366 lines
10 KiB
366 lines
10 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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import paddle
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import paddle.nn.functional as F
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np.random.seed(10)
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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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def ref_softmax(x, axis=None, dtype=None):
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x_t = x.copy()
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if dtype is not None:
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x_t = x_t.astype(dtype)
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if axis is None:
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axis = -1
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return np.apply_along_axis(stable_softmax, axis, x_t)
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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.float64
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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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np.random.seed(0)
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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 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 TestSoftmaxCUDNNOp3(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 0
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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 TestSoftmaxCUDNNOp4(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 1
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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 2
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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 TestSoftmaxCUDNNOp6(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 TestSoftmaxCUDNNOp7(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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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 TestSoftmaxCUDNNOp8(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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def get_axis(self):
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return 0
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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 TestSoftmaxCUDNNOp9(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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def get_axis(self):
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return 1
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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 TestSoftmaxCUDNNOp10(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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def get_axis(self):
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return 2
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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 TestSoftmaxCUDNNOp11(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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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 TestSoftmaxCUDNNOp12(TestSoftmaxCUDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5, 6]
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def get_axis(self):
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return 4
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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.skip('disable TestSoftmaxFP16Op2')
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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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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 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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class TestSoftmaxAPI(unittest.TestCase):
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def setUp(self):
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self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else paddle.CPUPlace()
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self.x_np = np.random.uniform(-1., 1., [2, 3, 4, 5]).astype('float32')
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self.out_ref = np.apply_along_axis(stable_softmax, -1, self.x_np)
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def test_static_check(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.data('X', self.x_np.shape, 'float32')
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out1 = F.softmax(x)
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m = paddle.nn.Softmax()
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out2 = m(x)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
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out_ref = ref_softmax(self.x_np, axis=-1, dtype=None)
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for r in res:
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self.assertEqual(np.allclose(out_ref, r), True)
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def test_dygraph_check(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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out1 = F.softmax(x)
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m = paddle.nn.Softmax()
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out2 = m(x)
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out_ref = ref_softmax(self.x_np, axis=-1, dtype=None)
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for r in [out1, out2]:
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self.assertEqual(np.allclose(out_ref, r.numpy()), True)
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out1 = F.softmax(x, axis=0)
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m = paddle.nn.Softmax(axis=0)
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out2 = m(x)
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out_ref = ref_softmax(self.x_np, axis=0, dtype=None)
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for r in [out1, out2]:
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self.assertEqual(np.allclose(out_ref, r.numpy()), True)
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out = F.softmax(x, dtype=np.float64)
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out_ref = ref_softmax(self.x_np, axis=-1, dtype=np.float64)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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paddle.enable_static()
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def test_error(self):
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with paddle.static.program_guard(paddle.static.Program()):
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# The input type must be Variable.
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self.assertRaises(TypeError, F.softmax, 1)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.data(name='x_int32', shape=[2, 3], dtype='int32')
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self.assertRaises(TypeError, F.softmax, x_int32)
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# support the input dtype is float16
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x_fp16 = paddle.data(name='x_fp16', shape=[2, 3], dtype='float16')
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F.softmax(x_fp16)
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if __name__ == "__main__":
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unittest.main()
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