a0c8fee0e6
This is needed to support back_prop=False in v2 control flow. PiperOrigin-RevId: 298720533 Change-Id: I33cd2f2603cff07193c0275878ff22c4c8338fa8
269 lines
9.4 KiB
Python
269 lines
9.4 KiB
Python
# Copyright 2018 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 while loops in XLA."""
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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 os
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.compiler.tf2xla.python import xla
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import function
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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 control_flow_ops
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import map_fn
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class WhileTest(xla_test.XLATestCase):
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def testSingletonLoopHandrolled(self):
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# Define a function for the loop body
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@function.Defun(dtypes.int32)
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def loop_body(step):
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step_out = step + constant_op.constant(1, dtype=dtypes.int32)
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return step_out
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# Define a function for the loop condition
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@function.Defun(dtypes.int32)
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def loop_cond(step):
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return step < 10
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with self.session() as sess:
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init_index = array_ops.placeholder(dtypes.int32, [])
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with self.test_scope():
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loop_outputs = xla.while_loop([init_index], loop_cond, loop_body)
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result = sess.run(loop_outputs, {init_index: 0})
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self.assertAllClose(result, [10], rtol=1e-3)
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def testCountingLoopHandrolled(self):
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# Define a function for the loop body
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@function.Defun(dtypes.int32, dtypes.float32)
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def loop_body(step, rsum):
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step_out = step + constant_op.constant(1, dtype=dtypes.int32)
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sum_out = rsum + constant_op.constant(1.5, dtype=dtypes.float32)
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return step_out, sum_out
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# Define a function for the loop condition
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@function.Defun(dtypes.int32, dtypes.float32)
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def loop_cond(step, rsum):
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del rsum
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return step < 10
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with self.session() as sess:
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init_index = array_ops.placeholder(dtypes.int32, [])
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init_sum = array_ops.placeholder(dtypes.float32, [])
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with self.test_scope():
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loop_outputs = xla.while_loop([init_index, init_sum], loop_cond,
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loop_body)
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result = sess.run(loop_outputs, {init_index: 0, init_sum: 0.0})
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self.assertAllClose(result, [10, 15.0], rtol=1e-3)
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no_iters_result = sess.run(loop_outputs, {init_index: 10, init_sum: 0.0})
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self.assertAllClose(no_iters_result, [10, 0.0], rtol=1e-3)
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def testCountingLoopHandrolledC64(self):
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# Define a function for the loop body
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@function.Defun(dtypes.int32, dtypes.complex64)
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def loop_body(step, rsum):
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step_out = step + constant_op.constant(1, dtype=dtypes.int32)
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sum_out = rsum + constant_op.constant(1.5 + 2j, dtype=dtypes.complex64)
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return step_out, sum_out
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# Define a function for the loop condition
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@function.Defun(dtypes.int32, dtypes.complex64)
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def loop_cond(step, rsum):
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del rsum
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return step < 10
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with self.session() as sess:
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init_index = array_ops.placeholder(dtypes.int32, [])
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init_sum = array_ops.placeholder(dtypes.complex64, [])
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with self.test_scope():
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loop_outputs = xla.while_loop([init_index, init_sum], loop_cond,
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loop_body)
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result = sess.run(loop_outputs, {init_index: 0, init_sum: 0.0})
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self.assertAllClose(result[1], np.complex64(15 + 20j), rtol=1e-3)
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no_iters_result = sess.run(loop_outputs, {init_index: 10, init_sum: 0.0})
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self.assertAllClose(no_iters_result[1], np.complex64(0), rtol=1e-3)
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def testLoopWithConstantOutput(self):
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# Define a function for the loop body
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@function.Defun(dtypes.int32, dtypes.int32)
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def loop_body(step, x):
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del x
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step_out = step + constant_op.constant(1, dtype=dtypes.int32)
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return (step_out, 7)
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# Define a function for the loop condition
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@function.Defun(dtypes.int32, dtypes.int32)
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def loop_cond(step, x):
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del x
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return step < 10
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with self.session() as sess:
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init_index = array_ops.placeholder(dtypes.int32, [])
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with self.test_scope():
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loop_outputs = xla.while_loop([init_index, 42], loop_cond, loop_body)
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result = sess.run(loop_outputs, {init_index: 0})
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self.assertAllClose(result, [10, 7], rtol=1e-3)
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def _testMaxItersSimple(self):
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if is_compile_on_demand():
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self.skipTest("list_ops are not supported in cpu_ondemand")
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with self.session() as sess, self.test_scope():
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xla_context = control_flow_ops.XLAControlFlowContext()
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xla_context.Enter()
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v = constant_op.constant(1.0)
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p = array_ops.placeholder(dtype=dtypes.int32)
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def create_while_loop():
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iterations = array_ops.size(p, name="iterations")
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r = control_flow_ops.while_loop(
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lambda *_: True,
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lambda i, x: (i + 1, v * x), (0, 1.0),
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maximum_iterations=iterations,
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name="outer")
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return array_ops.identity(r[1])
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output = create_while_loop()
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output = gradients_impl.gradients(output, v)[0]
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result = sess.run(output, feed_dict={p: [0, 0, 0]})
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print(result)
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xla_context.Exit()
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def testMaxItersSimple(self):
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self.skipTest("Fails with v1 control flow")
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# This fails with old control.
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# self._testMaxItersSimple()
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@test_util.enable_control_flow_v2
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def testMaxItersSimpleV2(self):
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self._testMaxItersSimple()
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def _testNestedWhileLoopWithMaxItersFromOuterContext(self):
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if is_compile_on_demand():
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self.skipTest("list_ops are not supported in cpu_ondemand")
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with self.session() as sess, self.test_scope():
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xla_context = control_flow_ops.XLAControlFlowContext()
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xla_context.Enter()
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v = constant_op.constant(1.0)
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p = array_ops.placeholder(dtype=dtypes.int32)
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def mid_body_builder(iterations):
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def mid_body(i, x):
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r = control_flow_ops.while_loop(
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lambda *_: True,
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lambda i, x: (i + 1, v * x), (0, x),
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maximum_iterations=iterations,
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name="inner")
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return (i + 1, gradients_impl.gradients(x + r[1], v)[0])
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return mid_body
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def outer_body(i, x):
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iterations = array_ops.size(p, name="iterations")
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return (i + 1, x + control_flow_ops.while_loop(
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lambda *_: True,
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mid_body_builder(iterations), (0, x),
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maximum_iterations=iterations,
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name="mid")[1])
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def create_while_loop():
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r = control_flow_ops.while_loop(
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lambda *_: True,
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outer_body, (0, 1.0),
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maximum_iterations=5,
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name="outer")
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return array_ops.identity(r[1])
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# p:placeholder
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# j = 0
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# i, x = 0, 1.
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# while j++ < 5:
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# i1, x1 = 0, x
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# while i1++ < len(p):
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# i2, x2 = 0, x1
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# while i2++ < len(p):
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# x2 = v * x2
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# x1 = grad(x1 + x2, v)
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# x = x1
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# output = x
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output = create_while_loop()
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sess.run(output, feed_dict={p: [0, 0, 0]})
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xla_context.Exit()
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def testNestedWhileLoopWithMaxItersFromOuterContext(self):
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self._testNestedWhileLoopWithMaxItersFromOuterContext()
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@test_util.enable_control_flow_v2
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def testNestedWhileLoopWithMaxItersFromOuterContextV2(self):
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self._testNestedWhileLoopWithMaxItersFromOuterContext()
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@test_util.enable_control_flow_v2
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def testMap(self):
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if is_compile_on_demand():
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self.skipTest("list_ops are not supported in cpu_ondemand")
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with self.session(), self.test_scope():
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xla_context = control_flow_ops.XLAControlFlowContext()
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xla_context.Enter()
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nums = [1, 2, 3, 4, 5, 6]
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elems = constant_op.constant(nums, name="data")
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r = map_fn.map_fn(lambda x: math_ops.multiply(math_ops.add(x, 3), 2),
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elems)
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self.assertAllEqual(r, np.array([(x + 3) * 2 for x in nums]))
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xla_context.Exit()
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@test_util.enable_control_flow_v2
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def testMapBackPropFalse(self):
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if is_compile_on_demand():
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self.skipTest("list_ops are not supported in cpu_ondemand")
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with self.session(), self.test_scope():
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xla_context = control_flow_ops.XLAControlFlowContext()
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xla_context.Enter()
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nums = [1, 2, 3, 4, 5, 6]
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elems = constant_op.constant(nums, name="data")
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r = map_fn.map_fn(
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lambda x: math_ops.multiply(math_ops.add(x, 3), 2),
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elems,
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back_prop=False)
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self.assertAllEqual(r, np.array([(x + 3) * 2 for x in nums]))
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xla_context.Exit()
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def is_compile_on_demand():
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return ("TF_XLA_FLAGS" in os.environ and
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"tf_xla_compile_on_demand" in os.environ["TF_XLA_FLAGS"])
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if __name__ == "__main__":
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os.environ["TF_XLA_FLAGS"] = ("--tf_xla_min_cluster_size=2 " +
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os.environ.get("TF_XLA_FLAGS", ""))
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test.main()
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