• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.ops.operations import _grad_ops as G
23from mindspore.ops import operations as P
24
25
26class Net(nn.Cell):
27    def __init__(self, reduction):
28        super(Net, self).__init__()
29        self.loss = P.NLLLoss(reduction=reduction)
30
31    def construct(self, predict, target, weight):
32        return self.loss(predict, target, weight)
33
34
35class NLLLossGradNet(nn.Cell):
36    def __init__(self, reduction):
37        super(NLLLossGradNet, self).__init__()
38        self.grad = G.NLLLossGrad(reduction=reduction)
39
40    def construct(self, x, dout_x, target, weight, total_weight):
41        gout = self.grad(x, dout_x, target, weight, total_weight)
42        return gout
43
44
45def nll_loss_template(nptype_input, nptype_weight, reduction):
46    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
47
48    nll_loss_net = Net(reduction)
49
50    predict = Tensor(
51        np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
52
53    target = Tensor(np.array([0, 1]).astype(np.int32))
54
55    weight = Tensor(np.array([0.45, -0.32, 1.21]).astype(nptype_weight))
56
57    loss, total_weight = nll_loss_net(predict, target, weight)
58
59    loss_np = loss.asnumpy()
60    total_weight_np = total_weight.asnumpy()
61
62    expected_tot_weight = np.array(0.129999995)
63
64    if reduction == 'none':
65        expected_loss = np.array([-0.238499984, -0.108800001])
66    elif reduction == 'mean':
67        expected_loss = np.array(-2.67153859)
68    elif reduction == 'sum':
69        expected_loss = np.array(-0.347299993)
70
71    if nptype_input == np.float32 and nptype_weight == np.float32:
72        ertol_loss = 1e-06
73    elif nptype_input == np.float16 or nptype_weight == np.float16:
74        ertol_loss = 1e-03
75
76    if nptype_weight == np.float32:
77        ertol_weight = 1e-06
78    elif nptype_weight == np.float16:
79        ertol_weight = 1e-03
80
81    np.testing.assert_allclose(loss_np, expected_loss, ertol_loss)
82    np.testing.assert_allclose(
83        total_weight_np, expected_tot_weight, ertol_weight)
84
85
86def nll_loss_grad_template(nptype_input, nptype_weight, reduction):
87    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
88
89    nll_loss_grad_net = NLLLossGradNet(reduction)
90
91    x = Tensor(
92        np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
93
94    if reduction == "none":
95        dloss = Tensor(
96            np.array([3.24, -2.13]).astype(nptype_input))
97    else:
98        dloss = Tensor(np.array(1.23).astype(nptype_input))
99
100    target = Tensor(np.array([0, 1]).astype(np.int32))
101    weight = Tensor(np.array([0.45, -0.32, 1.21]).astype(nptype_weight))
102
103    total_weight = Tensor(np.array(0.13).astype(nptype_weight))
104
105    dx = nll_loss_grad_net(x, dloss, target, weight, total_weight)
106
107    dx_np = dx.asnumpy()
108
109    print(dx)
110
111    if reduction == "none":
112        dx_expected = np.array([[-1.45799994, 0, 0], [0, -0.681600034, 0]])
113    elif reduction == "mean":
114        dx_expected = np.array([[-4.25769234, 0, 0], [0, 3.02769232, 0]])
115    else:
116        dx_expected = np.array([[-0.553499997, 0, 0], [0, 0.393599987, 0]])
117
118    if nptype_input == np.float32 and nptype_weight == np.float32:
119        ertol_loss = 1e-06
120    else:
121        ertol_loss = 1e-02
122
123    np.testing.assert_allclose(dx_np, dx_expected, ertol_loss)
124
125
126@pytest.mark.level0
127@pytest.mark.platform_x86_gpu_training
128@pytest.mark.env_onecard
129def test_nll_loss_no_reduction():
130    # Four combinations of fp32 and fp16 inputs and weights
131    nll_loss_template(np.float32, np.float32, "none")
132    nll_loss_template(np.float32, np.float16, "none")
133    nll_loss_template(np.float16, np.float32, "none")
134    nll_loss_template(np.float16, np.float16, "none")
135
136
137@pytest.mark.level0
138@pytest.mark.platform_x86_gpu_training
139@pytest.mark.env_onecard
140def test_nll_loss_mean_reduction():
141    # Four combinations of fp32 and fp16 inputs and weights
142    nll_loss_template(np.float32, np.float32, "mean")
143    nll_loss_template(np.float32, np.float16, "mean")
144    nll_loss_template(np.float16, np.float32, "mean")
145    nll_loss_template(np.float16, np.float16, "mean")
146
147
148@pytest.mark.level0
149@pytest.mark.platform_x86_gpu_training
150@pytest.mark.env_onecard
151def test_nll_loss_sum_reduction():
152    # Four combinations of fp32 and fp16 inputs and weights
153    nll_loss_template(np.float32, np.float32, "sum")
154    nll_loss_template(np.float32, np.float16, "sum")
155    nll_loss_template(np.float16, np.float32, "sum")
156    nll_loss_template(np.float16, np.float16, "sum")
157
158
159@pytest.mark.level0
160@pytest.mark.platform_x86_gpu_training
161@pytest.mark.env_onecard
162def test_nll_loss_grad_mean_reduction():
163    # Four combinations of fp32 and fp16 inputs and weights
164    nll_loss_grad_template(np.float32, np.float32, "mean")
165    nll_loss_grad_template(np.float32, np.float16, "mean")
166    nll_loss_grad_template(np.float16, np.float32, "mean")
167    nll_loss_grad_template(np.float16, np.float16, "mean")
168
169
170@pytest.mark.level0
171@pytest.mark.platform_x86_gpu_training
172@pytest.mark.env_onecard
173def test_nll_loss_grad_sum_reduction():
174    # Four combinations of fp32 and fp16 inputs and weights
175    nll_loss_grad_template(np.float32, np.float32, "sum")
176    nll_loss_grad_template(np.float32, np.float16, "sum")
177    nll_loss_grad_template(np.float16, np.float32, "sum")
178    nll_loss_grad_template(np.float16, np.float16, "sum")
179
180
181@pytest.mark.level0
182@pytest.mark.platform_x86_gpu_training
183@pytest.mark.env_onecard
184def test_nll_loss_grad_no_reduction():
185    # Four combinations of fp32 and fp16 inputs and weights
186    nll_loss_grad_template(np.float32, np.float32, "none")
187    nll_loss_grad_template(np.float32, np.float16, "none")
188    nll_loss_grad_template(np.float16, np.float32, "none")
189    nll_loss_grad_template(np.float16, np.float16, "none")
190