cbac31d59d
The majority of the changes are from PR #38630 ([Intel MKL] Enable BF16 Softmax/SoftmaxGrad) which was reverted because of test failures. PiperOrigin-RevId: 314152011 Change-Id: Ib50e1ae90016c05a6fc62b8d21ce7b3f34d28833
262 lines
9.1 KiB
Python
262 lines
9.1 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 Python ops defined in nn_grad.py."""
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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 numpy as np
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from tensorflow.python.eager import backprop
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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 test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_nn_ops
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import gradient_checker_v2
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import nn_grad # pylint: disable=unused-import
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from tensorflow.python.ops import nn_impl
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.platform import test
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class SoftmaxOpTest(test.TestCase):
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# This test is for bfloat16, but the type has a problem with compute_gradient.
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# TODO(penporn): Change the data type back to bfloat16 once b/157773623 is
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# fixed. (compute_gradient internally converts bfloat16 to float32 for
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# calculation anyway.)
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def testSoftmaxGradGradExtendType(self):
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with self.cached_session():
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def f(x):
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assert x.dtype == dtypes.float32
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with backprop.GradientTape() as tape:
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tape.watch(x)
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y = nn_ops.softmax(x)
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return tape.gradient(y, x)
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x = constant_op.constant([[-2, -1, 1, 3], [5, 7, 8, 9]],
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dtype=dtypes.float32)
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error = gradient_checker_v2.max_error(
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*gradient_checker_v2.compute_gradient(f, [x]))
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self.assertLess(error, 1e-4)
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class Relu6OpTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testRelu6GradGrad(self):
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inputs = constant_op.constant(
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[[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
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x_init_value = np.array([[-3.5, -1.5, 2, 4], [4.5, 7.5, 8.5, 11]])
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r = nn_ops.relu6(inputs)
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r_g = gradients_impl.gradients(r, inputs)[0]
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with self.cached_session():
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error = gradient_checker.compute_gradient_error(
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inputs,
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inputs.get_shape().as_list(),
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r_g,
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r_g.get_shape().as_list(),
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x_init_value=x_init_value)
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self.assertLess(error, 1e-4)
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class Conv2dOpTest(test.TestCase):
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def run_test(self, x, y):
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with self.test_session():
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error = gradient_checker.compute_gradient_error(x,
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x.get_shape().as_list(),
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y,
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y.get_shape().as_list())
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self.assertLess(error, 1e-3)
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@test_util.run_deprecated_v1
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def testConv2dGradWRTInput(self):
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x = array_ops.placeholder(
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dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
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f = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[2, 2, 3, 2],
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name='filter')
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y = nn_ops.conv2d(x, f, [1, 1, 1, 1], 'SAME')
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self.run_test(x, y)
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@test_util.run_deprecated_v1
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def testConv2dGradWRTFilter(self):
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x = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[1, 4, 4, 3],
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name='input')
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f = array_ops.placeholder(
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dtype=dtypes.float32, shape=[2, 2, 3, 2], name='filter')
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y = nn_ops.conv2d(x, f, [1, 1, 1, 1], 'SAME')
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self.run_test(f, y)
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@test_util.run_deprecated_v1
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def testConv2dBackpropFilterGrad(self):
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x = array_ops.placeholder(
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dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
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f = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[2, 2, 3, 2],
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name='filter')
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strides = [1, 1, 1, 1]
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padding = 'SAME'
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out = nn_impl.depthwise_conv2d(x, f, strides, padding)
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grad_wrt_input = gradients_impl.gradients(out, x)[0]
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self.run_test(f, grad_wrt_input)
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grad_wrt_filter = gradients_impl.gradients(out, f)[0]
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self.run_test(x, grad_wrt_filter)
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class DepthwiseConv2dTest(test.TestCase):
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def run_test(self, x, y):
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with self.test_session():
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error = gradient_checker.compute_gradient_error(x,
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x.get_shape().as_list(),
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y,
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y.get_shape().as_list())
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self.assertLess(error, 1e-3)
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@test_util.run_deprecated_v1
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def testDepthwiseConv2dGradWRTInput(self):
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x = array_ops.placeholder(
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dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
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f = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[2, 2, 3, 2],
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name='filter')
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strides = [1, 1, 1, 1]
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padding = 'SAME'
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y = nn_impl.depthwise_conv2d(x, f, strides, padding)
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self.run_test(x, y)
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@test_util.run_deprecated_v1
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def testDepthwiseConv2dGradWRTFilter(self):
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x = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[1, 4, 4, 3],
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name='input')
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f = array_ops.placeholder(
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dtype=dtypes.float32, shape=[2, 2, 3, 2], name='filter')
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strides = [1, 1, 1, 1]
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padding = 'SAME'
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y = nn_impl.depthwise_conv2d(x, f, strides, padding)
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self.run_test(f, y)
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@test_util.run_deprecated_v1
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def testDepthwiseConv2dBackpropFilterGrad(self):
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x = array_ops.placeholder(
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dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
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f = constant_op.constant([0.5],
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dtype=dtypes.float32,
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shape=[2, 2, 3, 2],
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name='filter')
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strides = [1, 1, 1, 1]
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padding = 'SAME'
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out = nn_impl.depthwise_conv2d(x, f, strides, padding)
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grad_wrt_input = gradients_impl.gradients(out, x)[0]
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self.run_test(f, grad_wrt_input)
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grad_wrt_filter = gradients_impl.gradients(out, f)[0]
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self.run_test(x, grad_wrt_filter)
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class EluGradOpTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testEluGradGradWRTgrad_ys(self):
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inputs = constant_op.constant(
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[[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
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dummy = constant_op.constant(
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[[3, 1, -1, -2], [9, 8, 7, 6]], dtype=dtypes.float32)
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elu = gen_nn_ops.elu(inputs)
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elu_grad = gradients_impl.gradients(elu, inputs, grad_ys=dummy)[0]
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with self.cached_session():
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error = gradient_checker.compute_gradient_error(
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dummy,
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dummy.shape,
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elu_grad,
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elu_grad.shape)
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self.assertLess(error, 1e-4)
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@test_util.run_deprecated_v1
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def testEluGradGradWRTinputs(self):
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inputs = constant_op.constant(
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[[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
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dummy = constant_op.constant(
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[[3, 1, -1, -2], [9, 8, 7, 6]], dtype=dtypes.float32)
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elu = gen_nn_ops.elu(inputs)
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elu_grad = gradients_impl.gradients(elu, inputs, grad_ys=dummy)[0]
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with self.cached_session():
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error = gradient_checker.compute_gradient_error(
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inputs,
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inputs.shape,
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elu_grad,
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elu_grad.shape)
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self.assertLess(error, 1e-4)
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class SeluGradOpTest(test.TestCase):
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@test_util.run_deprecated_v1
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def testSeluGradGradWRTgrad_ys(self):
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inputs = constant_op.constant(
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[[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
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dummy = constant_op.constant(
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[[3, 1, -1, -2], [9, 8, 7, 6]], dtype=dtypes.float32)
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selu = gen_nn_ops.selu(inputs)
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selu_grad = gradients_impl.gradients(selu, inputs, grad_ys=dummy)[0]
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with self.cached_session():
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error = gradient_checker.compute_gradient_error(
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dummy,
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dummy.shape,
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selu_grad,
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selu_grad.shape)
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self.assertLess(error, 1e-4)
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@test_util.run_deprecated_v1
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def testSeluGradGradWRTinputs(self):
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inputs = constant_op.constant(
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[[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
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dummy = constant_op.constant(
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[[3, 1, -1, -2], [9, 8, 7, 6]], dtype=dtypes.float32)
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selu = gen_nn_ops.selu(inputs)
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selu_grad = gradients_impl.gradients(selu, inputs, grad_ys=dummy)[0]
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with self.cached_session():
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error = gradient_checker.compute_gradient_error(
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inputs,
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inputs.shape,
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selu_grad,
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selu_grad.shape)
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self.assertLess(error, 1e-4)
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if __name__ == "__main__":
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test.main()
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