wehub-resource-sync
485 lines
16 KiB
Python
485 lines
16 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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skip_check_grad_ci,
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)
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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def ref_prelu(x, weight):
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x_t = x.copy()
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weight = weight.reshape(1, -1, 1, 1)
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neg_indices = x <= 0
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assert x.shape == neg_indices.shape
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x_t[neg_indices] = (x_t * weight)[neg_indices]
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return x_t
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def ref_prelu_nn(x, num_parameters, init):
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weight_np = np.full((num_parameters), init)
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return ref_prelu(x, weight_np)
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class TestFunctionalPReluAPI(unittest.TestCase):
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def setUp(self):
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self.place = get_device_place()
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self.x_np = np.random.uniform(-1.0, 1.0, [1, 2, 3, 4]).astype('float32')
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self.weight_np_0 = np.random.randn(1).astype('float32')
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self.weight_np_1 = np.random.randn(self.x_np.shape[1]).astype('float32')
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def static_check(self, weight_np):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.x_np.shape, 'float32')
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weight = paddle.static.data('Alpha', weight_np.shape, 'float32')
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out = F.prelu(x, weight)
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exe = paddle.static.Executor(self.place)
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res = exe.run(
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feed={'X': self.x_np, 'Alpha': weight_np}, fetch_list=[out]
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)
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out_ref = ref_prelu(self.x_np, weight_np)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
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def dygraph_check(self, weight_np):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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weight = paddle.to_tensor(weight_np)
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out = F.prelu(x, weight)
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out_ref = ref_prelu(self.x_np, weight_np)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_static_api(self):
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self.static_check(self.weight_np_0)
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self.static_check(self.weight_np_1)
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def test_dygraph_api(self):
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self.dygraph_check(self.weight_np_0)
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self.dygraph_check(self.weight_np_1)
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def test_error(self):
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with paddle.static.program_guard(paddle.static.Program()):
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weight_fp32 = paddle.static.data(
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name='weight_fp32', shape=[1], dtype='float32'
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)
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# The input type must be Variable.
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self.assertRaises(TypeError, F.prelu, x=1, weight=weight_fp32)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.static.data(
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name='x_int32', shape=[2, 3], dtype='int32'
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)
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self.assertRaises(TypeError, F.prelu, x=x_int32, weight=weight_fp32)
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# support the input dtype is float16
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if core.is_compiled_with_cuda() or is_custom_device():
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x_fp16 = paddle.static.data(
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name='x_fp16', shape=[2, 3], dtype='float16'
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)
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F.prelu(x=x_fp16, weight=weight_fp32)
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class TestNNPReluAPI(unittest.TestCase):
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def setUp(self):
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self.place = get_device_place()
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self.x_np = np.ones([1, 2, 3, 4]).astype('float32')
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def test_static_api(self):
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startup_program = paddle.static.Program()
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train_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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x = paddle.static.data(
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name='X', shape=self.x_np.shape, dtype='float32'
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)
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m = paddle.nn.PReLU()
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out = m(x)
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exe = paddle.static.Executor(self.place)
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exe.run(startup_program)
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res = exe.run(
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train_program, feed={'X': self.x_np}, fetch_list=[out]
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)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
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def test_dygraph_api(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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m = paddle.nn.PReLU()
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(num_parameters=self.x_np.shape[1])
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, self.x_np.shape[1], 0.25)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(init=0.5)
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(weight_attr=base.ParamAttr(name="weight"))
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(0.5)
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)
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)
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def prelu_api_wrapper(x, alpha, data_format="NCHW", mode="all"):
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return paddle._C_ops.prelu(x, alpha, data_format, mode)
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class PReluTest(OpTest):
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def setUp(self):
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self.init_dtype()
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self.init_input_shape()
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self.init_attr()
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self.op_type = "prelu"
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self.python_api = prelu_api_wrapper
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if self.dtype == np.uint16:
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as_type = self.np_dtype
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else:
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as_type = self.dtype
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x_np = np.random.uniform(-1, 1, self.x_shape).astype(as_type)
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# Since zero point in prelu is not differentiable, avoid randomize
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# zero.
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x_np[np.abs(x_np) < 0.005] = 0.02
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if self.attrs == {
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'mode': "all",
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"data_format": "NCHW",
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} or self.attrs == {'mode': "all", "data_format": "NHWC"}:
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alpha_np = np.random.uniform(-1, -0.5, (1))
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elif self.attrs == {'mode': "channel", "data_format": "NCHW"}:
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alpha_np = np.random.uniform(-1, -0.5, [1, self.x_shape[1], 1, 1])
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elif self.attrs == {'mode': "channel", "data_format": "NHWC"}:
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alpha_np = np.random.uniform(-1, -0.5, [1, 1, 1, self.x_shape[-1]])
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else:
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alpha_np = np.random.uniform(-1, -0.5, [1, *self.x_shape[1:]])
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alpha_np = alpha_np.astype(as_type)
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self.inputs = {'X': x_np, 'Alpha': alpha_np}
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# NOTE(zhiqu): reshape inputs['Alpha'] from [1, 100, 1, 1] to [1, 100] + [1]*len(x.shape[2:])
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# since np operands could not be broadcast together with shapes (1,100,2,2,2,3) (1,100,1,1)
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reshaped_alpha = self.inputs['Alpha']
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if self.attrs == {'mode': "channel", "data_format": "NCHW"}:
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reshaped_alpha = np.reshape(
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self.inputs['Alpha'],
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[1, self.x_shape[1]] + [1] * len(self.x_shape[2:]),
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)
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elif self.attrs == {'mode': "channel", "data_format": "NHWC"}:
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reshaped_alpha = np.reshape(
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self.inputs['Alpha'],
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[1] + [1] * len(self.x_shape[1:-1]) + [self.x_shape[-1]],
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)
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out_np = np.maximum(self.inputs['X'], 0.0)
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out_np = out_np + np.minimum(self.inputs['X'], 0.0) * reshaped_alpha
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assert out_np is not self.inputs['X']
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self.outputs = {'Out': out_np}
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def init_dtype(self):
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self.dtype = np.float64
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def init_input_shape(self):
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self.x_shape = [2, 100, 3, 4]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NCHW"}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(['X', 'Alpha'], 'Out', check_pir=True)
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAll(PReluTest):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 5]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NCHW"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllNHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 50]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NHWC"}
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class TestModeElt(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 5, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NCHW"}
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class TestModeEltNHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 5, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NHWC"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 200, 3]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NCHW"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank3NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 200, 3]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NHWC"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 2, 3, 4, 5, 6]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NCHW"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank6NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 2, 3, 4, 5, 6]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NHWC"}
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class TestModeChannelRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 200, 3]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NCHW"}
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class TestModeChannelRank3NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 3, 100]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NHWC"}
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class TestModeChannelRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 100, 2, 2, 2, 2]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NCHW"}
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class TestModeChannelRank6NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 2, 2, 2, 2, 100]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NHWC"}
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class TestModeElementRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 10, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NCHW"}
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class TestModeElementRank3NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 10, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NHWC"}
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class TestModeElementRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 2, 4, 5, 2]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NCHW"}
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class TestModeElementRank6NHWC(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 2, 4, 5, 2]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NHWC"}
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class TestModeElt_ZeroSize(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 0, 5, 10]
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def create_test_fp16_class(
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parent, check_grad=True, atol=1e-3, max_relative_error=0.05
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):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestPReluFp16Case(parent):
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def init_dtype(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() or is_custom_device():
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(
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place, atol=atol, check_pir=True
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)
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def test_check_grad(self):
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place = get_device_place()
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if core.is_float16_supported(place) and check_grad:
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# Use the default max_relative_error, not use max_relative_error
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self.check_grad_with_place(
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place, ['X', 'Alpha'], 'Out', check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "Fp16Op")
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TestPReluFp16Case.__name__ = cls_name
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globals()[cls_name] = TestPReluFp16Case
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def create_test_bf16_class(
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parent, check_grad=True, atol=1e-3, max_relative_error=0.05
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):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestPReluBF16Op(parent):
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def setUp(self):
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super().setUp()
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.inputs['Alpha'] = convert_float_to_uint16(self.inputs['Alpha'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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def init_dtype(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, atol=atol, check_pir=True)
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def test_check_grad(self):
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place = get_device_place()
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if check_grad:
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# Use the default max_relative_error, not use max_relative_error
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self.check_grad_with_place(
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place, ['X', 'Alpha'], 'Out', check_pir=True
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)
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cls_name = "{}_{}".format(parent.__name__, "BF16Op")
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TestPReluBF16Op.__name__ = cls_name
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globals()[cls_name] = TestPReluBF16Op
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create_test_fp16_class(TestModeElt)
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create_test_fp16_class(TestModeAllRank3)
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create_test_fp16_class(TestModeAllRank6)
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create_test_fp16_class(TestModeChannelRank3)
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create_test_fp16_class(TestModeChannelRank6)
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create_test_fp16_class(TestModeElementRank3)
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create_test_fp16_class(TestModeElementRank6)
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create_test_fp16_class(TestModeEltNHWC)
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create_test_fp16_class(TestModeAllRank3NHWC)
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create_test_fp16_class(TestModeAllRank6NHWC)
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create_test_fp16_class(TestModeChannelRank3NHWC)
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create_test_fp16_class(TestModeChannelRank6NHWC)
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create_test_fp16_class(TestModeElementRank3NHWC)
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create_test_fp16_class(TestModeElementRank6NHWC)
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create_test_bf16_class(TestModeElt)
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create_test_bf16_class(TestModeAllRank3)
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create_test_bf16_class(TestModeAllRank6)
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create_test_bf16_class(TestModeChannelRank3)
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create_test_bf16_class(TestModeChannelRank6)
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create_test_bf16_class(TestModeElementRank3)
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|
create_test_bf16_class(TestModeElementRank6)
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create_test_bf16_class(TestModeEltNHWC)
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create_test_bf16_class(TestModeAllRank3NHWC)
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|
create_test_bf16_class(TestModeAllRank6NHWC)
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|
create_test_bf16_class(TestModeChannelRank3NHWC)
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create_test_bf16_class(TestModeChannelRank6NHWC)
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create_test_bf16_class(TestModeElementRank3NHWC)
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create_test_bf16_class(TestModeElementRank6NHWC)
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if __name__ == "__main__":
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unittest.main()
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