| /external/tensorflow/tensorflow/python/distribute/ |
| D | strategy_gather_test.py | 15 """Tests for common methods in strategy classes."""
43 strategy=[
60 strategy=[
71 strategy): argument
72 distributed_values = strategy.experimental_distribute_values_from_function(
76 return strategy.gather(distributed_values, axis=axis)
82 value_on_replica for _ in range(strategy.num_replicas_in_sync)
87 def testGatherPerReplicaDense1D0Axis(self, strategy, pure_eager): argument
91 self._gather_same_shape_and_verify(single_value, axis, pure_eager, strategy)
93 def testGatherPerReplicaDense2D0Axis(self, strategy, pure_eager): argument
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| D | strategy_common_test.py | 15 """Tests for common methods in strategy classes."""
42 strategy=[
49 def testCaptureReplicaId(self, strategy): argument
61 return strategy.run(f)
65 def testMergeCallInitScope(self, strategy): argument
66 with strategy.scope():
83 return strategy.run(replica_fn)
85 result = strategy.experimental_local_results(fn())
86 self.assertAllClose(result, [12] * _get_num_replicas_per_client(strategy))
217 strategy=[
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| D | strategy_combinations_test.py | 40 strategy=strategy_combinations.two_replica_strategies,
42 def testTwoReplicaStrategy(self, strategy): argument
43 with strategy.scope():
49 one_per_replica = strategy.run(one)
50 num_replicas = strategy.reduce(
56 strategy=strategy_combinations.four_replica_strategies,
58 def testFourReplicaStrategy(self, strategy): argument
59 with strategy.scope():
65 one_per_replica = strategy.run(one)
66 num_replicas = strategy.reduce(
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| D | tpu_strategy_test.py | 85 strategy = tpu_lib.TPUStrategyV2(resolver)
86 strategy._enable_packed_variable_in_eager_mode = enable_packed_var
87 return strategy
259 strategy = get_tpu_strategy(enable_packed_var)
260 with strategy.scope():
273 strategy = get_tpu_strategy(enable_packed_var)
275 with strategy.scope():
300 strategy.experimental_distribute_datasets_from_function(dataset_fn))
310 strategy.run(step_fn, args=(next(iterator),))
322 strategy = get_tpu_strategy(enable_packed_var)
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| D | distribution_strategy_context.py | 15 """Utility to get tf.distribute.Strategy related contexts."""
34 # replica or cross-replica context for a particular tf.distribute.Strategy.
40 self.strategy = dist
47 def __init__(self, strategy): argument
48 _ThreadMode.__init__(self, strategy, strategy, None)
54 _ThreadMode.__init__(self, replica_ctx.strategy, None, replica_ctx)
105 strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
106 with strategy.scope():
113 non_aggregated = strategy.run(replica_fn)
120 aggregated = strategy.run(replica_fn)
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| D | strategy_test_lib.py | 141 def is_mirrored_strategy(strategy: distribute_lib.Strategy) -> bool: argument
143 strategy,
148 strategy: distribute_lib.Strategy) -> bool: argument
149 return isinstance(strategy, (mwms_lib.CollectiveAllReduceStrategy,
153 def is_tpu_strategy(strategy: distribute_lib.Strategy) -> bool: argument
154 return isinstance(strategy,
358 self, strategy, input_fn, expected_values, ignore_order=False): argument
361 iterable = strategy.distribute_datasets_from_function(input_fn)
367 list(strategy.experimental_local_results(next(iterator))))
371 self.evaluate(strategy.experimental_local_results(next(iterator)))
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| D | tpu_strategy_model_parallelism_test.py | 68 strategy = tpu_lib.TPUStrategyV2(
72 return strategy, num_replicas
82 strategy, num_replicas = get_tpu_strategy()
83 with strategy.scope():
85 with strategy.extended.experimental_logical_device(1):
88 self.assertLen(strategy.experimental_local_results(v), num_replicas)
89 self.assertLen(strategy.experimental_local_results(w), num_replicas)
91 strategy.experimental_local_results(v)[0].device)
93 strategy.experimental_local_results(w)[0].device)
107 result = strategy.run(f, args=(5.,))
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | distributed_file_utils.py | 53 def _get_base_dirpath(strategy): argument
54 task_id = strategy.extended._task_id # pylint: disable=protected-access
58 def _is_temp_dir(dirpath, strategy): argument
59 return dirpath.endswith(_get_base_dirpath(strategy))
62 def _get_temp_dir(dirpath, strategy): argument
63 if _is_temp_dir(dirpath, strategy):
66 temp_dir = os.path.join(dirpath, _get_base_dirpath(strategy))
71 def write_dirpath(dirpath, strategy): argument
78 strategy: The tf.distribute strategy object currently used.
83 if strategy is None:
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| D | dataset_creator_model_fit_test.py | 40 strategy=strategy_combinations.all_strategies +
52 def testModelFit(self, strategy): argument
53 model = self._model_fit(strategy)
56 def testModelFitwithStepsPerEpochNegativeOne(self, strategy): argument
64 if strategy._should_use_with_coordinator:
67 strategy,
74 strategy,
80 def testModelFitWithNumpyData(self, strategy): argument
84 strategy,
92 def testModelFitWithTensorData(self, strategy): argument
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| D | dataset_creator_model_fit_ps_only_test.py | 31 strategy=strategy_combinations.parameter_server_strategies_multi_worker,
36 def testModelFitWithRunEagerly(self, strategy): argument
40 self._model_fit(strategy, run_eagerly=True)
42 def testModelFitWithDatasetInstance(self, strategy): argument
49 strategy, x=dataset_ops.DatasetV2.from_tensor_slices([1, 1]))
51 def testModelPredict(self, strategy): argument
52 model, _ = self._model_compile(strategy)
57 def testClusterCoordinatorSingleInstance(self, strategy): argument
58 model = self._model_fit(strategy)
59 strategy = model.distribute_strategy
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_outside_compilation_test.py | 133 strategy = get_tpu_strategy()
146 return strategy.run(tpu_fn, args=(25.0,))
149 strategy.experimental_local_results(train_step()),
150 constant_op.constant(35., shape=(strategy.num_replicas_in_sync)))
153 strategy = get_tpu_strategy()
166 return strategy.run(tpu_fn, args=(25.0,))
169 strategy.experimental_local_results(train_step()),
170 constant_op.constant(35., shape=(strategy.num_replicas_in_sync)))
173 strategy = get_tpu_strategy()
187 return strategy.run(tpu_fn, args=(25.0,))
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| /external/jacoco/org.jacoco.core.test/src/org/jacoco/core/internal/instr/ |
| D | ProbeArrayStrategyFactoryTest.java | 52 final IProbeArrayStrategy strategy = test(Opcodes.V1_1, 0, false, true, in testClass1() local
54 assertEquals(ClassFieldProbeArrayStrategy.class, strategy.getClass()); in testClass1()
61 final IProbeArrayStrategy strategy = test(Opcodes.V1_2, 0, false, true, in testClass2() local
63 assertEquals(ClassFieldProbeArrayStrategy.class, strategy.getClass()); in testClass2()
70 final IProbeArrayStrategy strategy = test(Opcodes.V1_3, 0, false, true, in testClass3() local
72 assertEquals(ClassFieldProbeArrayStrategy.class, strategy.getClass()); in testClass3()
79 final IProbeArrayStrategy strategy = test(Opcodes.V1_4, 0, false, true, in testClass4() local
81 assertEquals(ClassFieldProbeArrayStrategy.class, strategy.getClass()); in testClass4()
88 final IProbeArrayStrategy strategy = test(Opcodes.V1_5, 0, false, true, in testClass5() local
90 assertEquals(ClassFieldProbeArrayStrategy.class, strategy.getClass()); in testClass5()
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| /external/tensorflow/tensorflow/python/distribute/integration_test/ |
| D | saved_model_test.py | 55 strategy=[
69 def test_read_sync_on_read_variable(self, strategy): argument
90 with strategy.scope():
100 strategy=[
109 # tf.distribute.Strategy and used for serving later. Serving usually only uses
110 # one device and this is simulated by loading the model under no strategy
115 # tf.distribute.Strategy. The saved tf.function should be an inference
128 def test_read_sync_on_read_variable(self, strategy): argument
145 with strategy.scope():
151 self.evaluate(strategy.experimental_local_results(m.v)), [0.5, 0.5])
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| /external/ComputeLibrary/src/core/NEON/kernels/arm_gemm/ |
| D | gemm_interleaved.hpp | 65 template<typename strategy, typename To, typename Tr, typename Tri, typename Tab>
70 strategy &strat, const To *a_ptr, const To *b_panel, size_t b_stride, Tri *c_panel,
79 template<typename strategy, typename To, typename Tr, typename Tri, typename Tab>
84 strategy &strat, const To *a_ptr, const To *b_panel, size_t, Tri *c_panel, in run()
89 const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); in run()
93 …auto p=prof.ScopedProfiler(PROFILE_KERNEL, (strategy::out_height() * bblocks * strategy::out_width… in run()
101 …auto p=prof.ScopedProfiler(PROFILE_MERGE, (strategy::out_height() * bblocks * strategy::out_width(… in run()
109 template<typename strategy, typename To, typename Tr, typename Tri, typename Tab>
114 strategy &strat, const To *a_ptr, const To *b_panel, size_t b_stride, Tri *c_panel, in run()
121 const int bblocks = iceildiv(n_max - n_0, strategy::out_width()); in run()
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| D | gemm_hybrid_indirect.hpp | 61 template<typename strategy, typename Tlo, typename Tro, typename Tr>
66 …const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<…
72 template<typename strategy, typename Tlo, typename Tro, typename Tr>
77 …const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<… in run()
81 …auto p = prof.ScopedProfiler(PROFILE_KERNEL, (unsigned long)M * kern_k * roundup(N, strategy::out_… in run()
87 if (bias_ptr && !accumulate && (N % strategy::out_width() != 0)) { in run()
89 unsigned int N_remainder = N % strategy::out_width(); in run()
107 Tr *bias_pad_buffer = reinterpret_cast<Tr *>(alloca(strategy::out_width() * sizeof(Tr))); in run()
118 template<typename strategy, typename Tlo, typename Tro, typename Tr>
123 …const strategy &strat, unsigned int num_strings, const unsigned int *string_ptr, IndirectInputArg<… in run()
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| D | gemm_interleaved_pretransposed_2d.hpp | 51 template<typename strategy, typename To, typename Tr>
53 typedef typename strategy::operand_type Toi;
54 typedef typename strategy::result_type Tri;
89 const GemmInterleavedPretransposed2d<strategy, To, Tr> &_parent;
100 blockwalker(const GemmInterleavedPretransposed2d<strategy, To, Tr> &parent) in blockwalker() argument
105 …blockwalker(const GemmInterleavedPretransposed2d<strategy, To, Tr> &parent, unsigned int x0, unsig… in blockwalker() argument
169 return ROUND_UP(sizeof(Tri) * _x_block * strategy::out_height()); in get_c_working_size()
184 strategy strat(_ci); in execute_pretranspose()
187 const unsigned int window_per_batch = _Mround / strategy::out_height(); in execute_pretranspose()
192 unsigned int m_0 = (m_start - (batch_0 * window_per_batch)) * strategy::out_height(); in execute_pretranspose()
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| /external/tensorflow/tensorflow/python/tpu/tests/ |
| D | tpu_embedding_v2_valid_input_test.py | 39 strategy, mid_level_api, _ = self._create_strategy_and_mid_level('sgd')
45 dataset = self._create_sparse_dataset(strategy)
48 strategy.experimental_distribute_dataset(
61 return strategy.run(tpu_fn)
68 return strategy.run(tpu_fn)
71 embedding_and_set_gradients(data), strategy, 0)
72 second = self._get_replica_numpy(embedding_only(data), strategy, 0)
85 num_replicas = strategy.num_replicas_in_sync
94 strategy, mid_level_api, _ = self._create_strategy_and_mid_level('sgd')
96 sparse = self._create_sparse_dataset(strategy)
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| D | tpu_embedding_v2_invalid_input_test.py | 53 strategy, mid_level_api, _ = self._create_strategy_and_mid_level('sgd')
64 strategy.run(test_apply_1)
74 strategy.run(test_apply_2)
77 strategy, mid_level_api, _ = self._create_strategy_and_mid_level('sgd')
79 dataset = self._create_dense_dataset(strategy, include_weights=True)
81 strategy.experimental_distribute_dataset(
93 return strategy.run(step)
100 strategy, mid_level_api, _ = self._create_strategy_and_mid_level('sgd')
102 sparse = self._create_sparse_dataset(strategy, include_weights=True)
103 ragged = self._create_ragged_dataset(strategy, include_weights=True)
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| /external/ComputeLibrary/examples/gemm_tuner/ |
| D | GemmTuner.py | 40 # Gemm strategy
41 Strategy = Enum("Strategy", ["Native", "ReshapedOnlyRHS", "Reshaped"]) variable
61 # Gemm configuration for strategy Native
76 # Gemm configuration for strategy Reshaped Only RHS
102 # Gemm configuration for strategy Reshaped
196 strategy: Strategy
218 gemm_param, strategy, gemm_config, measurement = benchmark_result
220 self._strategies.add(strategy)
230 """ Get the best GEMMConfig set per GEMMParam per Strategy
233 Tuple[GEMMParam, Strategy], List[Tuple[GEMMConfig, Measurement]]
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| /external/gson/gson/src/test/java/com/google/gson/ |
| D | ToNumberPolicyTest.java | 29 ToNumberStrategy strategy = ToNumberPolicy.DOUBLE; in testDouble() local
30 assertEquals(10.1, strategy.readNumber(fromString("10.1"))); in testDouble()
31 …assertEquals(3.141592653589793D, strategy.readNumber(fromString("3.141592653589793238462643383279"… in testDouble()
33 strategy.readNumber(fromString("1e400")); in testDouble()
39 strategy.readNumber(fromString("\"not-a-number\"")); in testDouble()
46 ToNumberStrategy strategy = ToNumberPolicy.LAZILY_PARSED_NUMBER; in testLazilyParsedNumber() local
47 assertEquals(new LazilyParsedNumber("10.1"), strategy.readNumber(fromString("10.1"))); in testLazilyParsedNumber()
48 …assertEquals(new LazilyParsedNumber("3.141592653589793238462643383279"), strategy.readNumber(fromS… in testLazilyParsedNumber()
49 assertEquals(new LazilyParsedNumber("1e400"), strategy.readNumber(fromString("1e400"))); in testLazilyParsedNumber()
53 ToNumberStrategy strategy = ToNumberPolicy.LONG_OR_DOUBLE; in testLongOrDouble() local
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| /external/objenesis/main/src/main/java/org/objenesis/ |
| D | ObjenesisBase.java | 19 import org.objenesis.strategy.InstantiatorStrategy;
24 * Base class to extend if you want to have a class providing your own default strategy. Can also be
31 /** Strategy used by this Objenesi implementation to create classes */
32 protected final InstantiatorStrategy strategy; field in ObjenesisBase
34 /** Strategy cache. Key = Class, Value = InstantiatorStrategy */
38 * Constructor allowing to pick a strategy and using cache
40 * @param strategy Strategy to use
42 public ObjenesisBase(InstantiatorStrategy strategy) { in ObjenesisBase() argument
43 this(strategy, true); in ObjenesisBase()
47 * Flexible constructor allowing to pick the strategy and if caching should be used
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| /external/squashfs-tools/squashfs-tools/ |
| D | gzip_wrapper.c | 33 static struct strategy strategy[] = { variable
112 for(i = 0; strategy[i].name; i++) { in gzip_options()
113 int n = strlen(strategy[i].name); in gzip_options()
114 if((strncmp(name, strategy[i].name, n) == 0) && in gzip_options()
117 if(strategy[i].selected == 0) { in gzip_options()
118 strategy[i].selected = 1; in gzip_options()
125 if(strategy[i].name == NULL) { in gzip_options()
127 "strategy\n"); in gzip_options()
152 if(strategy_count == 1 && strategy[0].selected) { in gzip_options_post()
154 strategy[0].selected = 0; in gzip_options_post()
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| /external/python/google-api-python-client/docs/dyn/ |
| D | dfareporting_v3_5.placementStrategies.html | 82 <p class="firstline">Deletes an existing placement strategy.</p>
85 <p class="firstline">Gets one placement strategy by ID.</p>
88 <p class="firstline">Inserts a new placement strategy.</p>
97 <p class="firstline">Updates an existing placement strategy. This method supports patch semantics.<…
100 <p class="firstline">Updates an existing placement strategy.</p>
109 <pre>Deletes an existing placement strategy.
113 id: string, Placement strategy ID. (required)
123 <pre>Gets one placement strategy by ID.
127 id: string, Placement strategy ID. (required)
136 { # Contains properties of a placement strategy.
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| D | dfareporting_v3_4.placementStrategies.html | 82 <p class="firstline">Deletes an existing placement strategy.</p>
85 <p class="firstline">Gets one placement strategy by ID.</p>
88 <p class="firstline">Inserts a new placement strategy.</p>
97 <p class="firstline">Updates an existing placement strategy. This method supports patch semantics.<…
100 <p class="firstline">Updates an existing placement strategy.</p>
109 <pre>Deletes an existing placement strategy.
113 id: string, Placement strategy ID. (required)
123 <pre>Gets one placement strategy by ID.
127 id: string, Placement strategy ID. (required)
136 { # Contains properties of a placement strategy.
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| D | dfareporting_v3_3.placementStrategies.html | 82 <p class="firstline">Deletes an existing placement strategy.</p>
85 <p class="firstline">Gets one placement strategy by ID.</p>
88 <p class="firstline">Inserts a new placement strategy.</p>
97 <p class="firstline">Updates an existing placement strategy. This method supports patch semantics.<…
100 <p class="firstline">Updates an existing placement strategy.</p>
109 <pre>Deletes an existing placement strategy.
113 id: string, Placement strategy ID. (required)
123 <pre>Gets one placement strategy by ID.
127 id: string, Placement strategy ID. (required)
136 { # Contains properties of a placement strategy.
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