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222 lines
6.5 KiB
222 lines
6.5 KiB
# Copyright (c) 2018 PaddlePaddle 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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def stable_softmax(x):
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"""Compute the softmax of vector x in a numerically stable way."""
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# clip to shiftx, otherwise, when calc loss with
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# log(exp(shiftx)), may get log(0)=INF
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shiftx = (x - np.max(x)).clip(-64.)
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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class TestSoftmaxOp(OpTest):
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def get_x_shape(self):
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return [10, 10]
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def get_axis(self):
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return -1
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def setUp(self):
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self.op_type = "softmax"
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self.use_cudnn = False
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self.use_mkldnn = False
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self.dtype = np.float32
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self.init_kernel_type()
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self.shape = self.get_x_shape()
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self.axis = self.get_axis()
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x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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out = np.apply_along_axis(stable_softmax, self.axis, x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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self.attrs = {
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'axis': self.axis,
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'use_cudnn': self.use_cudnn,
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'use_mkldnn': self.use_mkldnn
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}
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def init_kernel_type(self):
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pass
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def test_check_output(self):
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# TODO(wangzhongpu): support mkldnn op in dygraph mode
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if self.use_cudnn:
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place = core.CUDAPlace(0)
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self.check_output_with_place(
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place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
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else:
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self.check_output(check_dygraph=(self.use_mkldnn == False))
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def test_check_grad(self):
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# TODO(wangzhongpu): support mkldnn op in dygraph mode
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if self.use_cudnn or self.dtype == np.float16:
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ["X"],
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"Out",
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max_relative_error=0.01,
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check_dygraph=(self.use_mkldnn == False))
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else:
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self.check_grad(
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["X"],
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"Out",
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max_relative_error=0.01,
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check_dygraph=(self.use_mkldnn == False))
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class TestSoftmaxOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of softmax_op must be Variable.
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, fluid.layers.softmax, x1)
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# The input dtype of softmax_op must be float16, float32 or float64.
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x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.softmax, x2)
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
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fluid.layers.softmax(x3)
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class TestSoftmaxOp2(TestSoftmaxOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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class TestSoftmaxOp3(TestSoftmaxOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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def get_axis(self):
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return 0
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class TestSoftmaxOp4(TestSoftmaxOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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def get_axis(self):
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return 1
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class TestSoftmaxOp5(TestSoftmaxOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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def get_axis(self):
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return 2
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class TestSoftmaxOp6(TestSoftmaxOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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def get_axis(self):
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return 3
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxCUDNNOp(TestSoftmaxOp):
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def init_kernel_type(self):
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self.use_cudnn = True
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxCUDNNOp2(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxCUDNNOp5(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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def get_axis(self):
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return 3
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxFP16Op(TestSoftmaxOp):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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# FIXME: If the x_shape is [10, 10], gradient failed.
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def test_check_grad(self):
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pass
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxFP16Op2(TestSoftmaxOp):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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def get_x_shape(self):
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return [2, 3, 4, 5]
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-3)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestSoftmaxFP16CUDNNOp2(TestSoftmaxFP16CUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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if __name__ == "__main__":
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unittest.main()
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