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https://github.com/saymrwulf/pytorch.git
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ee62a595fc
Summary: Closes https://github.com/caffe2/caffe2/pull/1357 Reviewed By: dzhulgakov Differential Revision: D6107036 Pulled By: bddppq fbshipit-source-id: 9278dae988c3c0656b4e4fd08bf7ca1e2eec3348
96 lines
3.8 KiB
Python
96 lines
3.8 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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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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from caffe2.python import core
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from caffe2.python.test_util import rand_array
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import caffe2.python.hypothesis_test_util as hu
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from hypothesis import given
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import hypothesis.strategies as st
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class TestScatterOps(hu.HypothesisTestCase):
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# TODO(dzhulgakov): add test cases for failure scenarios
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@given(num_args=st.integers(1, 5),
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first_dim=st.integers(1, 20),
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index_dim=st.integers(1, 10),
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extra_dims=st.lists(st.integers(1, 4), min_size=0, max_size=3),
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ind_type=st.sampled_from([np.int32, np.int64]),
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**hu.gcs)
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def testScatterWeightedSum(
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self, num_args, first_dim, index_dim, extra_dims, ind_type, gc, dc):
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ins = ['data', 'w0', 'indices']
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for i in range(1, num_args + 1):
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ins.extend(['x' + str(i), 'w' + str(i)])
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op = core.CreateOperator(
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'ScatterWeightedSum',
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ins,
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['data'],
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device_option=gc)
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def ref(d, w0, ind, *args):
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r = d.copy()
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for i in ind:
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r[i] *= w0
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for i in range(0, len(args), 2):
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x = args[i]
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w = args[i+1]
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for i, j in enumerate(ind):
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r[j] += w * x[i]
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return [r]
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d = rand_array(first_dim, *extra_dims)
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ind = np.random.randint(0, first_dim, index_dim).astype(ind_type)
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# ScatterWeightedSumOp only supports w0=1.0 in CUDAContext
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if(gc == hu.gpu_do):
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w0 = np.array(1.0).astype(np.float32)
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else:
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w0 = rand_array()
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inputs = [d, w0, ind]
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for _ in range(1, num_args + 1):
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x = rand_array(index_dim, *extra_dims)
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w = rand_array()
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inputs.extend([x,w])
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self.assertReferenceChecks(gc, op, inputs, ref, threshold=1e-3)
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@given(first_dim=st.integers(1, 20),
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index_dim=st.integers(1, 10),
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extra_dims=st.lists(st.integers(1, 4), min_size=0, max_size=3),
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data_type=st.sampled_from([np.float16, np.float32, np.int32, np.int64]),
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ind_type=st.sampled_from([np.int32, np.int64]),
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**hu.gcs_cpu_only)
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def testScatterAssign(
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self, first_dim, index_dim, extra_dims, data_type, ind_type, gc, dc):
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op = core.CreateOperator('ScatterAssign',
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['data', 'indices', 'slices'], ['data'])
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def ref(d, ind, x):
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r = d.copy()
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r[ind] = x
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return [r]
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# let's have indices unique
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if first_dim < index_dim:
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first_dim, index_dim = index_dim, first_dim
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d = (rand_array(first_dim, *extra_dims) * 10).astype(data_type)
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ind = np.random.choice(first_dim, index_dim,
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replace=False).astype(ind_type)
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x = (rand_array(index_dim, *extra_dims) * 10).astype(data_type)
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self.assertReferenceChecks(gc, op, [d, ind, x], ref, threshold=1e-3)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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