252 lines
9.4 KiB
Python
252 lines
9.4 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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"""Functional tests for basic component wise operations using a GPU device."""
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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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import itertools
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import threading
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import numpy as np
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import random_seed
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradient_checker_v2
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.ops.gen_array_ops import broadcast_gradient_args
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from tensorflow.python.platform import test
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class GPUBinaryOpsTest(test.TestCase):
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def _compareGPU(self, x, y, np_func, tf_func):
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with self.cached_session(use_gpu=True) as sess:
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inx = ops.convert_to_tensor(x)
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iny = ops.convert_to_tensor(y)
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out = tf_func(inx, iny)
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tf_gpu = self.evaluate(out)
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with self.cached_session(use_gpu=False) as sess:
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inx = ops.convert_to_tensor(x)
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iny = ops.convert_to_tensor(y)
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out = tf_func(inx, iny)
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tf_cpu = self.evaluate(out)
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self.assertAllClose(tf_cpu, tf_gpu)
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def testFloatBasic(self):
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x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float32)
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y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float32)
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self._compareGPU(x, y, np.add, math_ops.add)
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self._compareGPU(x, y, np.subtract, math_ops.subtract)
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self._compareGPU(x, y, np.multiply, math_ops.multiply)
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self._compareGPU(x, y + 0.1, np.true_divide, math_ops.truediv)
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self._compareGPU(x, y + 0.1, np.floor_divide, math_ops.floordiv)
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self._compareGPU(x, y, np.power, math_ops.pow)
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def testFloatWithBCast(self):
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x = np.linspace(-5, 20, 15).reshape(3, 5).astype(np.float32)
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y = np.linspace(20, -5, 30).reshape(2, 3, 5).astype(np.float32)
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self._compareGPU(x, y, np.add, math_ops.add)
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self._compareGPU(x, y, np.subtract, math_ops.subtract)
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self._compareGPU(x, y, np.multiply, math_ops.multiply)
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self._compareGPU(x, y + 0.1, np.true_divide, math_ops.truediv)
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def testDoubleBasic(self):
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x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float64)
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y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float64)
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self._compareGPU(x, y, np.add, math_ops.add)
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self._compareGPU(x, y, np.subtract, math_ops.subtract)
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self._compareGPU(x, y, np.multiply, math_ops.multiply)
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self._compareGPU(x, y + 0.1, np.true_divide, math_ops.truediv)
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def testDoubleWithBCast(self):
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x = np.linspace(-5, 20, 15).reshape(3, 5).astype(np.float64)
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y = np.linspace(20, -5, 30).reshape(2, 3, 5).astype(np.float64)
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self._compareGPU(x, y, np.add, math_ops.add)
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self._compareGPU(x, y, np.subtract, math_ops.subtract)
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self._compareGPU(x, y, np.multiply, math_ops.multiply)
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self._compareGPU(x, y + 0.1, np.true_divide, math_ops.truediv)
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class MathBuiltinUnaryTest(test.TestCase):
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def _compare(self, x, np_func, tf_func, use_gpu):
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np_out = np_func(x)
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with self.cached_session(use_gpu=use_gpu) as sess:
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inx = ops.convert_to_tensor(x)
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ofunc = tf_func(inx)
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tf_out = self.evaluate(ofunc)
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self.assertAllClose(np_out, tf_out)
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def _inv(self, x):
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return 1.0 / x
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def _rsqrt(self, x):
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return self._inv(np.sqrt(x))
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def _testDtype(self, dtype, use_gpu):
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data = (np.arange(-3, 3) / 4.).reshape([1, 3, 2]).astype(dtype)
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data_gt_1 = data + 2 # for x > 1
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self._compare(data, np.abs, math_ops.abs, use_gpu)
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self._compare(data, np.arccos, math_ops.acos, use_gpu)
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self._compare(data, np.arcsin, math_ops.asin, use_gpu)
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self._compare(data, np.arcsinh, math_ops.asinh, use_gpu)
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self._compare(data_gt_1, np.arccosh, math_ops.acosh, use_gpu)
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self._compare(data, np.arctan, math_ops.atan, use_gpu)
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self._compare(data, np.ceil, math_ops.ceil, use_gpu)
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self._compare(data, np.cos, math_ops.cos, use_gpu)
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self._compare(data, np.cosh, math_ops.cosh, use_gpu)
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self._compare(data, np.exp, math_ops.exp, use_gpu)
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self._compare(data, np.floor, math_ops.floor, use_gpu)
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self._compare(data, np.log, math_ops.log, use_gpu)
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self._compare(data, np.log1p, math_ops.log1p, use_gpu)
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self._compare(data, np.negative, math_ops.negative, use_gpu)
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self._compare(data, self._rsqrt, math_ops.rsqrt, use_gpu)
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self._compare(data, np.sin, math_ops.sin, use_gpu)
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self._compare(data, np.sinh, math_ops.sinh, use_gpu)
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self._compare(data, np.sqrt, math_ops.sqrt, use_gpu)
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self._compare(data, np.square, math_ops.square, use_gpu)
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self._compare(data, np.tan, math_ops.tan, use_gpu)
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self._compare(data, np.tanh, math_ops.tanh, use_gpu)
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self._compare(data, np.arctanh, math_ops.atanh, use_gpu)
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def testTypes(self):
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for dtype in [np.float32]:
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self._testDtype(dtype, use_gpu=True)
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def testFloorDivide(self):
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x = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
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[1, 3, 2])
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y = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
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[1, 3, 2])
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np_out = np.floor_divide(x, y + 0.1)
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with self.session(use_gpu=True) as sess:
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inx = ops.convert_to_tensor(x)
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iny = ops.convert_to_tensor(y + 0.1)
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ofunc = inx / iny
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out_func2 = math_ops.floor(ofunc)
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tf_out = self.evaluate(out_func2)
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self.assertAllClose(np_out, tf_out)
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class BroadcastSimpleTest(test.TestCase):
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def _GetGradientArgs(self, xs, ys):
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return self.evaluate(broadcast_gradient_args(xs, ys))
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def testBroadcast(self):
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r0, r1 = self._GetGradientArgs([2, 3, 5], [1])
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self.assertAllEqual(r0, [])
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self.assertAllEqual(r1, [0, 1, 2])
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_GRAD_TOL = {dtypes.float32: 1e-3}
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def _compareGpu(self, x, y, np_func, tf_func):
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np_ans = np_func(x, y)
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with self.cached_session(use_gpu=True):
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inx = ops.convert_to_tensor(x)
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iny = ops.convert_to_tensor(y)
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out = tf_func(inx, iny)
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tf_gpu = self.evaluate(out)
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self.assertAllClose(np_ans, tf_gpu)
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self.assertShapeEqual(np_ans, out)
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# TODO(zhifengc/ke): make gradient checker work on GPU.
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def testGradient(self):
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x1 = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
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[1, 3, 2])
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x2 = (1 + np.linspace(0, 5, np.prod([1, 3, 2]))).astype(np.float32).reshape(
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[1, 3, 2])
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def div_x1(x1):
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return math_ops.truediv(x1, x2) * math_ops.cast(1.1, dtype=x1.dtype)
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def div_x2(x2):
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return math_ops.truediv(x1, x2) * math_ops.cast(1.1, dtype=x2.dtype)
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with self.cached_session():
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err = gradient_checker_v2.max_error(*gradient_checker_v2.compute_gradient(
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div_x1, [x1]))
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self.assertLess(err, self._GRAD_TOL[dtypes.as_dtype(x1.dtype)])
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err = gradient_checker_v2.max_error(*gradient_checker_v2.compute_gradient(
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div_x2, [x2]))
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self.assertLess(err, self._GRAD_TOL[dtypes.as_dtype(x2.dtype)])
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self._compareGpu(x1, x2, np.true_divide, math_ops.truediv)
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self._compareGpu(x1, x2 + 0.1, np.floor_divide, math_ops.floordiv)
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class GpuMultiSessionMemoryTest(test_util.TensorFlowTestCase):
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"""Tests concurrent sessions executing on the same GPU."""
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def _run_session(self, session, results):
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n_iterations = 500
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with session as s:
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data = variables.Variable(1.0)
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with ops.device('/device:GPU:0'):
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random_seed.set_random_seed(1)
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matrix1 = variables.Variable(
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random_ops.truncated_normal([1024, 1]), name='matrix1')
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matrix2 = variables.Variable(
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random_ops.truncated_normal([1, 1024]), name='matrix2')
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x1 = math_ops.multiply(data, matrix1, name='x1')
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x3 = math_ops.matmul(x1, math_ops.matmul(matrix2, matrix1))
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x4 = math_ops.matmul(array_ops.transpose(x3), x3, name='x4')
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s.run(variables.global_variables_initializer())
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for _ in xrange(n_iterations):
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value = s.run(x4)
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results.add(value.flat[0])
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if len(results) != 1:
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break
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@test_util.run_v1_only('b/126596827 needs graph mode in multiple threads')
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def testConcurrentSessions(self):
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n_threads = 4
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threads = []
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results = []
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for _ in xrange(n_threads):
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session = self.session(graph=ops.Graph(), use_gpu=True)
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results.append(set())
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args = (session, results[-1])
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threads.append(threading.Thread(target=self._run_session, args=args))
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for thread in threads:
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thread.start()
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for thread in threads:
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thread.join()
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flat_results = set(itertools.chain(*results))
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self.assertEqual(1,
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len(flat_results),
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'Expected single value, got %r' % flat_results)
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if __name__ == '__main__':
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test.main()
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