mirror of
https://github.com/zebrajr/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
272 lines
9.8 KiB
Python
272 lines
9.8 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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import unittest
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from caffe2.python import workspace, brew, model_helper
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from caffe2.python.modeling.gradient_clipping import GradientClipping
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import numpy as np
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class GradientClippingTest(unittest.TestCase):
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def test_gradient_clipping_by_norm(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l2_norm',
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clip_threshold=0.1,
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 2 * (3 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 17)
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def test_gradient_clipping_by_norm_l1_norm(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l1_norm',
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clip_threshold=0.1,
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 2 * (2 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 15)
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def test_gradient_clipping_by_norm_using_param_norm(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l2_norm',
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clip_threshold=0.1,
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use_parameter_norm=True,
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 2 * (5 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 21)
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def test_gradient_clipping_by_norm_compute_norm_ratio(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l2_norm',
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clip_threshold=0.1,
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use_parameter_norm=True,
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compute_norm_ratio=True,
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 2 * (6 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 23)
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def test_gradient_clipping_by_value(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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clip_max = 1e-8
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clip_min = 0
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net_modifier = GradientClipping(
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grad_clip_method='by_value',
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clip_max=clip_max,
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clip_min=clip_min,
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 2 * (1 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 13)
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fc1_w_grad = workspace.FetchBlob('fc1_w_grad')
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self.assertLessEqual(np.amax(fc1_w_grad), clip_max)
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self.assertGreaterEqual(np.amin(fc1_w_grad), clip_min)
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def test_gradient_clipping_by_norm_including_blobs(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l2_norm',
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clip_threshold=0.1,
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blobs_to_include=['fc1_w'],
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blobs_to_exclude=None
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 1 * (3 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 14)
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def test_gradient_clipping_by_norm_excluding_blobs(self):
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model = model_helper.ModelHelper(name="test")
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data = model.net.AddExternalInput("data")
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fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
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# no operator name set, will use default
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fc2 = brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
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sigm = model.net.Sigmoid(fc2, 'sigm')
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sq = model.net.SquaredL2Distance([sigm, 'label'], 'sq')
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loss = model.net.SumElements(sq, 'loss')
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grad_map = model.AddGradientOperators([loss])
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grad_map_for_param = {key: grad_map[key] for key in ['fc1_w', 'fc2_w']}
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net_modifier = GradientClipping(
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grad_clip_method='by_norm',
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clip_norm_type='l2_norm',
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clip_threshold=0.1,
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blobs_to_include=None,
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blobs_to_exclude=['fc1_w', 'fc2_w']
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)
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net_modifier(model.net, grad_map=grad_map_for_param)
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workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
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workspace.FeedBlob('label', np.random.rand(10, 1).astype(np.float32))
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workspace.RunNetOnce(model.param_init_net)
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workspace.RunNetOnce(model.net)
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# 5 forward ops + 6 backward ops + 0 * (3 gradient clipping ops)
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self.assertEqual(len(model.net.Proto().op), 11)
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