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https://github.com/zebrajr/pytorch.git
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cc3662e939
Summary: While there is currently support for scaling the base learning rate when loading the model, there is not support for scaling the base learning rate during training. This is needed for LATTE's seq2seq translation models, as the learning schedule is not predefined and is modified at runtime. Reviewed By: jhcross Differential Revision: D5701391 fbshipit-source-id: ae3bec45f238db1a2be7af9c04d720067e9095d5
270 lines
11 KiB
Python
270 lines
11 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 (
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OptimizerTestBase, LRModificationTestBase
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)
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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, 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(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 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, optimizer
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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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count = optimizer._optimizer_instance_count['SgdOptimizer']
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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_{}_lr_cpu".format(count): -0.15,
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"SgdOptimizer_{}_lr_cpu".format(count + 1): -0.2,
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"SgdOptimizer_{}_lr_cpu".format(count + 2): -0.1,
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"SgdOptimizer_{}_lr_cpu".format(count + 3): -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.assertAlmostEqual(
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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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