| /external/tensorflow/tensorflow/compiler/xla/service/ |
| hlo_sharding_test.cc | 44 HloSharding sharding = HloSharding::Replicate(); local
45 EXPECT_TRUE(sharding.IsReplicated());
46 EXPECT_TRUE(sharding.IsTileMaximal());
47 EXPECT_TRUE(sharding.UsesDevice(0));
48 EXPECT_TRUE(sharding.UsesDevice(65535));
51 EXPECT_EQ(other, sharding);
53 EXPECT_IS_OK(sharding.Validate(ShapeUtil::MakeShape(U32, {4}),
55 EXPECT_IS_NOT_OK(sharding.UniqueDevice());
59 HloSharding sharding = HloSharding::AssignDevice(5); local
60 EXPECT_FALSE(sharding.IsReplicated())
84 HloSharding sharding = local
93 HloSharding sharding = local
102 HloSharding sharding = local
112 HloSharding sharding = local
121 HloSharding sharding = local
[all...] |
| hlo_tfgraph_builder.cc | 106 instruction->sharding().HasUniqueDevice()) {
108 "dev", instruction->sharding().UniqueDevice().ConsumeValueOrDie());
219 instruction->sharding().HasUniqueDevice()) {
220 TF_ASSIGN_OR_RETURN(int64 device, instruction->sharding().UniqueDevice());
|
| user_computation_test.cc | 230 OpSharding sharding; local
231 sharding.set_type(OpSharding::Type::OpSharding_Type_MAXIMAL);
232 sharding.add_tile_assignment_dimensions(1);
233 sharding.add_tile_assignment_devices(kDevice);
235 TF_EXPECT_OK(computation.SetOpSharding(b_handle, sharding));
|
| batchnorm_expander.cc | 290 batch_norm->sharding().GetAsShapeTree(batch_norm->shape()).element({0});
298 tuple->set_sharding(batch_norm->sharding());
390 inst->set_sharding(batch_norm->sharding());
395 shifted_normalized->set_sharding(batch_norm->sharding());
584 batch_norm->sharding().GetAsShapeTree(batch_norm->shape()).element({0});
592 tuple->set_sharding(batch_norm->sharding());
|
| hlo_sharding.cc | 152 "UniqueDevice() called on sharding that executes on multiple devices");
166 StrCat("Sharding is tuple-shaped but validation shape is not."));
178 " leaf elements, but this sharding contains ",
190 &status, StrCat("Note: While validating sharding tuple element ",
204 &status, StrCat("Note: While validating sharding ", ToString(),
214 StrCat("Validation shape is a tuple but sharding is not."));
249 "Tile rank is different to the input rank. sharding=", ToString(),
258 "Tile shape is the same as the input shape. If a replicated sharding "
296 TF_ASSIGN_OR_RETURN(HloSharding sharding,
298 tuple_shardings.push_back(sharding);
[all...] |
| user_computation.h | 295 const OpSharding& sharding);
|
| hlo_instruction.h | 921 const HloSharding& sharding() const { function in class:xla::HloInstruction
[all...] |
| hlo_instruction.cc | 690 broadcast->set_sharding(operand->sharding());
715 reshaped_operand->set_sharding(operand->sharding());
722 broadcast->set_sharding(operand->sharding());
[all...] |
| hlo_graph_dumper.cc | 419 // When coloring by sharding information, we track the sharding string
853 string shard_str = instr->sharding().ToString();
[all...] |
| user_computation.cc | [all...] |
| service.cc | [all...] |
| /external/tensorflow/tensorflow/compiler/tf2xla/ |
| sharding_util_test.cc | 26 [](tensorflow::gtl::optional<xla::OpSharding> sharding) -> int64 {
27 if (sharding.has_value() &&
28 sharding.value().type() ==
30 return sharding.value().tile_assignment_devices(0);
|
| sharding_util.cc | 35 xla::OpSharding sharding; local
37 if (!sharding.ParseFromString(value)) {
42 return tensorflow::gtl::optional<xla::OpSharding>(sharding);
84 TF_ASSIGN_OR_RETURN(tensorflow::gtl::optional<xla::OpSharding> sharding,
86 return ParseShardingFromDevice(device_name, num_cores_per_replica, sharding);
95 TF_ASSIGN_OR_RETURN(tensorflow::gtl::optional<xla::OpSharding> sharding,
97 return ParseShardingFromDevice(device_name, num_cores_per_replica, sharding);
|
| tf2xla_util.cc | 263 tensorflow::gtl::optional<xla::OpSharding> sharding,
267 if (sharding.has_value()) {
268 TF_RET_CHECK(sharding.value().type() ==
270 const int core_annotation = sharding.value().tile_assignment_devices(0);
|
| xla_compiler.cc | 401 // Since we can't change the sharding metadata of <value> as this point,
402 // create a tuple/get-tuple-element combination so that sharding
505 auto sharding,
507 if (sharding.has_value()) {
508 TF_RET_CHECK(sharding.value().type() ==
510 const int core = sharding.value().tile_assignment_devices(0);
|
| /external/tensorflow/tensorflow/compiler/xla/tools/parser/ |
| hlo_parser_test.cc | 190 %v1 = f32[4]{0} parameter(0), sharding={maximal device=1}
191 %v2 = f32[4]{0} parameter(1), sharding={maximal device=1}
192 %greater-than = pred[4]{0} greater-than(f32[4]{0} %v1, f32[4]{0} %v2), sharding={replicated}
193 ROOT %select = f32[4]{0} select(pred[4]{0} %greater-than, f32[4]{0} %v1, f32[4]{0} %v2), sharding={}
231 ROOT %tuple = (f32[], f32[3]{0}, f32[2,3]{1,0}) tuple(f32[] %v1, f32[3]{0} %v2, f32[2,3]{1,0} %v3), sharding={{replicated}, {maximal device=0}, {replicated}}
267 %recv = (f32[], u32[]) recv(), channel_id=15, sharding={maximal device=1}
268 ROOT %recv-done = f32[] recv-done((f32[], u32[]) %recv), channel_id=15, sharding={maximal device=1}
269 %constant = f32[] constant(2.1), sharding={maximal device=0}
270 %send = (f32[], u32[]) send(f32[] %constant), channel_id=16, sharding={maximal device=0}, control-predecessors={%recv}
271 %send-done = () send-done((f32[], u32[]) %send), channel_id=16, sharding={maximal device=0
[all...] |
| hlo_parser.cc | 170 bool ParseSharding(OpSharding* sharding);
171 bool ParseSingleSharding(OpSharding* sharding, bool lbrace_pre_lexed);
432 optional<OpSharding> sharding; local
433 attrs["sharding"] = {/*required=*/false, AttrTy::kSharding, &sharding};
[all...] |
| /tools/tradefederation/core/src/com/android/tradefed/config/ |
| IGlobalConfiguration.java | 233 /** Sets the {@link IShardHelper} to be used when sharding a configuration. */
234 public void setShardingStrategy(IShardHelper sharding);
|
| GlobalConfiguration.java | 511 public void setShardingStrategy(IShardHelper sharding) {
512 setConfigurationObjectNoThrow(SHARDING_STRATEGY_TYPE_NAME, sharding);
|
| /external/tensorflow/tensorflow/compiler/xla/client/ |
| computation_builder.h | 77 void SetSharding(const OpSharding& sharding) { sharding_ = sharding; }
79 // Clears the sharding. Ops will be sharded according to the default placement
84 const tensorflow::gtl::optional<OpSharding>& sharding() const { function in class:xla::ComputationBuilder
[all...] |
| /tools/tradefederation/core/src/com/android/tradefed/invoker/ |
| TestInvocation.java | 650 mStatus = "sharding";
651 boolean sharding = invocationPath.shardConfig(config, context, rescheduler);
652 if (sharding) {
|
| /external/tensorflow/tensorflow/compiler/xla/ |
| xla_data.proto | 933 // This sharding is replicated across all devices (implies maximal,
936 // This sharding is maximal - one device runs the entire operation.
938 // This sharding is a tuple - only the tuple_shardings field is valid.
957 // applied, this is inferred from the instruction this sharding gets attached
965 OpSharding sharding = 40;
|
| /build/soong/third_party/zip/ |
| reader.go | 333 // an old zip32 file that was sharding inputs into the largest chunks
|
| /prebuilts/go/darwin-x86/src/archive/zip/ |
| reader.go | 410 // an old zip32 file that was sharding inputs into the largest chunks
|
| /prebuilts/go/linux-x86/src/archive/zip/ |
| reader.go | 410 // an old zip32 file that was sharding inputs into the largest chunks
|
