mirror of
https://github.com/zebrajr/pytorch.git
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13980d2bb5
Summary: Fix case when optimizer isn't called within a device scope context. Fix OptimizerContext lr blob names Reviewed By: volkhin Differential Revision: D5421046 fbshipit-source-id: 186a0d05f40d4442c5ba5736084626da73a0c0f1
255 lines
10 KiB
Python
255 lines
10 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.python.optimizer import (
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build_sgd, build_multi_precision_sgd, build_ftrl,
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build_adagrad, build_adam, 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 OptimizerTestBase
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from caffe2.python.test_util import TestCase
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from caffe2.python import workspace
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from caffe2.python.core import DataType
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import numpy as np
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import unittest
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class TestSgd(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_sgd(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.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(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_multi_precision_sgd(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.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(DataType.FLOAT16)
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class TestFtrl(OptimizerTestBase, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = True
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return build_ftrl(
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model, engine=None, alpha=1.0, beta=0.1, lambda1=0.0, lambda2=0.0)
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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, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_adagrad(model, base_learning_rate=1.0)
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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, TestCase):
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def build_optimizer(self, model):
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self._skip_gpu = False
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return build_adam(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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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")
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with core.DeviceScope(infer_blob_device("fc3_b")):
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adagrad(model.net, model.param_init_net, "fc3_b", "fc3_b_grad")
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fc3_lr_blobs = []
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for op in model.net.Proto().op:
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if op.type == 'Adagrad' and op.input[0] == 'fc3_w' or \
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op.input[0] == 'fc3_b':
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self.assertTrue(op.input[3] not in fc2_lr_blobs)
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self.assertTrue(op.input[3] not in fc1_lr_blobs)
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fc3_lr_blobs.append(op.input[3])
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self.assertEqual(fc3_lr_blobs[0], fc3_lr_blobs[1])
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class TestWeightDecay(TestCase):
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def test_weight_decay(self):
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from caffe2.python import brew
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from caffe2.python.model_helper import ModelHelper
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model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
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cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
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a = brew.fc(model, cnv, 'a', 100, 200)
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pred = brew.fc(model, a, 'b', 200, 5)
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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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add_weight_decay(model, weight_decay=1e-4)
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build_sgd(model, 0.11)
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expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
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# Check the proto that all weights are decayed and not non-weights
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# are decayed.
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for op in model.net.Proto().op:
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if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
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if op.output[0] not in expected_weight_grad:
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print(
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"Unexpected param for weight_decay: {}".
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format(op.output[0])
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)
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self.assertTrue(op.output[0] in expected_weight_grad)
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expected_weight_grad.remove(op.output[0])
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self.assertEqual(
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expected_weight_grad,
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set(),
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"Not all weights were decayed: {}".format(expected_weight_grad)
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)
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class TestOptimizerContext(TestCase):
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def test_optimizer_context(self):
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from caffe2.python import brew
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from caffe2.python.model_helper import ModelHelper
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model = ModelHelper(name="test", arg_scope={'order': 'NCHW'})
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cnv_optim = SgdOptimizer(0.15)
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weight_optim = SgdOptimizer(0.2)
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bias_optim = SgdOptimizer(0.1)
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with UseOptimizer(cnv_optim):
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cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
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with UseOptimizer({'WEIGHT': weight_optim, 'BIAS': bias_optim}):
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a = brew.fc(model, cnv, 'a', 100, 200)
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pred = brew.fc(model, a, 'b', 200, 5)
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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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add_weight_decay(model, weight_decay=1e-4)
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# use the following optimizer if none specified in param_info
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build_sgd(model, 0.11)
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expected_weight_grad = {'b_w_grad', 'a_w_grad', 'cnv_w_grad'}
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expected_learning_rate = {
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'SgdOptimizer_0_lr_cpu': -0.15, 'SgdOptimizer_1_lr_cpu': -0.2,
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'SgdOptimizer_2_lr_cpu': -0.1, 'SgdOptimizer_3_lr_cpu': -0.11
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}
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for op in model.net.Proto().op:
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# Check the proto that all weights are decayed and not non-weights
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# are decayed.
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if op.type == 'WeightedSum' and 'wd_0_0' in op.input:
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if op.output[0] not in expected_weight_grad:
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print(
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"Unexpected param for weight_decay: {}".
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format(op.output[0])
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)
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self.assertTrue(op.output[0] in expected_weight_grad)
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expected_weight_grad.remove(op.output[0])
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# Check the learning rate for each parameter
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if op.type == 'LearningRate':
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val = 0
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for arg in op.arg:
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if arg.name == 'base_lr':
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val = arg.f
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self.assertEqual(
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val,
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expected_learning_rate[op.output[0]]
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)
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self.assertEqual(
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expected_weight_grad,
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set(),
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"Not all weights were decayed: {}".format(expected_weight_grad)
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)
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