wehub-resource-sync
851 lines
31 KiB
Python
851 lines
31 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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"""Tests for ShardedVariable."""
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import os
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.checkpoint import checkpoint as util
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from tensorflow.python.client import session as session_lib
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from tensorflow.python.compat import v2_compat
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import distribute_lib
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from tensorflow.python.distribute import parameter_server_strategy_v2
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from tensorflow.python.distribute import sharded_variable
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from tensorflow.python.distribute.cluster_resolver import cluster_resolver as cluster_resolver_lib
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from tensorflow.python.distribute.test_util import get_cluster_def
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from tensorflow.python.distribute.test_util import TestClusterParams
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import indexed_slices
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.module import module
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import embedding_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables as variables_lib
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import load
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from tensorflow.python.saved_model import loader
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from tensorflow.python.saved_model import save
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model import tag_constants
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from tensorflow.python.trackable import autotrackable
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from tensorflow.python.training import server_lib
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from tensorflow.python.util import nest
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# We create one cluster to share between tests. The cluster should be large
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# enough to accommodate all the tests. Adjust the following constants as needed
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# but be aware of resource limitations in OSS tests.
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test_cluster_params = TestClusterParams(None, 2, 3)
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def _load_and_run(
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model_dir,
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inputs,
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signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY):
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"""Load a SavedModel into a TF 1.x-style graph and run `signature_key`."""
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graph = ops.Graph()
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with graph.as_default(), session_lib.Session() as session:
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meta_graph_def = loader.load(session, [tag_constants.SERVING], model_dir)
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signature = meta_graph_def.signature_def[signature_key]
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feed_dict = {}
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for arg_name in inputs.keys():
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input_tensor = session.graph.get_tensor_by_name(
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signature.inputs[arg_name].name)
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feed_dict[input_tensor] = inputs[arg_name]
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output_dict = {}
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for output_name, output_tensor_info in signature.outputs.items():
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output_dict[output_name] = session.graph.get_tensor_by_name(
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output_tensor_info.name)
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return session.run(output_dict, feed_dict=feed_dict)
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class PartitionerTest(test.TestCase):
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def test_fixed_shards_partitioner(self):
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partitioner = sharded_variable.FixedShardsPartitioner(num_shards=2)
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got = partitioner(tensor_shape.TensorShape([10, 3]), dtypes.float32)
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self.assertAllEqual(got, [2, 1])
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def test_min_size_partitioner(self):
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partitioner = sharded_variable.MinSizePartitioner(
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min_shard_bytes=4, max_shards=2)
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got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
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self.assertAllEqual(got, [2, 1])
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partitioner = sharded_variable.MinSizePartitioner(
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min_shard_bytes=4, max_shards=10)
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got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
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self.assertAllEqual(got, [6, 1])
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def test_max_size_partitioner(self):
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partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=4)
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got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
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self.assertAllEqual(got, [6, 1])
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partitioner = sharded_variable.MaxSizePartitioner(
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max_shard_bytes=4, max_shards=2)
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got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
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self.assertAllEqual(got, [2, 1])
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partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=1024)
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got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
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self.assertAllEqual(got, [1, 1])
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def test_partitioner_invalid_args(self):
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with self.assertRaisesRegex(
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ValueError, 'Argument `min_shard_bytes` must be positive.'
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):
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sharded_variable.MinSizePartitioner(min_shard_bytes=-1)
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with self.assertRaisesRegex(
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ValueError, 'Argument `max_shards` must be positive.'
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):
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sharded_variable.MinSizePartitioner(min_shard_bytes=4, max_shards=-1)
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with self.assertRaisesRegex(
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ValueError, 'Argument `bytes_per_string` must be positive.'
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):
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sharded_variable.MinSizePartitioner(
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min_shard_bytes=4, bytes_per_string=-1
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)
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with self.assertRaisesRegex(
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ValueError, 'Argument `max_shard_bytes` must be positive.'
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):
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sharded_variable.MaxSizePartitioner(max_shard_bytes=-1)
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with self.assertRaisesRegex(
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ValueError, 'Argument `max_shards` must be positive.'
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):
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sharded_variable.MaxSizePartitioner(max_shard_bytes=4, max_shards=-1)
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with self.assertRaisesRegex(
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ValueError, 'Argument `bytes_per_string` must be positive.'
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):
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sharded_variable.MaxSizePartitioner(
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max_shard_bytes=4, bytes_per_string=-1
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)
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class ShardedVariableTest(test.TestCase, parameterized.TestCase):
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def test_sharded_variable_simple(self):
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v0 = variables_lib.Variable([0])
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v1 = variables_lib.Variable([1])
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s = sharded_variable.ShardedVariable([v0, v1], name='s')
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self.assertEqual(s.variables[0], v0)
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self.assertEqual(s.variables[1], v1)
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self.assertEqual(s.shape.as_list(), [2])
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self.assertEqual(s.dtype, v0.dtype)
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self.assertEqual(s.name, 's')
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def test_assign(self):
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v0 = variables_lib.Variable([[0, 0]])
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v1 = variables_lib.Variable([[1, 1], [2, 2]])
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v2 = variables_lib.Variable([[3, 3]])
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s = sharded_variable.ShardedVariable([v0, v1, v2])
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ret = s.assign([[4, 4], [5, 5], [6, 6], [7, 7]])
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self.assertAllEqual(self.evaluate(s.variables[0]), [[4, 4]])
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self.assertAllEqual(self.evaluate(s.variables[1]), [[5, 5], [6, 6]])
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self.assertAllEqual(self.evaluate(s.variables[2]), [[7, 7]])
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self.assertIs(ret, s)
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def test_assign_add(self):
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v0 = variables_lib.Variable([[0, 0]])
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v1 = variables_lib.Variable([[1, 1], [2, 2]])
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v2 = variables_lib.Variable([[3, 3]])
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s = sharded_variable.ShardedVariable([v0, v1, v2])
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ret = s.assign_add([[1, 1], [1, 1], [2, 2], [2, 2]])
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self.assertAllEqual(self.evaluate(s.variables[0]), [[1, 1]])
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self.assertAllEqual(self.evaluate(s.variables[1]), [[2, 2], [4, 4]])
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self.assertAllEqual(self.evaluate(s.variables[2]), [[5, 5]])
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self.assertIs(ret, s)
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def test_assign_sub(self):
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v0 = variables_lib.Variable([[0, 0]])
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v1 = variables_lib.Variable([[1, 1], [2, 2]])
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v2 = variables_lib.Variable([[3, 3]])
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s = sharded_variable.ShardedVariable([v0, v1, v2])
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ret = s.assign_sub([[0, 0], [1, 1], [1, 1], [3, 3]])
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self.assertAllEqual(self.evaluate(s.variables[0]), [[0, 0]])
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self.assertAllEqual(self.evaluate(s.variables[1]), [[0, 0], [1, 1]])
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self.assertAllEqual(self.evaluate(s.variables[2]), [[0, 0]])
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self.assertIs(ret, s)
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def test_scatter_add_uneven_partition(self):
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v = variables_lib.Variable(array_ops.zeros((32, 1)))
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sparse_delta = indexed_slices.IndexedSlices(
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values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]),
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indices=constant_op.constant([0, 10, 11, 12, 30, 31]))
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v0 = variables_lib.Variable(array_ops.zeros((11, 1)))
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v1 = variables_lib.Variable(array_ops.zeros((11, 1)))
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v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
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sv = sharded_variable.ShardedVariable([v0, v1, v2])
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v.scatter_add(sparse_delta)
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sv.scatter_add(sparse_delta)
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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@def_function.function
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def func():
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v.scatter_add(sparse_delta)
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sv.scatter_add(sparse_delta)
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func()
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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@parameterized.parameters('scatter_add', 'scatter_div', 'scatter_max',
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'scatter_min', 'scatter_mul', 'scatter_sub',
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'scatter_update')
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def test_scatter_ops_even_partition(self, op):
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v = variables_lib.Variable(array_ops.zeros((30, 1)))
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# Make sure values does not contain 0 due to testing `scatter_div`!
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sparse_delta = indexed_slices.IndexedSlices(
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values=constant_op.constant([[1.], [2.], [3.], [4.], [5.]]),
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indices=constant_op.constant([0, 10, 12, 21, 22]))
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v0 = variables_lib.Variable(array_ops.zeros((10, 1)))
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v1 = variables_lib.Variable(array_ops.zeros((10, 1)))
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v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
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sv = sharded_variable.ShardedVariable([v0, v1, v2])
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getattr(v, op)(sparse_delta, name='scatter_v')
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getattr(sv, op)(sparse_delta, name='scatter_sv')
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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@def_function.function
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def func():
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getattr(v, op)(sparse_delta, name='scatter_v')
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getattr(sv, op)(sparse_delta, name='scatter_sv')
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func()
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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def test_batch_scatter_update(self):
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v = variables_lib.Variable(array_ops.zeros((32, 1)))
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sparse_delta = indexed_slices.IndexedSlices(
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values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]),
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indices=constant_op.constant([10, 11, 12, 13, 14, 15]))
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v0 = variables_lib.Variable(array_ops.zeros((11, 1)))
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v1 = variables_lib.Variable(array_ops.zeros((11, 1)))
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v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
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sv = sharded_variable.ShardedVariable([v0, v1, v2])
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v.batch_scatter_update(sparse_delta)
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sv.batch_scatter_update(sparse_delta)
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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@def_function.function
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def func():
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v.batch_scatter_update(sparse_delta)
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sv.batch_scatter_update(sparse_delta)
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func()
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self.assertAllEqual(v, ops.convert_to_tensor(sv))
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def test_sparse_read(self):
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values = array_ops.reshape(
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math_ops.range(30, dtype=dtypes.float32), (30, 1)
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)
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v = variables_lib.Variable(values)
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indices = constant_op.constant([21, 0, 12, 22, 10])
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v0 = variables_lib.Variable(values[:10])
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v1 = variables_lib.Variable(values[10:20])
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v2 = variables_lib.Variable(values[20:])
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sv = sharded_variable.ShardedVariable([v0, v1, v2])
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self.assertAllEqual(v.sparse_read(indices), sv.sparse_read(indices))
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@def_function.function
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def func():
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return v.sparse_read(indices), sv.sparse_read(indices)
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got, expect = func()
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self.assertAllEqual(got, expect)
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def test_control_dep_on_assign(self):
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v0 = variables_lib.Variable([[0, 0]])
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v1 = variables_lib.Variable([[1, 1], [2, 2]])
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v2 = variables_lib.Variable([[3, 3]])
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s = sharded_variable.ShardedVariable([v0, v1, v2])
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@def_function.function
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def func():
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ret = s.assign([[4, 4], [5, 5], [6, 6], [7, 7]])
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with ops.control_dependencies([ret]):
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a = array_ops.ones((1, 1))
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with ops.control_dependencies([control_flow_ops.group(ret)]):
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b = array_ops.ones((1, 1))
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return a, b
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a, b = func()
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self.assertAllEqual(a, [[1]])
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self.assertAllEqual(b, [[1]])
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self.assertAllEqual(s.variables[0], [[4, 4]])
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def test_convert_to_tensor(self):
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v0 = variables_lib.Variable([[0, 0]])
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v1 = variables_lib.Variable([[1, 1], [2, 2]])
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v2 = variables_lib.Variable([[3, 3]])
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s = sharded_variable.ShardedVariable([v0, v1, v2])
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t = ops.convert_to_tensor(s)
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self.assertAllEqual(t, [[0, 0], [1, 1], [2, 2], [3, 3]])
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def test_save_restore(self):
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fname = os.path.join(self.get_temp_dir(), 'checkpoint')
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variables = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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variables_lib.Variable([2]),
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variables_lib.Variable([3])
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]
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s = sharded_variable.ShardedVariable(variables, name='s')
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cp = util.Checkpoint(s=s)
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self.assertEqual(self.evaluate(cp.s.variables[0]), [0])
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cp.write(fname)
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self.evaluate(cp.s.variables[0].assign([4]))
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self.assertEqual(self.evaluate(cp.s.variables[0]), [4])
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cp.restore(fname)
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# Tests that the original weights are restored.
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self.assertEqual(self.evaluate(cp.s.variables[0]), [0])
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def test_save_restore_different_partitions(self):
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fname = os.path.join(self.get_temp_dir(), 'checkpoint')
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variables = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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variables_lib.Variable([2]),
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variables_lib.Variable([3])
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]
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s = sharded_variable.ShardedVariable(variables, name='s')
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cp = util.Checkpoint(s=s)
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cp.write(fname)
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variables2 = [variables_lib.Variable([0, 0, 0, 0])]
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s2 = sharded_variable.ShardedVariable(variables2, name='s')
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# Restore from 4 partitions into 1.
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cp2 = util.Checkpoint(s=s2)
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cp2.restore(fname)
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self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1, 2, 3])
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self.evaluate(cp2.s.variables[0].assign([5, 10, 15, 20]))
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cp2.write(fname)
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# Restore 1 partition into 4.
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cp.restore(fname)
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self.assertEqual(self.evaluate(cp.s.variables[0]), [5])
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self.assertEqual(self.evaluate(cp.s.variables[1]), [10])
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self.assertEqual(self.evaluate(cp.s.variables[2]), [15])
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self.assertEqual(self.evaluate(cp.s.variables[3]), [20])
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def test_save_restore_4_to_2_partitions(self):
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fname = os.path.join(self.get_temp_dir(), 'checkpoint')
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variables = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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variables_lib.Variable([2]),
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variables_lib.Variable([3])
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]
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s = sharded_variable.ShardedVariable(variables, name='s')
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cp = util.Checkpoint(s=s)
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cp.write(fname)
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variables2 = [
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variables_lib.Variable([0, 0]),
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variables_lib.Variable([0, 0])
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]
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s2 = sharded_variable.ShardedVariable(variables2, name='s')
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cp2 = util.Checkpoint(s=s2)
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cp2.restore(fname)
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# Assert that weights from the 4 partitions were loaded here.
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self.assertLen(cp2.s.variables, 2)
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self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1])
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self.assertAllEqual(self.evaluate(cp2.s.variables[1]), [2, 3])
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def test_delayed_restore(self):
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fname = os.path.join(self.get_temp_dir(), 'checkpoint')
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model = autotrackable.AutoTrackable()
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variables = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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variables_lib.Variable([2]),
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variables_lib.Variable([3])
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]
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model.s = sharded_variable.ShardedVariable(variables)
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cp = util.Checkpoint(model=model)
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cp.write(fname)
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model2 = autotrackable.AutoTrackable()
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cp2 = util.Checkpoint(model=model2)
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cp2.restore(fname)
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variables2 = [
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variables_lib.Variable([0]),
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variables_lib.Variable([0]),
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variables_lib.Variable([0]),
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variables_lib.Variable([0])
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]
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model2.s = sharded_variable.ShardedVariable(variables2)
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self.assertAllEqual(self.evaluate(model2.s.variables[0]), [0])
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self.assertAllEqual(self.evaluate(model2.s.variables[1]), [1])
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self.assertAllEqual(self.evaluate(model2.s.variables[2]), [2])
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self.assertAllEqual(self.evaluate(model2.s.variables[3]), [3])
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def test_delayed_restore_4_to_2_partitions(self):
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fname = os.path.join(self.get_temp_dir(), 'checkpoint')
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model = autotrackable.AutoTrackable()
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variables = [
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variables_lib.Variable([0]),
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variables_lib.Variable([1]),
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variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
model.s = sharded_variable.ShardedVariable(variables)
|
|
cp = util.Checkpoint(model=model)
|
|
cp.write(fname)
|
|
|
|
model2 = autotrackable.AutoTrackable()
|
|
cp2 = util.Checkpoint(model=model2)
|
|
cp2.restore(fname)
|
|
variables2 = [
|
|
variables_lib.Variable([0, 0]),
|
|
variables_lib.Variable([0, 0])
|
|
]
|
|
model2.s = sharded_variable.ShardedVariable(variables2)
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[0]), [0, 1])
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[1]), [2, 3])
|
|
|
|
def test_save_graph_def(self):
|
|
root = autotrackable.AutoTrackable()
|
|
v1 = variables_lib.Variable([3.])
|
|
v2 = variables_lib.Variable([2.])
|
|
root.v = sharded_variable.ShardedVariable([v1, v2])
|
|
root.train = def_function.function(
|
|
lambda x: embedding_ops.embedding_lookup_v2(root.v.variables, x))
|
|
# TODO(b/144057383): Remove the necessity of root.serve once saving context
|
|
# is made to tf.function cache.
|
|
root.serve = def_function.function(
|
|
lambda x: embedding_ops.embedding_lookup_v2(root.v.variables[0], x),
|
|
input_signature=[tensor_spec.TensorSpec([2], dtypes.int32, name='x')])
|
|
|
|
# Trace and use root.train
|
|
self.assertAllEqual([3., 2.], root.train([0, 1]).numpy())
|
|
|
|
save_dir = os.path.join(self.get_temp_dir(), 'saved_model')
|
|
save.save(root, save_dir, root.serve)
|
|
self.assertAllEqual([3., 2.],
|
|
_load_and_run(save_dir, {'x': [0, 1]})['output_0'])
|
|
|
|
# Continue using root.train for training
|
|
self.assertAllEqual([3., 2.], root.train([0, 1]).numpy())
|
|
|
|
def test_validation_errors(self):
|
|
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
|
|
sharded_variable.ShardedVariable(None)
|
|
|
|
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
|
|
sharded_variable.ShardedVariable(
|
|
[variables_lib.Variable([0]), 'not-a-variable'])
|
|
|
|
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
|
|
sharded_variable.ShardedVariable([])
|
|
|
|
with self.assertRaisesRegex(ValueError, 'must have the same dtype'):
|
|
sharded_variable.ShardedVariable([
|
|
variables_lib.Variable([0], dtype='int64'),
|
|
variables_lib.Variable([1], dtype='int32')
|
|
])
|
|
|
|
with self.assertRaisesRegex(ValueError, 'the same shapes except'):
|
|
sharded_variable.ShardedVariable([
|
|
variables_lib.Variable(array_ops.ones((5, 10))),
|
|
variables_lib.Variable(array_ops.ones((5, 20)))
|
|
])
|
|
|
|
with self.assertRaisesRegex(ValueError, '`SaveSliceInfo` should not'):
|
|
v = variables_lib.Variable([0])
|
|
v._set_save_slice_info(
|
|
variables_lib.Variable.SaveSliceInfo(
|
|
full_name='s', full_shape=[2], var_offset=[0], var_shape=[1]))
|
|
sharded_variable.ShardedVariable([v])
|
|
|
|
def test_as_function_input(self):
|
|
variables1 = [
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables1)
|
|
variables2 = [
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([2]),
|
|
]
|
|
s2 = sharded_variable.ShardedVariable(variables2)
|
|
|
|
trace_count = [0]
|
|
|
|
@def_function.function
|
|
def func(sharded_var):
|
|
trace_count[0] = trace_count[0] + 1
|
|
sharded_var.assign([0, 0])
|
|
|
|
func(s)
|
|
self.assertAllEqual(ops.convert_to_tensor(s), [0, 0])
|
|
self.assertEqual(trace_count[0], 1)
|
|
func(s2)
|
|
self.assertAllEqual(ops.convert_to_tensor(s2), [0, 0])
|
|
self.assertEqual(trace_count[0], 1)
|
|
|
|
def test_flatten(self):
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables)
|
|
|
|
got = nest.flatten(s)
|
|
self.assertIs(s, got[0])
|
|
|
|
got = nest.flatten(s, expand_composites=True)
|
|
expected = nest.flatten(variables, expand_composites=True)
|
|
self.assertEqual(got, expected)
|
|
|
|
def test_tf_module(self):
|
|
|
|
class Model(module.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
self.w = sharded_variable.ShardedVariable(variables)
|
|
|
|
model = Model()
|
|
|
|
self.assertLen(model.variables, 2)
|
|
self.assertEqual(model.variables[0], [0])
|
|
self.assertEqual(model.variables[1], [1])
|
|
self.assertAllEqual(model.variables, model.trainable_variables)
|
|
|
|
self.assertLen(model._trackable_children(), 1)
|
|
self.assertIs(model._trackable_children().popitem()[1], model.w)
|
|
|
|
def test_embedding_lookup(self):
|
|
v = [
|
|
variables_lib.Variable([[1., 2.], [3., 4.]]),
|
|
variables_lib.Variable([[5., 6.], [7., 8.]]),
|
|
variables_lib.Variable([[9., 10.]])
|
|
]
|
|
sv = sharded_variable.ShardedVariable(v)
|
|
|
|
@def_function.function
|
|
def lookup():
|
|
ids = constant_op.constant([0, 3, 4])
|
|
return embedding_ops.embedding_lookup_v2(sv, ids)
|
|
|
|
@def_function.function
|
|
def sparse_lookup():
|
|
sp_ids = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[0, 3, 4, 1],
|
|
dense_shape=[3, 3])
|
|
return embedding_ops.embedding_lookup_sparse_v2(sv, sp_ids, None)
|
|
|
|
@def_function.function
|
|
def safe_sparse_lookup():
|
|
sp_ids = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[0, -1, 4, 1],
|
|
dense_shape=[3, 3])
|
|
sp_weights = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[1., 1., -1., 1.],
|
|
dense_shape=[3, 3])
|
|
return embedding_ops.safe_embedding_lookup_sparse_v2(
|
|
sv, sp_ids, sp_weights)
|
|
|
|
for func in [lookup, sparse_lookup, safe_sparse_lookup]:
|
|
num_gather_ops = 0
|
|
for op in func.get_concrete_function().graph.get_operations():
|
|
if op.type == 'ResourceGather':
|
|
num_gather_ops += 1
|
|
self.assertEqual(
|
|
num_gather_ops, len(v), 'Number of ResourceGather op '
|
|
f'({num_gather_ops}) does not match expected ({len(v)}), possibly '
|
|
'due to ShardedVariable accidentally being converted to tensor in '
|
|
'embedding_lookup ops.')
|
|
|
|
self.assertAllEqual(lookup(), [[1., 2.], [7., 8.], [9., 10.]])
|
|
self.assertAllClose(sparse_lookup(), [[4., 5.], [9., 10.], [3., 4.]])
|
|
self.assertAllClose(safe_sparse_lookup(), [[1., 2.], [0., 0.], [3., 4.]])
|
|
|
|
def test_slicing(self):
|
|
data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14],
|
|
[15, 16]]
|
|
v = [
|
|
variables_lib.Variable(data[:3]),
|
|
variables_lib.Variable(data[3:6]),
|
|
variables_lib.Variable(data[6:])
|
|
]
|
|
sv = sharded_variable.ShardedVariable(v)
|
|
empty = v[0][0:0]
|
|
|
|
# Test cases: all individual indices
|
|
for ix in range(len(data)):
|
|
self.assertAllEqual(sv[ix].numpy(), data[ix])
|
|
|
|
# Test cases: positive step
|
|
self.assertAllEqual(sv[:], array_ops.concat(v, axis=0))
|
|
self.assertAllEqual(sv[:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[-8:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[-10:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[5:], [[11, 12], [13, 14], [15, 16]])
|
|
self.assertAllEqual(sv[5:-1], [[11, 12], [13, 14]])
|
|
self.assertAllEqual(sv[::3], [[1, 2], [7, 8], [13, 14]])
|
|
self.assertAllEqual(sv[::5], [[1, 2], [11, 12]])
|
|
self.assertAllEqual(sv[1::6], [[3, 4], [15, 16]])
|
|
self.assertAllEqual(sv[1:5:6], [[3, 4]])
|
|
self.assertAllEqual(sv[1::7], [[3, 4]])
|
|
self.assertAllEqual(sv[2:7], [[5, 6], [7, 8], [9, 10], [11, 12], [13, 14]])
|
|
self.assertAllEqual(sv[2:7:2], [[5, 6], [9, 10], [13, 14]])
|
|
self.assertAllEqual(sv[2:7:3], [[5, 6], [11, 12]])
|
|
|
|
# Test cases: negative step
|
|
self.assertAllEqual(
|
|
sv[::-1], array_ops.reverse(array_ops.concat(v, axis=0), axis=[0]))
|
|
self.assertAllEqual(sv[2::-1], [[5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[2:-8:-1], [[5, 6], [3, 4]])
|
|
self.assertAllEqual(sv[2:-10:-1], [[5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[4::-1], [[9, 10], [7, 8], [5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[-1:-3:-1], [[15, 16], [13, 14]])
|
|
self.assertAllEqual(sv[::-5], [[15, 16], [5, 6]])
|
|
self.assertAllEqual(sv[6::-6], [[13, 14], [1, 2]])
|
|
self.assertAllEqual(sv[6:5:-6], [[13, 14]])
|
|
self.assertAllEqual(sv[6::-7], [[13, 14]])
|
|
self.assertAllEqual(sv[7:1:-1],
|
|
[[15, 16], [13, 14], [11, 12], [9, 10], [7, 8], [5, 6]])
|
|
self.assertAllEqual(sv[7:1:-2], [[15, 16], [11, 12], [7, 8]])
|
|
self.assertAllEqual(sv[7:1:-4], [[15, 16], [7, 8]])
|
|
|
|
# Test cases: empty slice
|
|
self.assertAllEqual(sv[0:0], empty)
|
|
self.assertAllEqual(sv[5:3], empty)
|
|
self.assertAllEqual(sv[3:5:-1], empty)
|
|
self.assertAllEqual(sv[-1:0], empty)
|
|
self.assertAllEqual(sv[2:-1:-1], empty)
|
|
|
|
# Test cases: slicing other dimensions
|
|
self.assertAllEqual(sv[:, 0], [1, 3, 5, 7, 9, 11, 13, 15])
|
|
self.assertAllEqual(sv[:, 0:1], [[1], [3], [5], [7], [9], [11], [13], [15]])
|
|
|
|
# Test cases: normal indexing
|
|
self.assertAllEqual(sv[2], [5, 6])
|
|
self.assertAllEqual(sv[6], [13, 14])
|
|
self.assertAllEqual(sv[2, 1], 6)
|
|
self.assertAllEqual(sv[-2], [13, 14])
|
|
with self.assertRaisesRegex(IndexError, 'out of bounds'):
|
|
_ = sv[100]
|
|
with self.assertRaisesRegex(IndexError, 'out of bounds'):
|
|
_ = sv[-100]
|
|
|
|
# Test cases: Ellipsis
|
|
self.assertAllEqual(sv[...], array_ops.concat(v, axis=0))
|
|
self.assertAllEqual(sv[..., 0], [1, 3, 5, 7, 9, 11, 13, 15])
|
|
self.assertAllEqual(sv[0:1, ...], [[1, 2]])
|
|
|
|
# Test cases: newaxis
|
|
self.assertAllEqual(
|
|
sv[array_ops.newaxis, ...],
|
|
array_ops.expand_dims_v2(array_ops.concat(v, axis=0), axis=0))
|
|
self.assertAllEqual(
|
|
sv[array_ops.newaxis, 0:5],
|
|
array_ops.expand_dims_v2(array_ops.concat(v, axis=0)[0:5], axis=0))
|
|
|
|
# Test cases: boolean masks
|
|
self.assertAllEqual(sv[ops.convert_to_tensor(sv) > 10],
|
|
[11, 12, 13, 14, 15, 16])
|
|
|
|
# Test cases: tensor input
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[constant_op.constant(1)::]
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[:constant_op.constant(1):]
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[constant_op.constant(1)]
|
|
|
|
# Test cases: inside tf.function
|
|
@def_function.function
|
|
def func():
|
|
a = sv[:, 0]
|
|
return a
|
|
|
|
self.assertAllEqual(func(), [1, 3, 5, 7, 9, 11, 13, 15])
|
|
|
|
def test_operator_overload(self):
|
|
v1 = [
|
|
variables_lib.Variable([1.]),
|
|
variables_lib.Variable([2.]),
|
|
]
|
|
sv1 = sharded_variable.ShardedVariable(v1)
|
|
|
|
v2 = [
|
|
variables_lib.Variable([1.]),
|
|
variables_lib.Variable([2.]),
|
|
]
|
|
sv2 = sharded_variable.ShardedVariable(v2)
|
|
|
|
equal = sv1 == sv2
|
|
self.assertAllEqual(equal, [True, True])
|
|
self.assertAllEqual(sv1 + sv2, [2.0, 4.0])
|
|
|
|
def test_shards_have_container_set(self):
|
|
v1 = [
|
|
variables_lib.Variable([1.]),
|
|
variables_lib.Variable([2.]),
|
|
]
|
|
sv1 = sharded_variable.ShardedVariable(v1)
|
|
for v in sv1.variables:
|
|
self.assertTrue(hasattr(v, '_sharded_container'))
|
|
self.assertIs(v._sharded_container(), sv1)
|
|
|
|
def test_numpy(self):
|
|
v1 = [
|
|
variables_lib.Variable([1.]),
|
|
variables_lib.Variable([2.]),
|
|
]
|
|
sv1 = sharded_variable.ShardedVariable(v1)
|
|
sv1_np = sv1.numpy()
|
|
self.assertIsInstance(sv1_np, np.ndarray)
|
|
self.assertAllEqual(sv1_np, np.array([1., 2.]))
|
|
|
|
|
|
class ShardedVariableSaveLoadTest(test.TestCase, parameterized.TestCase):
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
cluster_def = get_cluster_def(test_cluster_params, num_workers=2, num_ps=3)
|
|
self.cluster_resolver = cluster_resolver_lib.SimpleClusterResolver(
|
|
server_lib.ClusterSpec(cluster_def))
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
# Reset context to disconnect from the cluster.
|
|
context._reset_context()
|
|
|
|
def _create_strategy(self, num_shards):
|
|
if num_shards > 1:
|
|
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
|
|
self.cluster_resolver,
|
|
variable_partitioner=sharded_variable.FixedShardsPartitioner(
|
|
num_shards))
|
|
else:
|
|
strategy = distribute_lib._get_default_strategy()
|
|
return strategy
|
|
|
|
@combinations.generate(
|
|
combinations.combine(
|
|
shard_config=[[2, 2], [2, 3], [3, 2], [2, 1], [1, 1]],
|
|
))
|
|
def testSaveAndLoadSingleVariable(self, shard_config):
|
|
"""Test saving and loading ShardedVariable with different numbers of shards.
|
|
|
|
Loading tf.Variables into multiple Shards is not yet supported
|
|
|
|
Args:
|
|
shard_config: The number of shards to use before and after loading. For
|
|
example, [2, 1] means to create and save the variable with 2 shards and
|
|
load it into 1 shard (i.e., a regular tf.Variable).
|
|
"""
|
|
strategy = self._create_strategy(shard_config[0])
|
|
|
|
with strategy.scope():
|
|
var = variables_lib.Variable([1., 2., 3., 4., 5., 6.])
|
|
|
|
# Save variable
|
|
model_dir = self.get_temp_dir()
|
|
save.save(var, model_dir)
|
|
|
|
strategy2 = self._create_strategy(shard_config[1])
|
|
with strategy2.scope():
|
|
# Load variable
|
|
loaded = load.load(model_dir)
|
|
|
|
# Assert all values loaded, values are same
|
|
if shard_config[1] > 1:
|
|
loaded = array_ops.concat(loaded.variables, axis=0)
|
|
self.assertLen(loaded.numpy(), 6)
|
|
|
|
if shard_config[0] > 1:
|
|
var = array_ops.concat(var.variables, axis=0)
|
|
self.assertAllClose(var.numpy(), loaded.numpy())
|
|
|
|
def testSaveAndLoadModuleUnderStrategy(self):
|
|
|
|
class Dense(module.Module):
|
|
|
|
def __init__(self):
|
|
self.kernel = variables_lib.Variable(
|
|
random_ops.random_uniform((6, 6)), name='kernel')
|
|
self.bias = variables_lib.Variable(
|
|
random_ops.random_uniform((6,)), name='bias')
|
|
|
|
@def_function.function
|
|
def __call__(self, x):
|
|
out = math_ops.matmul(self.kernel, x)
|
|
out = out + self.bias
|
|
return out
|
|
|
|
x = constant_op.constant(
|
|
math_ops.range(6, dtype=dtypes.float32), shape=[6, 1])
|
|
|
|
strategy = self._create_strategy(2)
|
|
with strategy.scope():
|
|
layer = Dense()
|
|
expect = layer(x)
|
|
|
|
model_dir = self.get_temp_dir()
|
|
save.save(layer, model_dir)
|
|
|
|
strategy2 = self._create_strategy(3)
|
|
with strategy2.scope():
|
|
loaded_layer = load.load(model_dir)
|
|
# Should fail with informative error
|
|
with self.assertRaisesRegex(ValueError, 'run a loaded non-Keras'):
|
|
got = loaded_layer(x)
|
|
|
|
# Loading without a strategy should work, because the tf.function is traced
|
|
# with a single variable as input
|
|
loaded_layer = load.load(model_dir)
|
|
got = loaded_layer(x)
|
|
self.assertAllClose(got, expect)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
v2_compat.enable_v2_behavior()
|
|
test.main()
|