mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
5cd0f5e8ec
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/41645 Pull Request resolved: https://github.com/facebookresearch/pytext/pull/1405 Test Plan: buck test Reviewed By: thatch Differential Revision: D20323893 fbshipit-source-id: 54665d589568c4198e96a27f0ed8e5b41df7b86b
715 lines
23 KiB
Python
715 lines
23 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 __future__ import unicode_literals
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from functools import partial
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from hypothesis import given, settings
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import numpy as np
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import unittest
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import hypothesis.strategies as st
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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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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if len(D.shape) > 1:
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R[g, :] += W[line] * D[I[line], :]
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else:
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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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if len(GO.shape) > 1:
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GI[line, :] = W[line] * GO[g, :]
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else:
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GI[line] = W[line] * GO[g]
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if GW is not None:
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if len(GO.shape) > 1:
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GW[line] = np.dot(GO[g].flatten(), D[I[line], :].flatten())
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else:
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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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@serial.given(
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inputs=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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**hu.gcs
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)
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def test_lengths_sum(self, inputs, gc, dc):
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X, Y = inputs
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op = core.CreateOperator("LengthsSum", ["X", "Y"], "out")
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def ref(D, L):
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R = np.zeros(shape=(L.size, ) + D.shape[1:], dtype=D.dtype)
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line = 0
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for g in range(L.size):
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for _ in range(L[g]):
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if len(D.shape) > 1:
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R[g, :] += D[line, :]
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else:
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R[g] += D[line]
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line += 1
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return [R]
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self.assertReferenceChecks(gc, op, [X, Y], ref)
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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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@serial.given(
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inputs=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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),
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**hu.gcs
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)
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def test_sparse_lengths_sum(self, inputs, gc, dc):
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X, Y, Z = inputs
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op = core.CreateOperator("SparseLengthsSum", ["X", "Y", "Z"], "out")
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def ref(D, I, L):
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R = np.zeros(shape=(L.size, ) + D.shape[1:], dtype=D.dtype)
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line = 0
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for g in range(L.size):
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for _ in range(L[g]):
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if len(D.shape) > 1:
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R[g, :] += D[I[line], :]
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else:
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R[g] += D[I[line]]
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line += 1
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return [R]
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self.assertReferenceChecks(gc, op, [X, Y, Z], ref)
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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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@serial.given(
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inputs=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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**hu.gcs
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)
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def test_lengths_mean(self, inputs, gc, dc):
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X, Y = inputs
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op = core.CreateOperator("LengthsMean", ["X", "Y"], "out")
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def ref(D, L):
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R = np.zeros(shape=(L.size, ) + D.shape[1:], dtype=D.dtype)
|
|
line = 0
|
|
for g in range(L.size):
|
|
for _ in range(L[g]):
|
|
if len(D.shape) > 1:
|
|
R[g, :] += D[line, :]
|
|
else:
|
|
R[g] += D[line]
|
|
line += 1
|
|
if L[g] > 1:
|
|
if len(D.shape) > 1:
|
|
R[g, :] = R[g, :] / L[g]
|
|
else:
|
|
R[g] = R[g] / L[g]
|
|
|
|
return [R]
|
|
|
|
self.assertReferenceChecks(gc, op, [X, Y], ref)
|
|
self.assertDeviceChecks(dc, op, [X, Y], [0])
|
|
self.assertGradientChecks(gc, op, [X, Y], 0, [0])
|
|
|
|
@serial.given(
|
|
inputs=hu.sparse_lengths_tensor(
|
|
dtype=np.float32,
|
|
min_value=1,
|
|
max_value=5,
|
|
allow_empty=True
|
|
),
|
|
**hu.gcs
|
|
)
|
|
def test_sparse_lengths_mean(self, inputs, gc, dc):
|
|
X, Y, Z = inputs
|
|
op = core.CreateOperator("SparseLengthsMean", ["X", "Y", "Z"], "out")
|
|
|
|
def ref(D, I, L):
|
|
R = np.zeros(shape=(L.size, ) + D.shape[1:], dtype=D.dtype)
|
|
line = 0
|
|
for g in range(L.size):
|
|
for _ in range(L[g]):
|
|
if len(D.shape) > 1:
|
|
R[g, :] += D[I[line], :]
|
|
else:
|
|
R[g] += D[I[line]]
|
|
line += 1
|
|
|
|
if L[g] > 1:
|
|
if len(D.shape) > 1:
|
|
R[g, :] = R[g, :] / L[g]
|
|
else:
|
|
R[g] = R[g] / L[g]
|
|
|
|
return [R]
|
|
|
|
self.assertReferenceChecks(gc, op, [X, Y, Z], ref)
|
|
self.assertDeviceChecks(dc, op, [X, Y, Z], [0])
|
|
self.assertGradientChecks(gc, op, [X, Y, Z], 0, [0])
|
|
|
|
@serial.given(
|
|
grad_on_weights=st.booleans(),
|
|
inputs=hu.sparse_lengths_tensor(
|
|
dtype=np.float32,
|
|
min_value=1,
|
|
max_value=5,
|
|
allow_empty=True
|
|
),
|
|
seed=st.integers(min_value=0, max_value=100),
|
|
**hu.gcs
|
|
)
|
|
def test_sparse_lengths_weighted_sum(
|
|
self, grad_on_weights, inputs, seed, gc, dc):
|
|
D, I, L = inputs
|
|
|
|
np.random.seed(int(seed))
|
|
|
|
W = np.random.rand(I.size).astype(np.float32)
|
|
op = core.CreateOperator(
|
|
"SparseLengthsWeightedSum",
|
|
["D", "W", "I", "L"],
|
|
"out",
|
|
grad_on_weights=grad_on_weights)
|
|
self.assertDeviceChecks(dc, op, [D, W, I, L], [0])
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[D, W, I, L],
|
|
reference=sparse_lengths_weighted_sum_ref,
|
|
threshold=1e-4,
|
|
output_to_grad='out',
|
|
grad_reference=partial(
|
|
sparse_lengths_weighted_sum_grad_ref,
|
|
grad_on_weights=grad_on_weights),
|
|
)
|
|
self.assertGradientChecks(gc, op, [D, W, I, L], 0, [0])
|
|
if grad_on_weights:
|
|
self.assertGradientChecks(gc, op, [D, W, I, L], 1, [0])
|
|
|
|
@given(**hu.gcs)
|
|
def test_sparse_lengths_indices_in_gradient_sum_gpu(self, gc, dc):
|
|
X = np.random.rand(3, 3, 4, 5).astype(np.float32)
|
|
Y = np.asarray([3, 3, 2]).astype(np.int32)
|
|
Z = np.random.randint(0, 50, size=8).astype(np.int64)
|
|
op = core.CreateOperator(
|
|
"SparseLengthsIndicesInGradientSumGradient", ["X", "Y", "Z"], "out"
|
|
)
|
|
self.assertDeviceChecks(dc, op, [X, Y, Z], [0])
|
|
|
|
@given(**hu.gcs)
|
|
def test_sparse_lengths_indices_in_gradient_mean_gpu(self, gc, dc):
|
|
X = np.random.rand(3, 3, 4, 5).astype(np.float32)
|
|
Y = np.asarray([3, 3, 2]).astype(np.int32)
|
|
Z = np.random.randint(0, 50, size=8).astype(np.int64)
|
|
op = core.CreateOperator(
|
|
"SparseLengthsIndicesInGradientMeanGradient", ["X", "Y", "Z"], "out"
|
|
)
|
|
self.assertDeviceChecks(dc, op, [X, Y, Z], [0])
|
|
|
|
@given(**hu.gcs_cpu_only)
|
|
def test_legacy_sparse_and_lengths_sum_gradient(self, gc, dc):
|
|
X = np.random.rand(3, 64).astype(np.float32)
|
|
Y = np.asarray([20, 20, 10]).astype(np.int32)
|
|
workspace.FeedBlob("X", X)
|
|
workspace.FeedBlob("Y", Y)
|
|
test_net = core.Net("test_net")
|
|
test_net.SparseLengthsSumGradient(["X", "Y"], "out1")
|
|
test_net.LengthsSumGradient(["X", "Y"], "out2")
|
|
workspace.RunNetOnce(test_net)
|
|
out1 = workspace.FetchBlob("out1")
|
|
out2 = workspace.FetchBlob("out2")
|
|
self.assertTrue((out1 == out2).all())
|
|
|
|
@given(**hu.gcs)
|
|
@settings(deadline=10000)
|
|
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)
|
|
|
|
@serial.given(**hu.gcs_cpu_only)
|
|
def test_sparse_lengths_positional_weighted_sum(
|
|
self, gc, dc):
|
|
D = np.random.rand(50, 3, 4, 5).astype(np.float32)
|
|
W = np.random.rand(50).astype(np.float32)
|
|
indices = np.random.randint(0, 50, size=10).astype(np.int64)
|
|
L = np.asarray([4, 4, 2]).astype(np.int32)
|
|
op = core.CreateOperator(
|
|
"SparseLengthsPositionalWeightedSum",
|
|
["D", "W", "indices", "L"],
|
|
"out")
|
|
|
|
def ref_sparse(D, W, indices, L):
|
|
workspace.FeedBlob("L", L)
|
|
lengths_range_fill_op = core.CreateOperator(
|
|
"LengthsRangeFill", ["L"], ["L_pos_seq"])
|
|
workspace.RunOperatorOnce(lengths_range_fill_op)
|
|
|
|
workspace.FeedBlob("W", W)
|
|
gather_op = core.CreateOperator(
|
|
"Gather", ["W", "L_pos_seq"], ["W_gathered"])
|
|
workspace.RunOperatorOnce(gather_op)
|
|
|
|
workspace.FeedBlob("D", D)
|
|
workspace.FeedBlob("indices", indices)
|
|
sparse_op = core.CreateOperator(
|
|
"SparseLengthsWeightedSum",
|
|
["D", "W_gathered", "indices", "L"],
|
|
"out_ref")
|
|
workspace.RunOperatorOnce(sparse_op)
|
|
|
|
return (workspace.FetchBlob("out_ref"),)
|
|
|
|
self.assertReferenceChecks(
|
|
gc, op, [D, W, indices, L], ref_sparse)
|
|
|
|
# @given(
|
|
# inputs=hu.lengths_tensor(
|
|
# dtype=np.float32,
|
|
# min_value=1,
|
|
# max_value=5,
|
|
# min_dim=1,
|
|
# max_dim=1,
|
|
# allow_empty=False,
|
|
# ),
|
|
# **hu.gcs
|
|
# )
|
|
# def test_lengths_max_gpu(self, inputs, gc, dc):
|
|
# def lengths_max_ref(I, L):
|
|
# R = np.zeros(shape=(len(L)), dtype=I.dtype)
|
|
# line = 0
|
|
# for g in range(len(L)):
|
|
# for i in range(L[g]):
|
|
# if i == 0:
|
|
# R[g] = I[line]
|
|
# else:
|
|
# R[g] = max(R[g], I[line])
|
|
# line += 1
|
|
# return [R]
|
|
|
|
# X, lengths = inputs
|
|
# op = core.CreateOperator("LengthsMax", ["X", "lengths"], "out")
|
|
# self.assertDeviceChecks(dc, op, [X, lengths], [0])
|
|
# self.assertReferenceChecks(
|
|
# device_option=gc,
|
|
# op=op,
|
|
# inputs=[X, lengths],
|
|
# reference=lengths_max_ref,
|
|
# threshold=1e-4,
|
|
# output_to_grad='out',
|
|
# )
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|