mirror of
https://github.com/saymrwulf/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
97 lines
3.3 KiB
Python
97 lines
3.3 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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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 __future__ import unicode_literals
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import numpy as np
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import unittest
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from caffe2.python import core, workspace, tt_core
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import caffe2.python.hypothesis_test_util as hu
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class TestTTSVD(hu.HypothesisTestCase):
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def test_full_tt_svd(self):
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size = 256
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np.random.seed(1234)
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X = np.expand_dims(
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np.random.rand(size).astype(np.float32), axis=0)
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W = np.random.rand(size, size).astype(np.float32)
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b = np.zeros(size).astype(np.float32)
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inp_sizes = [4, 4, 4, 4]
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out_sizes = [4, 4, 4, 4]
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op_fc = core.CreateOperator(
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"FC",
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["X", "W", "b"],
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["Y"],
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("W", W)
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workspace.FeedBlob("b", b)
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workspace.RunOperatorOnce(op_fc)
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Y_fc = workspace.FetchBlob("Y").flatten()
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# Testing TT-decomposition with high ranks
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full_tt_ranks = [1, 16, 256, 16, 1]
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full_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
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full_tt_ranks)
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full_op_tt = core.CreateOperator(
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"TT",
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["X", "b", "cores"],
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["Y"],
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inp_sizes=inp_sizes,
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out_sizes=out_sizes,
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tt_ranks=full_tt_ranks,
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("cores", full_cores)
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workspace.RunOperatorOnce(full_op_tt)
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Y_full_tt = workspace.FetchBlob("Y").flatten()
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assert(len(Y_fc) == len(Y_full_tt))
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self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_full_tt), 0, delta=1e-3)
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# Testing TT-decomposition with minimal ranks
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sparse_tt_ranks = [1, 1, 1, 1, 1]
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sparse_cores = tt_core.matrix_to_tt(W, inp_sizes, out_sizes,
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sparse_tt_ranks)
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sparse_op_tt = core.CreateOperator(
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"TT",
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["X", "b", "cores"],
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["Y"],
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inp_sizes=inp_sizes,
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out_sizes=out_sizes,
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tt_ranks=sparse_tt_ranks,
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)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("b", b)
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workspace.FeedBlob("cores", sparse_cores)
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workspace.RunOperatorOnce(sparse_op_tt)
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Y_sparse_tt = workspace.FetchBlob("Y").flatten()
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assert(len(Y_fc) == len(Y_sparse_tt))
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self.assertAlmostEquals(np.linalg.norm(Y_fc - Y_sparse_tt),
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39.974, delta=1e-3)
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if __name__ == '__main__':
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unittest.main()
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