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/external/llvm-project/mlir/test/Analysis/
Dtest-number-of-block-executions.mlir2 // RUN: -test-print-number-of-block-executions \
6 // CHECK-LABEL: Number of executions: empty
9 // CHECK-NEXT: Number of executions: 1
15 // CHECK-LABEL: Number of executions: sequential
18 // CHECK-NEXT: Number of executions: 1
22 // CHECK-NEXT: Number of executions: 1
26 // CHECK-NEXT: Number of executions: 1
32 // CHECK-LABEL: Number of executions: conditional
35 // CHECK-NEXT: Number of executions: 1
39 // CHECK-NEXT: Number of executions: 1
[all …]
Dtest-number-of-operation-executions.mlir2 // RUN: -test-print-number-of-operation-executions \
6 // CHECK-LABEL: Number of executions: empty
9 // CHECK-NEXT: Number of executions: 1
15 // CHECK-LABEL: Number of executions: propagate_parent_num_executions
18 // CHECK-NEXT: Number of executions: 1
21 // CHECK-NEXT: Number of executions: 1
24 // CHECK-NEXT: Number of executions: 1
28 // CHECK-NEXT: Number of executions: 1
31 // CHECK-NEXT: Number of executions: 2
34 // CHECK-NEXT: Number of executions: 2
[all …]
/external/tensorflow/tensorflow/python/debug/lib/
Ddumping_callback_test.py149 self.assertFalse(reader.executions())
155 executions = reader.executions()
159 for execution in executions:
491 executions = reader.executions()
492 self.assertLen(executions, 1)
493 self.assertIn("sin1p_log_sum", executions[0].op_type)
495 graph = reader.graph_by_id(executions[0].graph_id)
501 self.assertLen(executions[0].output_tensor_device_ids, 1)
503 reader.device_name_by_id(executions[0].output_tensor_device_ids[0]),
838 executions = reader.executions()
[all …]
Ddebug_events_writer_test.py230 self.assertFalse(reader.executions())
234 executions = reader.executions()
235 for i, execution in enumerate(executions):
253 executions = reader.executions()
254 self.assertLen(executions, num_execution_events)
255 for i, execution in enumerate(executions):
348 executions = reader.executions()
349 executed_op_types = [execution.op_type for execution in executions]
412 exec_digests = reader.executions(digest=True)
438 executions = [None] * 100
[all …]
Ddebug_events_monitors_test.py45 self.executions = dict()
50 if execution_index in self.executions:
52 self.executions[execution_index] = execution
84 self.assertLen(test_monitor.executions, 1)
86 execution = test_monitor.executions[0]
135 self.assertLen(test_monitor.executions, 1)
137 execution = test_monitor.executions[0]
/external/antlr/antlr3-maven-plugin/src/site/apt/
Dusage.apt.vm41 <executions>
48 </executions>
57 Note that you can create multiple executions, and thus build some grammars with
/external/antlr/antlr3-maven-plugin/src/site/apt/examples/
Dsimple.apt12 <executions>
18 </executions>
Dlibraries.apt32 <executions>
41 </executions>
/external/auto/common/
DREADME.md53 <executions>
73 </executions>
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_ReaderNumRecordsProduced.pbtxt12 This is the same as the number of ReaderRead executions that have
Dapi_def_ReaderNumRecordsProducedV2.pbtxt14 This is the same as the number of ReaderRead executions that have
Dapi_def_Barrier.pbtxt44 summary: "Defines a barrier that persists across different graph executions."
Dapi_def_MutexLock.pbtxt50 Often the use case is that two executions of the same graph, in parallel,
/external/kotlinx.atomicfu/
DREADME.md202 <executions>
215 </executions>
222 <executions>
231 </executions>
320 <executions>
334 </executions>
/external/tensorflow/tensorflow/compiler/xrt/
Dxrt.proto133 // Optional key to disambiguate between executions. This is only
135 // concurrently with executions.
157 // Optional key to disambiguate between executions. This is only needed if
158 // multiple host send/recvs may be outstanding concurrently with executions.
/external/tensorflow/tensorflow/core/framework/
Dresource_handle.proto15 // not valid across executions, but can be serialized back and forth from within
Dremote_fused_graph_execute_info.proto16 // not valid across executions, but can be serialized back and forth from within
Dgraph_transfer_info.proto54 // not valid across executions, but can be serialized back and forth from within
/external/avb/
Daftltool1973 executions = sorted(execution_times)
1979 median = (executions[execution_count // 2 - 1]
1980 + executions[execution_count // 2]) / 2
1982 median = executions[execution_count // 2]
1991 o.write(' Total: {}\n'.format(len(executions)))
1999 o.write(' Min: {:.2f} sec\n'.format(min(executions)))
2000 o.write(' Max: {:.2f} sec\n'.format(max(executions)))
Daftltool.py1973 executions = sorted(execution_times)
1979 median = (executions[execution_count // 2 - 1]
1980 + executions[execution_count // 2]) / 2
1982 median = executions[execution_count // 2]
1991 o.write(' Total: {}\n'.format(len(executions)))
1999 o.write(' Min: {:.2f} sec\n'.format(min(executions)))
2000 o.write(' Max: {:.2f} sec\n'.format(max(executions)))
/external/tensorflow/tensorflow/core/profiler/protobuf/
Dop_metrics.proto43 // Number of executions.
/external/python/cpython2/Doc/library/
Dtimeit.rst66 *timer* function and run its :meth:`.timeit` method with *number* executions.
75 count and *number* executions.
115 Time *number* executions of the main statement. This executes the setup
/external/grpc-grpc-java/
DREADME.md109 <executions>
116 </executions>
/external/python/cpython3/Doc/library/
Dtimeit.rst68 *timer* function and run its :meth:`.timeit` method with *number* executions.
80 count and *number* executions. The optional *globals* argument specifies a
125 Time *number* executions of the main statement. This executes the setup
/external/dokka/
DREADME.md204 <executions>
211 </executions>
230 <executions>
237 </executions>

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