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from caffe2.python import brew, core, optimizer
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from caffe2.python.model_helper import ModelHelper
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model = ModelHelper(name="test")
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fc1 = brew.fc(model, 'data', 'fc1', 100, 50)
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fc2 = brew.fc(model, fc1, 'fc2', 50, 25)
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pred = brew.fc(model, fc2, 'fc3', 25, 10)
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(softmax, loss) = model.SoftmaxWithLoss(
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[pred, 'label'],
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['softmax', 'loss'],
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)
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model.AddGradientOperators([loss])
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param_to_device = optimizer._get_param_to_device(model)
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def infer_blob_device(blob_name):
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return optimizer.get_param_device(
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blob_name, "{}_grad".format(blob_name), param_to_device
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)
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sgd_1 = optimizer.SgdOptimizer(base_learning_rate=0.1)
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sgd_2 = optimizer.SgdOptimizer(base_learning_rate=0.2)
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adagrad = optimizer.AdagradOptimizer()
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# Check same optimizer share the same learning rate.
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with core.DeviceScope(infer_blob_device("fc1_w")):
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sgd_1(model.net, model.param_init_net, "fc1_w", "fc1_w_grad")
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with core.DeviceScope(infer_blob_device("fc1_b")):
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sgd_1(model.net, model.param_init_net, "fc1_b", "fc1_b_grad")
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fc1_lr_blobs = []
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for op in model.net.Proto().op:
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if op.type == 'WeightedSum' and op.input[0] == 'fc1_w' or \
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op.input[0] == 'fc1_b':
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fc1_lr_blobs.append(op.input[3])
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self.assertEqual(fc1_lr_blobs[0], fc1_lr_blobs[1])
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# Check different instance of the same optimizer has a different lr.
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with core.DeviceScope(infer_blob_device("fc2_w")):
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sgd_2(model.net, model.param_init_net, "fc2_w", "fc2_w_grad")
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with core.DeviceScope(infer_blob_device("fc2_b")):
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sgd_2(model.net, model.param_init_net, "fc2_b", "fc2_b_grad")
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fc2_lr_blobs = []
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for op in model.net.Proto().op:
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if op.type == 'WeightedSum' and op.input[0] == 'fc2_w' or \
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op.input[0] == 'fc2_b':
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self.assertTrue(op.input[3] not in fc1_lr_blobs)
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fc2_lr_blobs.append(op.input[3])
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self.assertEqual(fc2_lr_blobs[0], fc2_lr_blobs[1])
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# Check different optimizer type case
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with core.DeviceScope(infer_blob_device("fc3_w")):
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adagrad(model.net, model.param_init_net, "fc3_w", "fc3_w_grad")
|
|
with core.DeviceScope(infer_blob_device("fc3_b")):
|
|
adagrad(model.net, model.param_init_net, "fc3_b", "fc3_b_grad")
|
|
fc3_lr_blobs = []
|
|
for op in model.net.Proto().op:
|
|
if op.type == 'Adagrad' and op.input[0] == 'fc3_w' or \
|
|
op.input[0] == 'fc3_b':
|
|
self.assertTrue(op.input[3] not in fc2_lr_blobs)
|
|
self.assertTrue(op.input[3] not in fc1_lr_blobs)
|
|
fc3_lr_blobs.append(op.input[3])
|
|
self.assertEqual(fc3_lr_blobs[0], fc3_lr_blobs[1])
|
|
|
|
|
|
class TestWeightDecay(TestCase):
|
|
|
|
def test_weight_decay(self):
|
|
from caffe2.python import brew
|
|
from caffe2.python.model_helper import ModelHelper
|
|
|
|
model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
|
|
cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
|
|
a = brew.fc(model, cnv, 'a', 100, 200)
|
|
pred = brew.fc(model, a, 'b', 200, 5)
|
|
(softmax, loss) = model.SoftmaxWithLoss(
|
|
[pred, 'label'],
|
|
['softmax', 'loss'],
|
|
)
|
|
model.AddGradientOperators([loss])
|
|
|
|
add_weight_decay(model, weight_decay=1e-4)
|
|
build_sgd(model, 0.11)
|
|
|
|
expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
|
|
|
|
# Check the proto that all weights are decayed and not non-weights
|
|
# are decayed.
|
|
for op in model.net.Proto().op:
|
|
if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
|
|
if op.output[0] not in expected_weight_grad:
|
|
print(
|
|
"Unexpected param for weight_decay: {}".
|
|
format(op.output[0])
|
|
)
|
|
self.assertTrue(op.output[0] in expected_weight_grad)
|
|
expected_weight_grad.remove(op.output[0])
|
|
|
|
self.assertEqual(
|
|
expected_weight_grad,
|
|
set(),
|
|
"Not all weights were decayed: {}".format(expected_weight_grad)
|
|
)
|
|
|
|
|
|
class TestOptimizerContext(TestCase):
|
|
|
|
def test_optimizer_context(self):
|
|
from caffe2.python import brew, optimizer
|
|
from caffe2.python.model_helper import ModelHelper
|
|
|
|
model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
|
|
count = optimizer._optimizer_instance_count['SgdOptimizer']
|
|
cnv_optim = SgdOptimizer(0.15)
|
|
weight_optim = SgdOptimizer(0.2)
|
|
bias_optim = SgdOptimizer(0.1)
|
|
|
|
with UseOptimizer(cnv_optim):
|
|
cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
|
|
with UseOptimizer({'WEIGHT': weight_optim, 'BIAS': bias_optim}):
|
|
a = brew.fc(model, cnv, 'a', 100, 200)
|
|
pred = brew.fc(model, a, 'b', 200, 5)
|
|
(softmax, loss) = model.SoftmaxWithLoss(
|
|
[pred, 'label'],
|
|
['softmax', 'loss'],
|
|
)
|
|
model.AddGradientOperators([loss])
|
|
|
|
add_weight_decay(model, weight_decay=1e-4)
|
|
# use the following optimizer if none specified in param_info
|
|
build_sgd(model, 0.11)
|
|
expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
|
|
expected_learning_rate = {
|
|
"SgdOptimizer_{}_lr_cpu".format(count): -0.15,
|
|
"SgdOptimizer_{}_lr_cpu".format(count + 1): -0.2,
|
|
"SgdOptimizer_{}_lr_cpu".format(count + 2): -0.1,
|
|
"SgdOptimizer_{}_lr_cpu".format(count + 3): -0.11
|
|
}
|
|
|
|
for op in model.net.Proto().op:
|
|
# Check the proto that all weights are decayed and not non-weights
|
|
# are decayed.
|
|
if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
|
|
if op.output[0] not in expected_weight_grad:
|
|
print(
|
|
"Unexpected param for weight_decay: {}".
|
|
format(op.output[0])
|
|
)
|
|
self.assertTrue(op.output[0] in expected_weight_grad)
|
|
expected_weight_grad.remove(op.output[0])
|
|
# Check the learning rate for each parameter
|
|
if op.type == 'LearningRate':
|
|
val = 0
|
|
for arg in op.arg:
|
|
if arg.name == 'base_lr':
|
|
val = arg.f
|
|
self.assertAlmostEqual(
|
|
val,
|
|
expected_learning_rate[op.output[0]]
|
|
)
|
|
|
|
self.assertEqual(
|
|
expected_weight_grad,
|
|
set(),
|
|
"Not all weights were decayed: {}".format(expected_weight_grad)
|
|
)
|