| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Tests for ShardedVariable.""" |
| |
| import os |
| |
| from absl.testing import parameterized |
| import numpy as np |
| from tensorflow.python.checkpoint import checkpoint as util |
| from tensorflow.python.client import session as session_lib |
| from tensorflow.python.compat import v2_compat |
| from tensorflow.python.distribute import combinations |
| from tensorflow.python.distribute import distribute_lib |
| from tensorflow.python.distribute import parameter_server_strategy_v2 |
| from tensorflow.python.distribute import sharded_variable |
| from tensorflow.python.distribute.cluster_resolver import cluster_resolver as cluster_resolver_lib |
| from tensorflow.python.distribute.test_util import get_cluster_def |
| from tensorflow.python.distribute.test_util import TestClusterParams |
| from tensorflow.python.eager import context |
| from tensorflow.python.eager import def_function |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import indexed_slices |
| from tensorflow.python.framework import ops |
| from tensorflow.python.framework import sparse_tensor |
| from tensorflow.python.framework import tensor_shape |
| from tensorflow.python.framework import tensor_spec |
| from tensorflow.python.module import module |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import control_flow_ops |
| from tensorflow.python.ops import embedding_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import random_ops |
| from tensorflow.python.ops import variables as variables_lib |
| from tensorflow.python.platform import test |
| from tensorflow.python.saved_model import load |
| from tensorflow.python.saved_model import loader |
| from tensorflow.python.saved_model import save |
| from tensorflow.python.saved_model import signature_constants |
| from tensorflow.python.saved_model import tag_constants |
| from tensorflow.python.trackable import autotrackable |
| from tensorflow.python.training import server_lib |
| from tensorflow.python.util import nest |
| |
| # We create one cluster to share between tests. The cluster should be large |
| # enough to accommodate all the tests. Adjust the following constants as needed |
| # but be aware of resource limitations in OSS tests. |
| test_cluster_params = TestClusterParams(None, 2, 3) |
| |
| |
| def _load_and_run( |
| model_dir, |
| inputs, |
| signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY): |
| """Load a SavedModel into a TF 1.x-style graph and run `signature_key`.""" |
| graph = ops.Graph() |
| with graph.as_default(), session_lib.Session() as session: |
| meta_graph_def = loader.load(session, [tag_constants.SERVING], model_dir) |
| signature = meta_graph_def.signature_def[signature_key] |
| feed_dict = {} |
| for arg_name in inputs.keys(): |
| input_tensor = session.graph.get_tensor_by_name( |
| signature.inputs[arg_name].name) |
| feed_dict[input_tensor] = inputs[arg_name] |
| output_dict = {} |
| for output_name, output_tensor_info in signature.outputs.items(): |
| output_dict[output_name] = session.graph.get_tensor_by_name( |
| output_tensor_info.name) |
| return session.run(output_dict, feed_dict=feed_dict) |
| |
| |
| class PartitionerTest(test.TestCase): |
| |
| def test_fixed_shards_partitioner(self): |
| partitioner = sharded_variable.FixedShardsPartitioner(num_shards=2) |
| got = partitioner(tensor_shape.TensorShape([10, 3]), dtypes.float32) |
| self.assertAllEqual(got, [2, 1]) |
| |
| def test_min_size_partitioner(self): |
| partitioner = sharded_variable.MinSizePartitioner( |
| min_shard_bytes=4, max_shards=2) |
| got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32) |
| self.assertAllEqual(got, [2, 1]) |
| |
| partitioner = sharded_variable.MinSizePartitioner( |
| min_shard_bytes=4, max_shards=10) |
| got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32) |
| self.assertAllEqual(got, [6, 1]) |
| |
| def test_max_size_partitioner(self): |
| partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=4) |
| got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32) |
| self.assertAllEqual(got, [6, 1]) |
| |
| partitioner = sharded_variable.MaxSizePartitioner( |
| max_shard_bytes=4, max_shards=2) |
| got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32) |
| self.assertAllEqual(got, [2, 1]) |
| |
| partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=1024) |
| got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32) |
| self.assertAllEqual(got, [1, 1]) |
| |
| |
| class ShardedVariableTest(test.TestCase, parameterized.TestCase): |
| |
| def test_sharded_variable_simple(self): |
| v0 = variables_lib.Variable([0]) |
| v1 = variables_lib.Variable([1]) |
| s = sharded_variable.ShardedVariable([v0, v1], name='s') |
| self.assertEqual(s.variables[0], v0) |
| self.assertEqual(s.variables[1], v1) |
| self.assertEqual(s.shape.as_list(), [2]) |
| self.assertEqual(s.dtype, v0.dtype) |
| self.assertEqual(s.name, 's') |
| |
| def test_assign(self): |
| v0 = variables_lib.Variable([[0, 0]]) |
| v1 = variables_lib.Variable([[1, 1], [2, 2]]) |
| v2 = variables_lib.Variable([[3, 3]]) |
| s = sharded_variable.ShardedVariable([v0, v1, v2]) |
| ret = s.assign([[4, 4], [5, 5], [6, 6], [7, 7]]) |
| self.assertAllEqual(self.evaluate(s.variables[0]), [[4, 4]]) |
| self.assertAllEqual(self.evaluate(s.variables[1]), [[5, 5], [6, 6]]) |
| self.assertAllEqual(self.evaluate(s.variables[2]), [[7, 7]]) |
| self.assertIs(ret, s) |
| |
| def test_assign_add(self): |
| v0 = variables_lib.Variable([[0, 0]]) |
| v1 = variables_lib.Variable([[1, 1], [2, 2]]) |
| v2 = variables_lib.Variable([[3, 3]]) |
| s = sharded_variable.ShardedVariable([v0, v1, v2]) |
| ret = s.assign_add([[1, 1], [1, 1], [2, 2], [2, 2]]) |
| self.assertAllEqual(self.evaluate(s.variables[0]), [[1, 1]]) |
| self.assertAllEqual(self.evaluate(s.variables[1]), [[2, 2], [4, 4]]) |
| self.assertAllEqual(self.evaluate(s.variables[2]), [[5, 5]]) |
| self.assertIs(ret, s) |
| |
| def test_assign_sub(self): |
| v0 = variables_lib.Variable([[0, 0]]) |
| v1 = variables_lib.Variable([[1, 1], [2, 2]]) |
| v2 = variables_lib.Variable([[3, 3]]) |
| s = sharded_variable.ShardedVariable([v0, v1, v2]) |
| ret = s.assign_sub([[0, 0], [1, 1], [1, 1], [3, 3]]) |
| self.assertAllEqual(self.evaluate(s.variables[0]), [[0, 0]]) |
| self.assertAllEqual(self.evaluate(s.variables[1]), [[0, 0], [1, 1]]) |
| self.assertAllEqual(self.evaluate(s.variables[2]), [[0, 0]]) |
| self.assertIs(ret, s) |
| |
| def test_scatter_add_uneven_partition(self): |
| v = variables_lib.Variable(array_ops.zeros((32, 1))) |
| sparse_delta = indexed_slices.IndexedSlices( |
| values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]), |
| indices=constant_op.constant([0, 10, 11, 12, 30, 31])) |
| |
| v0 = variables_lib.Variable(array_ops.zeros((11, 1))) |
| v1 = variables_lib.Variable(array_ops.zeros((11, 1))) |
| v2 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| sv = sharded_variable.ShardedVariable([v0, v1, v2]) |
| |
| v.scatter_add(sparse_delta) |
| sv.scatter_add(sparse_delta) |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| @def_function.function |
| def func(): |
| v.scatter_add(sparse_delta) |
| sv.scatter_add(sparse_delta) |
| |
| func() |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| @parameterized.parameters('scatter_add', 'scatter_div', 'scatter_max', |
| 'scatter_min', 'scatter_mul', 'scatter_sub', |
| 'scatter_update') |
| def test_scatter_ops_even_partition(self, op): |
| v = variables_lib.Variable(array_ops.zeros((30, 1))) |
| # Make sure values does not contain 0 due to testing `scatter_div`! |
| sparse_delta = indexed_slices.IndexedSlices( |
| values=constant_op.constant([[1.], [2.], [3.], [4.], [5.]]), |
| indices=constant_op.constant([0, 10, 12, 21, 22])) |
| |
| v0 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| v1 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| v2 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| sv = sharded_variable.ShardedVariable([v0, v1, v2]) |
| |
| getattr(v, op)(sparse_delta, name='scatter_v') |
| getattr(sv, op)(sparse_delta, name='scatter_sv') |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| @def_function.function |
| def func(): |
| getattr(v, op)(sparse_delta, name='scatter_v') |
| getattr(sv, op)(sparse_delta, name='scatter_sv') |
| |
| func() |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| def test_batch_scatter_update(self): |
| v = variables_lib.Variable(array_ops.zeros((32, 1))) |
| sparse_delta = indexed_slices.IndexedSlices( |
| values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]), |
| indices=constant_op.constant([10, 11, 12, 13, 14, 15])) |
| |
| v0 = variables_lib.Variable(array_ops.zeros((11, 1))) |
| v1 = variables_lib.Variable(array_ops.zeros((11, 1))) |
| v2 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| sv = sharded_variable.ShardedVariable([v0, v1, v2]) |
| |
| v.batch_scatter_update(sparse_delta) |
| sv.batch_scatter_update(sparse_delta) |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| @def_function.function |
| def func(): |
| v.batch_scatter_update(sparse_delta) |
| sv.batch_scatter_update(sparse_delta) |
| |
| func() |
| self.assertAllEqual(v, ops.convert_to_tensor(sv)) |
| |
| def test_sparse_read(self): |
| v = variables_lib.Variable(array_ops.zeros((30, 1))) |
| indices = constant_op.constant([0, 10, 12, 21, 22]) |
| |
| v0 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| v1 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| v2 = variables_lib.Variable(array_ops.zeros((10, 1))) |
| sv = sharded_variable.ShardedVariable([v0, v1, v2]) |
| |
| self.assertAllEqual(v.sparse_read(indices), sv.sparse_read(indices)) |
| |
| @def_function.function |
| def func(): |
| return v.sparse_read(indices), sv.sparse_read(indices) |
| |
| got, expect = func() |
| self.assertAllEqual(got, expect) |
| |
| def test_control_dep_on_assign(self): |
| v0 = variables_lib.Variable([[0, 0]]) |
| v1 = variables_lib.Variable([[1, 1], [2, 2]]) |
| v2 = variables_lib.Variable([[3, 3]]) |
| s = sharded_variable.ShardedVariable([v0, v1, v2]) |
| |
| @def_function.function |
| def func(): |
| ret = s.assign([[4, 4], [5, 5], [6, 6], [7, 7]]) |
| with ops.control_dependencies([ret]): |
| a = array_ops.ones((1, 1)) |
| with ops.control_dependencies([control_flow_ops.group(ret)]): |
| b = array_ops.ones((1, 1)) |
| return a, b |
| |
| func() |
| |
| def test_convert_to_tensor(self): |
| v0 = variables_lib.Variable([[0, 0]]) |
| v1 = variables_lib.Variable([[1, 1], [2, 2]]) |
| v2 = variables_lib.Variable([[3, 3]]) |
| s = sharded_variable.ShardedVariable([v0, v1, v2]) |
| t = ops.convert_to_tensor(s) |
| self.assertAllEqual(t, [[0, 0], [1, 1], [2, 2], [3, 3]]) |
| |
| def test_save_restore(self): |
| fname = os.path.join(self.get_temp_dir(), 'checkpoint') |
| variables = [ |
| variables_lib.Variable([0]), |
| variables_lib.Variable([1]), |
| variables_lib.Variable([2]), |
| variables_lib.Variable([3]) |
| ] |
| s = sharded_variable.ShardedVariable(variables, name='s') |
| |
| cp = util.Checkpoint(s=s) |
| self.assertEqual(self.evaluate(cp.s.variables[0]), [0]) |
| cp.write(fname) |
| |
| self.evaluate(cp.s.variables[0].assign([4])) |
| self.assertEqual(self.evaluate(cp.s.variables[0]), [4]) |
| |
| cp.restore(fname) |
| # Tests that the original weights are restored. |
| self.assertEqual(self.evaluate(cp.s.variables[0]), [0]) |
| |
| def test_save_restore_different_partitions(self): |
| fname = os.path.join(self.get_temp_dir(), 'checkpoint') |
| variables = [ |
| variables_lib.Variable([0]), |
| variables_lib.Variable([1]), |
| variables_lib.Variable([2]), |
| variables_lib.Variable([3]) |
| ] |
| s = sharded_variable.ShardedVariable(variables, name='s') |
| |
| cp = util.Checkpoint(s=s) |
| cp.write(fname) |
| |
| variables2 = [variables_lib.Variable([0, 0, 0, 0])] |
| s2 = sharded_variable.ShardedVariable(variables2, name='s') |
| |
| # Restore from 4 partitions into 1. |
| cp2 = util.Checkpoint(s=s2) |
| cp2.restore(fname) |
| self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1, 2, 3]) |
| |
| self.evaluate(cp2.s.variables[0].assign([5, 10, 15, 20])) |
| cp2.write(fname) |
| |
| # Restore 1 partition into 4. |
| cp.restore(fname) |
| self.assertEqual(self.evaluate(cp.s.variables[0]), [5]) |
| self.assertEqual(self.evaluate(cp.s.variables[1]), [10]) |
| self.assertEqual(self.evaluate(cp.s.variables[2]), [15]) |
| self.assertEqual(self.evaluate(cp.s.variables[3]), [20]) |
| |
| def test_save_restore_4_to_2_partitions(self): |
| fname = os.path.join(self.get_temp_dir(), 'checkpoint') |
| variables = [ |
| variables_lib.Variable([0]), |
| variables_lib.Variable([1]), |
| variables_lib.Variable([2]), |
| variables_lib.Variable([3]) |
| ] |
| s = sharded_variable.ShardedVariable(variables, name='s') |
| cp = util.Checkpoint(s=s) |
| cp.write(fname) |
| |
| variables2 = [ |
| variables_lib.Variable([0, 0]), |
| variables_lib.Variable([0, 0]) |
| ] |
| s2 = sharded_variable.ShardedVariable(variables2, name='s') |
| cp2 = util.Checkpoint(s=s2) |
| cp2.restore(fname) |
| # Assert that weights from the 4 partitions were loaded here. |
| self.assertLen(cp2.s.variables, 2) |
| self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1]) |
| self.assertAllEqual(self.evaluate(cp2.s.variables[1]), [2, 3]) |
| |
| def test_delayed_restore(self): |
| fname = os.path.join(self.get_temp_dir(), 'checkpoint') |
| model = autotrackable.AutoTrackable() |
| variables = [ |
| variables_lib.Variable([0]), |
| variables_lib.Variable([1]), |
| 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]), |
| variables_lib.Variable([0]), |
| variables_lib.Variable([0]), |
| variables_lib.Variable([0]) |
| ] |
| model2.s = sharded_variable.ShardedVariable(variables2) |
| self.assertAllEqual(self.evaluate(model2.s.variables[0]), [0]) |
| self.assertAllEqual(self.evaluate(model2.s.variables[1]), [1]) |
| self.assertAllEqual(self.evaluate(model2.s.variables[2]), [2]) |
| self.assertAllEqual(self.evaluate(model2.s.variables[3]), [3]) |
| |
| def test_delayed_restore_4_to_2_partitions(self): |
| fname = os.path.join(self.get_temp_dir(), 'checkpoint') |
| model = autotrackable.AutoTrackable() |
| variables = [ |
| variables_lib.Variable([0]), |
| variables_lib.Variable([1]), |
| 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): |
| v = [ |
| variables_lib.Variable([[1, 2], [3, 4], [5, 6]]), |
| variables_lib.Variable([[7, 8], [9, 10], [11, 12]]), |
| variables_lib.Variable([[13, 14], [15, 16]]) |
| ] |
| sv = sharded_variable.ShardedVariable(v) |
| empty = v[0][0:0] |
| |
| # 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)) |
| |
| # 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() |
