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0867120f1e
Summary: AT Reviewed By: enosair Differential Revision: D6525541 fbshipit-source-id: dc095af1c3485d029f7744aadb66f8c51acf8ffe
508 lines
17 KiB
Python
508 lines
17 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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from caffe2.python import core
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from functools import partial
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from hypothesis import given
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from caffe2.python import workspace
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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import unittest
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class TesterBase:
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def segment_reduce_op(self, data, segment_ids, reducer, indices=None):
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segments = self.split(data, segment_ids, indices)
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output = np.zeros((len(segments), ) + data.shape[1:])
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for i, segment in enumerate(segments):
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if len(segment) > 0:
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output[i] = reducer(segment)
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else:
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output[i] = 0.0
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return output
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def segment_reduce_grad_op(
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self,
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data,
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segment_ids,
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reducer_grad,
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grad_out,
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output,
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indices=None
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):
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segments = self.split(data, segment_ids, indices)
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segment_grads = [
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reducer_grad(grad_out[i], [output[i]], [segment])
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for i, segment in enumerate(segments)
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]
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return self.unsplit(data.shape[1:], segment_grads, segment_ids)
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def _test(self, prefix, input_strategy, refs, gpu=False, **kwargs):
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tester = self
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operator_args = kwargs.pop('operator_args', {})
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threshold = kwargs.pop('threshold', 1e-4)
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grad_check = kwargs.pop('grad_check', True)
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@given(X=input_strategy, **hu.gcs)
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def test_segment_ops(self, X, gc, dc):
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if not gpu and gc.device_type > 0:
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return
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for op_name, ref, grad_ref in refs:
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inputs = ['input%d' % i for i in range(0, len(X))]
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op = core.CreateOperator(
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prefix + op_name, inputs, ['output'], **operator_args
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)
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print('Operator %s, ' % op.type, gc.device_type)
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def seg_reduce(data, *args):
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indices, segments = (
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args if len(args) == 2 else (None, args[0])
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)
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out = tester.segment_reduce_op(
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data=data,
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segment_ids=segments,
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indices=indices,
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reducer=ref
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)
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return (out, )
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def seg_reduce_grad(grad_out, outputs, inputs):
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data = inputs[0]
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args = inputs[1:]
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indices, segments = (
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args if len(args) == 2 else (None, args[0])
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)
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# grad r.t. data
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grad_val = tester.segment_reduce_grad_op(
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data, segments, grad_ref, grad_out, outputs[0], indices
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)
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# if sparse, include indices along with data gradient
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data_grad_slice = (
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(grad_val, indices) if indices is not None else grad_val
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)
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# other inputs don't have gradient
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return (data_grad_slice, ) + (None, ) * (len(inputs) - 1)
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kwargs = {}
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if grad_check:
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kwargs['output_to_grad'] = 'output'
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kwargs['grad_reference'] = seg_reduce_grad
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=X,
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reference=seg_reduce,
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threshold=threshold,
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**kwargs
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)
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return test_segment_ops
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class SegmentsTester(TesterBase):
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def split(self, data, segment_ids, indices=None):
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"""
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Given:
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data[M1 x M2 x ... x Md]
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the input data
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indices[N] the index of each entry of segment_ids into data,
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where 0 <= index[i] < M1,
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with default indices=[0,1,...N]
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segment_ids[N] the segment_id for each entry of indices,
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returns K outputs, each one containing data entries corresponding
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to one of the segments present in `segment_ids`.
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"""
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if segment_ids.size == 0:
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return []
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K = max(segment_ids) + 1
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outputs = [
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np.zeros(
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(np.count_nonzero(segment_ids == seg_id), ) + data.shape[1:],
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dtype=data.dtype
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) for seg_id in range(0, K)
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]
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counts = np.zeros(K, dtype=int)
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for i, seg_id in enumerate(segment_ids):
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data_idx = i if indices is None else indices[i]
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outputs[seg_id][counts[seg_id]] = data[data_idx]
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counts[seg_id] += 1
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return outputs
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def unsplit(self, extra_shape, inputs, segment_ids):
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""" Inverse operation to `split`, with indices=None """
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output = np.zeros((len(segment_ids), ) + extra_shape)
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if len(segment_ids) == 0:
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return output
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K = max(segment_ids) + 1
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counts = np.zeros(K, dtype=int)
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for i, seg_id in enumerate(segment_ids):
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output[i] = inputs[seg_id][counts[seg_id]]
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counts[seg_id] += 1
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return output
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class LengthsTester(TesterBase):
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def split(self, data, lengths, indices=None):
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K = len(lengths)
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outputs = [
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np.zeros((lengths[seg_id], ) + data.shape[1:],
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dtype=data.dtype) for seg_id in range(0, K)
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]
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start = 0
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for i in range(0, K):
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for j in range(0, lengths[i]):
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data_index = start + j
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if indices is not None:
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data_index = indices[data_index]
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outputs[i][j] = data[data_index]
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start += lengths[i]
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return outputs
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def unsplit(self, extra_shape, inputs, lengths):
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N = sum(lengths)
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output = np.zeros((N, ) + extra_shape)
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K = len(lengths)
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assert len(inputs) == K
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current = 0
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for i in range(0, K):
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for j in range(0, lengths[i]):
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output[current] = inputs[i][j]
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current += 1
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return output
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def sum_grad(grad_out, outputs, inputs):
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return np.repeat(
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np.expand_dims(grad_out, axis=0),
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inputs[0].shape[0],
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axis=0
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)
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def logsumexp(x):
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return np.log(np.sum(np.exp(x), axis=0))
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def logsumexp_grad(grad_out, outputs, inputs):
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sum_exps = np.sum(np.exp(inputs[0]), axis=0)
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return np.repeat(
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np.expand_dims(grad_out / sum_exps, 0),
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inputs[0].shape[0],
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axis=0
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) * np.exp(inputs[0])
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def logmeanexp(x):
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return np.log(np.mean(np.exp(x), axis=0))
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def mean(x):
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return np.mean(x, axis=0)
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def mean_grad(grad_out, outputs, inputs):
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return np.repeat(
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np.expand_dims(grad_out / inputs[0].shape[0], 0),
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inputs[0].shape[0],
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axis=0
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)
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def max_fwd(x):
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return np.amax(x, axis=0)
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def max_grad(grad_out, outputs, inputs):
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flat_inputs = inputs[0].flatten()
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flat_outputs = np.array(outputs[0]).flatten()
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flat_grad_in = np.zeros(flat_inputs.shape)
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flat_grad_out = np.array(grad_out).flatten()
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blocks = inputs[0].shape[0]
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if blocks == 0:
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return np.zeros(inputs[0].shape)
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block_size = flat_inputs.shape[0] // blocks
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for i in range(block_size):
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out_grad = flat_grad_out[i]
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out = flat_outputs[i]
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for j in range(blocks):
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idx = j * block_size + i
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# we can produce multiple outputs for max
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if out == flat_inputs[idx]:
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flat_grad_in[idx] = out_grad
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return np.resize(flat_grad_in, inputs[0].shape)
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REFERENCES_ALL = [
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('Sum', partial(np.sum, axis=0), sum_grad),
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('Mean', partial(np.mean, axis=0), mean_grad),
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]
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REFERENCES_SORTED = [
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('RangeSum', partial(np.sum, axis=0), sum_grad),
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('RangeLogSumExp', logsumexp, logsumexp_grad),
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# gradient is the same as sum
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('RangeLogMeanExp', logmeanexp, logsumexp_grad),
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('RangeMean', mean, mean_grad),
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('RangeMax', max_fwd, max_grad),
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]
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REFERENCES_LENGTHS_ONLY = [
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('Max', partial(np.amax, axis=0), max_grad),
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]
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def sparse_lengths_weighted_sum_ref(D, W, I, L):
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R = np.zeros(shape=(len(L), ) + D.shape[1:], dtype=D.dtype)
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line = 0
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for g in range(len(L)):
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for _ in range(L[g]):
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R[g, :] += W[line] * D[I[line], :]
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line += 1
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return [R]
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def sparse_lengths_weighted_sum_grad_ref(
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GO, fwd_out, fwd_in, grad_on_weights=False):
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D, W, I, L = fwd_in
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GI = np.zeros(shape=(len(I), ) + D.shape[1:], dtype=D.dtype)
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GW = np.zeros(shape=W.shape, dtype=W.dtype) if grad_on_weights else None
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line = 0
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for g in range(len(L)):
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for _ in range(L[g]):
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GI[line, :] = W[line] * GO[g, :]
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if GW is not None:
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GW[line] = np.dot(GO[g].flatten(), D[I[line], :].flatten())
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line += 1
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print(GW)
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return [(GI, I), GW, None, None]
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class TestSegmentOps(hu.HypothesisTestCase):
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def test_sorted_segment_ops(self):
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SegmentsTester()._test(
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'SortedSegment',
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hu.segmented_tensor(
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dtype=np.float32,
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is_sorted=True,
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allow_empty=True
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),
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REFERENCES_ALL + REFERENCES_SORTED
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)(self)
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def test_unsorted_segment_ops(self):
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SegmentsTester()._test(
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'UnsortedSegment',
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hu.segmented_tensor(
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dtype=np.float32,
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is_sorted=False,
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allow_empty=True
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),
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REFERENCES_ALL,
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)(self)
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def test_unsorted_segment_ops_gpu(self):
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SegmentsTester()._test(
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'UnsortedSegment',
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hu.segmented_tensor(
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dtype=np.float32,
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is_sorted=False,
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allow_empty=True,
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),
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REFERENCES_ALL,
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gpu=workspace.has_gpu_support,
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grad_check=False,
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)(self)
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def test_sparse_sorted_segment_ops(self):
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SegmentsTester()._test(
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'SparseSortedSegment',
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hu.sparse_segmented_tensor(
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dtype=np.float32,
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is_sorted=True,
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allow_empty=True
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),
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REFERENCES_ALL
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)(self)
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def test_sparse_unsorted_segment_ops(self):
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SegmentsTester()._test(
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'SparseUnsortedSegment',
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hu.sparse_segmented_tensor(
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dtype=np.float32,
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is_sorted=False,
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allow_empty=True
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),
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REFERENCES_ALL
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)(self)
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def test_lengths_ops(self):
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LengthsTester()._test(
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'Lengths',
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hu.lengths_tensor(
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dtype=np.float32,
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min_value=1,
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max_value=5,
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allow_empty=True
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),
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REFERENCES_ALL + REFERENCES_LENGTHS_ONLY
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)(self)
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def test_sparse_lengths_ops(self):
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for itype in [np.int32, np.int64]:
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LengthsTester()._test(
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'SparseLengths',
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hu.sparse_lengths_tensor(
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dtype=np.float32,
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min_value=1,
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max_value=5,
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allow_empty=True,
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itype=itype,
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),
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REFERENCES_ALL
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)(self)
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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@given(**hu.gcs)
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def test_unsorted_sums_large(self, gc, dc):
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X = np.random.rand(10000, 32, 12).astype(np.float32)
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segments = np.random.randint(0, 10000, size=10000).astype(np.int32)
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op = core.CreateOperator("UnsortedSegmentSum", ["X", "segments"], "out")
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self.assertDeviceChecks(dc, op, [X, segments], [0])
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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@given(**hu.gcs)
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def test_sorted_segment_range_mean(self, gc, dc):
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X = np.random.rand(6, 32, 12).astype(np.float32)
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segments = np.array([0, 0, 1, 1, 2, 3]).astype(np.int32)
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op = core.CreateOperator(
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"SortedSegmentRangeMean",
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["X", "segments"],
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"out"
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)
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self.assertDeviceChecks(dc, op, [X, segments], [0])
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self.assertGradientChecks(gc, op, [X, segments], 0, [0])
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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@given(**hu.gcs)
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def test_sorted_segment_range_log_mean_exp(self, gc, dc):
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X = np.random.rand(7, 32, 12).astype(np.float32)
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segments = np.array([0, 0, 1, 1, 2, 2, 3]).astype(np.int32)
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op = core.CreateOperator(
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"SortedSegmentRangeLogMeanExp",
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["X", "segments"],
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"out"
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)
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self.assertDeviceChecks(dc, op, [X, segments], [0])
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self.assertGradientChecks(gc, op, [X, segments], 0, [0])
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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@given(**hu.gcs)
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def test_unsorted_means_large(self, gc, dc):
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X = np.random.rand(10000, 31, 19).astype(np.float32)
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segments = np.random.randint(0, 10000, size=10000).astype(np.int32)
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op = core.CreateOperator("UnsortedSegmentMean", ["X", "segments"], "out")
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self.assertDeviceChecks(dc, op, [X, segments], [0])
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@given(**hu.gcs)
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def test_lengths_sum_gpu(self, gc, dc):
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X = np.random.rand(50, 3, 4, 5).astype(np.float32)
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Y = np.asarray([20, 20, 10]).astype(np.int32)
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op = core.CreateOperator("LengthsSum", ["X", "Y"], "out")
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self.assertDeviceChecks(dc, op, [X, Y], [0])
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self.assertGradientChecks(gc, op, [X, Y], 0, [0])
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@given(**hu.gcs)
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def test_sparse_lengths_sum_gpu(self, gc, dc):
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X = np.random.rand(50, 3, 4, 5).astype(np.float32)
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Y = np.random.randint(0, 50, size=10).astype(np.int64)
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Z = np.asarray([4, 4, 2]).astype(np.int32)
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op = core.CreateOperator("SparseLengthsSum", ["X", "Y", "Z"], "out")
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self.assertDeviceChecks(dc, op, [X, Y, Z], [0])
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self.assertGradientChecks(gc, op, [X, Y, Z], 0, [0])
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@given(**hu.gcs)
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def test_sparse_lengths_weighted_sum_gpu(self, gc, dc):
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for grad_on_weights in (False, True):
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D = np.random.rand(50, 3, 4, 5).astype(np.float32)
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W = np.random.rand(10).astype(np.float32)
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I = np.random.randint(0, 50, size=10).astype(np.int64)
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L = np.asarray([4, 4, 2]).astype(np.int32)
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op = core.CreateOperator(
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"SparseLengthsWeightedSum",
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["D", "W", "I", "L"],
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"out",
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grad_on_weights=grad_on_weights)
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self.assertDeviceChecks(dc, op, [D, W, I, L], [0])
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[D, W, I, L],
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reference=sparse_lengths_weighted_sum_ref,
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threshold=1e-4,
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output_to_grad='out',
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grad_reference=partial(
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sparse_lengths_weighted_sum_grad_ref,
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grad_on_weights=grad_on_weights),
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)
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self.assertGradientChecks(gc, op, [D, W, I, L], 0, [0])
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@given(**hu.gcs)
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def test_sparse_lengths_indices_in_gradient_sum_gpu(self, gc, dc):
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X = np.random.rand(3, 3, 4, 5).astype(np.float32)
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Y = np.asarray([3, 3, 2]).astype(np.int32)
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Z = np.random.randint(0, 50, size=8).astype(np.int64)
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op = core.CreateOperator(
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"SparseLengthsIndicesInGradientSumGradient", ["X", "Y", "Z"], "out"
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)
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self.assertDeviceChecks(dc, op, [X, Y, Z], [0])
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@given(**hu.gcs_cpu_only)
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def test_legacy_sparse_and_lengths_sum_gradient(self, gc, dc):
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X = np.random.rand(3, 64).astype(np.float32)
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Y = np.asarray([20, 20, 10]).astype(np.int32)
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workspace.FeedBlob("X", X)
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workspace.FeedBlob("Y", Y)
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test_net = core.Net("test_net")
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test_net.SparseLengthsSumGradient(["X", "Y"], "out1")
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test_net.LengthsSumGradient(["X", "Y"], "out2")
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|
workspace.RunNetOnce(test_net)
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|
out1 = workspace.FetchBlob("out1")
|
|
out2 = workspace.FetchBlob("out2")
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|
self.assertTrue((out1 == out2).all())
|
|
|
|
@given(**hu.gcs)
|
|
def test_sparse_lengths_sum_invalid_index(self, gc, dc):
|
|
D = np.random.rand(50, 3, 4, 5).astype(np.float32)
|
|
I = (np.random.randint(0, 10000, size=10) + 10000).astype(np.int64)
|
|
L = np.asarray([4, 4, 2]).astype(np.int32)
|
|
op = core.CreateOperator(
|
|
"SparseLengthsSum",
|
|
["D", "I", "L"],
|
|
"out")
|
|
workspace.FeedBlob('D', D)
|
|
workspace.FeedBlob('I', I)
|
|
workspace.FeedBlob('L', L)
|
|
with self.assertRaises(RuntimeError):
|
|
workspace.RunOperatorOnce(op)
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|