experiments/STT-tensorflow
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Merge pull request #42615 from drebain:drebain_patch

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PiperOrigin-RevId: 328985012
Change-Id: I0b4c461f07e8a838df05bfa329efdb1a8f1293f1
This commit is contained in:
TensorFlower Gardener 2020-08-28 12:19:18 -07:00
commit 16895e59b8
3 changed files with 29 additions and 0 deletions
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11
tensorflow/core/kernels/dynamic_partition_op_gpu.cu.cc
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@ -48,6 +48,14 @@ limitations under the License.
#include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/core/util/gpu_kernel_helper.h"
#include "tensorflow/core/util/transform_output_iterator.h" #include "tensorflow/core/util/transform_output_iterator.h"
#if GOOGLE_CUDA
#include "tensorflow/stream_executor/cuda/cuda_activation.h"
using stream_executor::cuda::ScopedActivateExecutorContext;
#elif TENSORFLOW_USE_ROCM
#include "tensorflow/core/platform/rocm.h"
using stream_executor::rocm::ScopedActivateExecutorContext;
#endif // GOOGLE_CUDA
namespace tensorflow { namespace tensorflow {
typedef Eigen::GpuDevice GPUDevice; typedef Eigen::GpuDevice GPUDevice;
@ -302,6 +310,9 @@ class DynamicPartitionOpGPU : public AsyncOpKernel {
TensorReference partition_ref(partition_count); TensorReference partition_ref(partition_count);
auto wrapped_callback = [this, c, &data, &partitions, indices_out, auto wrapped_callback = [this, c, &data, &partitions, indices_out,
partition_ref, cpu_tensor, done]() { partition_ref, cpu_tensor, done]() {
auto stream = c->op_device_context()->stream();
ScopedActivateExecutorContext scoped_activation{stream->parent()};
OpOutputList outputs; OpOutputList outputs;
this->AllocateOutputs(c, &data, &partitions, &cpu_tensor, &outputs, done); this->AllocateOutputs(c, &data, &partitions, &cpu_tensor, &outputs, done);
if (!c->status().ok()) { if (!c->status().ok()) {

3
tensorflow/python/kernel_tests/BUILD
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@ -2048,6 +2048,9 @@ cuda_py_test(
name = "dynamic_partition_op_test", name = "dynamic_partition_op_test",
size = "medium", size = "medium",
srcs = ["dynamic_partition_op_test.py"], srcs = ["dynamic_partition_op_test.py"],
tags = [
"multi_and_single_gpu",
],
tfrt_enabled = True, tfrt_enabled = True,
deps = [ deps = [
"//tensorflow/python:array_ops", "//tensorflow/python:array_ops",

15
tensorflow/python/kernel_tests/dynamic_partition_op_test.py
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@ -23,8 +23,10 @@ import unittest
import numpy as np import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import config
from tensorflow.python.framework import constant_op from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import data_flow_ops
@ -346,6 +348,19 @@ class DynamicPartitionTest(test.TestCase):
res = self.evaluate(partitioned) res = self.evaluate(partitioned)
self.assertEqual(res[-1].shape[0], 192) self.assertEqual(res[-1].shape[0], 192)
# see https://github.com/tensorflow/tensorflow/issues/42500
def testMultiGPU(self):
device_list = config.list_logical_devices("GPU")
results = []
for device in device_list:
with ops.device(device.name):
data = constant_op.constant(np.zeros((1000,)))
partitions = constant_op.constant(np.arange(1000, dtype=np.int32) % 10)
result = data_flow_ops.dynamic_partition(data, partitions, 10)
results.append(self.evaluate(result))
if device_list:
self.assertAllEqual(results, np.zeros((len(device_list), 10, 100)))
if __name__ == "__main__": if __name__ == "__main__":
test.main() test.main()