66c48046f1
This is mostly the result of an internal cleanup and formatting pass. PiperOrigin-RevId: 286318018 Change-Id: I8f9e2f7519070035da73f9f24d2fc90864abc51b
199 lines
6.9 KiB
Python
199 lines
6.9 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the 'License');
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an 'AS IS' BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for nccl ops. See also the cc test for nccl_communicator."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from functools import partial
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import numpy as np
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradients
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from tensorflow.python.ops import nccl_ops
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from tensorflow.python.platform import test
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def _DeviceTensors(tensors, devices):
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res = []
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for t, d in zip(tensors, devices):
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with ops.device(d):
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res.append(array_ops.identity(t))
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return res
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def _NcclAllReduce(nccl_fun, tensors, devices):
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return nccl_fun(_DeviceTensors(tensors, devices))
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def _NcclReduce(nccl_fun, tensors, devices):
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receiver = np.random.randint(0, len(devices))
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with ops.device(devices[receiver]):
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return [nccl_fun(_DeviceTensors(tensors, devices))]
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def _NcclBroadcast(tensors, devices):
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sender = np.random.randint(0, len(devices))
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with ops.device(devices[sender]):
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tensor = array_ops.identity(tensors[0])
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broadcast = nccl_ops.broadcast(tensor)
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return _DeviceTensors([broadcast] * len(devices), devices)
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class NcclTestCase(test.TestCase):
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def _Test(self,
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nccl_reduce,
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numpy_fn,
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device_sets=(['/device:GPU:1', '/device:GPU:2', '/device:GPU:0'],
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['/device:GPU:1', '/device:GPU:0'])):
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"""Tests that nccl_reduce does the same as reduction with numpy_fn.
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Args:
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nccl_reduce: A function taking a list of tensors and a list of devices,
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and returns a list of reduced tensors and a list of ops to perform the
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reduction.
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numpy_fn: A function taking two tensors and returning the reduction of the
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two.
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device_sets: Tuple of virtual devices to run test on.
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"""
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for dtype in [np.float16, np.float32, np.int32, np.int64, np.float64]:
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# Create session inside outer loop to test use of
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# same communicator across multiple sessions.
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with self.test_session(use_gpu=True) as sess:
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for devices in device_sets:
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shape = (3, 4)
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random = (np.random.random_sample(shape) - .5) * 1024
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tensors = []
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for _ in devices:
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tensors.append(random.astype(dtype))
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np_ans = tensors[0]
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for t in tensors[1:]:
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np_ans = numpy_fn(np_ans, t)
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reduce_tensors = nccl_reduce(tensors, devices)
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self.assertNotEmpty(reduce_tensors)
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# Test shape inference.
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for r in reduce_tensors:
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self.assertEqual(shape, r.get_shape())
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result_tensors = [array_ops.identity(t) for t in reduce_tensors]
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# Check GPU availability *after* creating session, see b/68975239.
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if not test.is_gpu_available():
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# If no GPU is available, only test graph construction.
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continue
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# Test execution and results.
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for t in self.evaluate(result_tensors):
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self.assertAllClose(t, np_ans)
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def _TestGradient(self, nccl_reduce, numpy_fn):
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"""Tests the gradient of nccl_reduce.
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Args:
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nccl_reduce: A function taking a list of tensors and a list of devices,
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and returns a list of reduced tensors and a list of ops to perform the
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reduction.
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numpy_fn: A function taking two tensors and returning the gradient of the
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reduction of the two.
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"""
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def _Gradient(tensors, devices):
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inputs = [array_ops.placeholder(t.dtype, t.shape) for t in tensors]
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reduce_tensors = nccl_reduce(inputs, devices)
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losses = _DeviceTensors(tensors, [t.device for t in reduce_tensors])
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grads = gradients.gradients(
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reduce_tensors, inputs, losses, colocate_gradients_with_ops=True)
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return [g for g in grads if g is not None]
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self._Test(_Gradient, numpy_fn)
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class AllReduceTest(NcclTestCase):
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def testAllReduce(self):
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self._Test(partial(_NcclAllReduce, nccl_ops.all_sum), lambda x, y: x + y)
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self._Test(partial(_NcclAllReduce, nccl_ops.all_prod), lambda x, y: x * y)
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self._Test(partial(_NcclAllReduce, nccl_ops.all_min), np.minimum)
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self._Test(partial(_NcclAllReduce, nccl_ops.all_max), np.maximum)
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def testAllSumGrad(self):
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self._TestGradient(
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partial(_NcclAllReduce, nccl_ops.all_sum), lambda x, y: x + y)
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def testErrors(self):
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with self.assertRaisesRegexp(ValueError, 'Device assignment required'):
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nccl_ops.all_sum([array_ops.identity(np.random.random_sample((3, 4)))])
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with self.assertRaisesRegexp(ValueError, 'Must pass >0 tensors'):
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nccl_ops.all_sum([])
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class SingleReduceTest(NcclTestCase):
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def testSum(self):
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self._Test(partial(_NcclReduce, nccl_ops.reduce_sum), lambda x, y: x + y)
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def testSumGrad(self):
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self._TestGradient(partial(_NcclReduce, nccl_ops.reduce_sum),
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lambda x, y: x)
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class BroadcastTest(NcclTestCase):
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def testBroadcast(self):
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self._Test(_NcclBroadcast, lambda x, y: x)
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def testBroadcastSingleDevice(self):
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# Broadcasts on a single device are removed completely during rewrite.
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self._Test(_NcclBroadcast, lambda x, y: x,
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(['/device:GPU:0', '/device:GPU:0'],))
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def testBroadcastToCpuError(self):
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try:
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# Broadcasts to CPU is not supported.
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self._Test(_NcclBroadcast, lambda x, y: x,
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(['/device:GPU:0', '/device:CPU:0'],))
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except errors.NotFoundError as e:
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self.assertRegexpMatches(
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str(e), "No registered '_NcclBroadcastRecv' OpKernel for CPU devices")
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else:
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# Session isn't executed when no GPU is available.
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if test.is_gpu_available():
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self.fail("Didn't raise NotFoundError trying to broadcast to CPU")
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class CombinedTest(NcclTestCase):
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"""Test all-reduce vs. single-reduce plus broadcast in one session.run."""
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def _Combined(self, tensors, devices):
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all_reduce_tensors = _NcclAllReduce(nccl_ops.all_sum, tensors, devices)
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single_reduce_tensors = _NcclReduce(nccl_ops.reduce_sum, tensors, devices)
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broadcast_tensors = _NcclBroadcast(single_reduce_tensors, devices)
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return all_reduce_tensors + broadcast_tensors
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def testCombined(self):
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self._Test(self._Combined, lambda x, y: x + y)
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if __name__ == '__main__':
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test.main()
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