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Added int8 kernel for oneDNN LSTM op (#31894)
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153 changes: 153 additions & 0 deletions
153
python/paddle/fluid/tests/unittests/mkldnn/test_fusion_lstm_int8_mkldnn_op.py
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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import unittest | ||
| import numpy as np | ||
| from paddle.fluid.tests.unittests.op_test import OpTest | ||
| from paddle.fluid.tests.unittests.test_fusion_lstm_op import fc, ACTIVATION, fusion_lstm | ||
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| class TestFusionLSTMINT8MKLDNNOp(OpTest): | ||
| def set_confs(self): | ||
| pass | ||
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| def setUp(self): | ||
| self.op_type = "fusion_lstm" | ||
| self.lod = [[2, 3, 5, 4]] | ||
| self.IC = 3 | ||
| self.OC = 5 | ||
| self.is_reverse = False | ||
| self.has_initial_state = False | ||
| self.act_cell = 'tanh' | ||
| self.act_gate = 'sigmoid' | ||
| self.act_cand = 'tanh' | ||
| self.use_peepholes = False # LSTM u8 doesn't support peepholes | ||
| self.use_mkldnn = True | ||
| self.force_fp32_output = False | ||
| self.error_margin = 1e-5 | ||
| self.set_confs() | ||
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| # RNN dimensions | ||
| T = sum(self.lod[0]) | ||
| N = len(self.lod[0]) | ||
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| # Input data | ||
| x_f32 = np.random.rand(T, self.IC).astype('float32') * 2 - 1 | ||
| scale_data = 63.0 | ||
| shift_data = 64.0 | ||
| x_u8 = np.rint(x_f32 * scale_data + shift_data).astype(np.uint8) | ||
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| # WeightX/WeightH data | ||
| wx = np.random.rand(self.IC, 4 * self.OC).astype('float32') * 2 - 1 | ||
| wh = np.random.rand(self.OC, 4 * self.OC).astype('float32') * 2 - 1 | ||
|
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| # Calculating weight scales | ||
| # scales = 127 / max(abs(channel_wise(weightsX + weightsH))) | ||
| s8_max = 127.0 | ||
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| scale_weights = s8_max / np.max( | ||
| np.abs(np.concatenate( | ||
| [wx[:, :], wh[:, :]], axis=0)), axis=0) | ||
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| scale_weights = scale_weights.astype('float') | ||
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| if self.use_peepholes: | ||
| b = np.random.rand(1, 7 * self.OC).astype('float32') | ||
| else: | ||
| b = np.random.rand(1, 4 * self.OC).astype('float32') | ||
| w_b = np.copy(b[:, 0:4 * self.OC]) | ||
| w_c = b[:, 4 * self.OC:] if self.use_peepholes else None | ||
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| bx = np.random.normal(size=(1, 4 * self.OC)).astype('float32') | ||
| b[0, 0:4 * self.OC] += bx[0, :] | ||
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| if self.has_initial_state: | ||
| h0 = np.random.rand(N, self.OC).astype('float32') | ||
| c0 = np.random.rand(N, self.OC).astype('float32') | ||
| else: | ||
| h0 = np.zeros((N, self.OC)).astype('float32') | ||
| c0 = np.zeros((N, self.OC)).astype('float32') | ||
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| hidden_f32, c = fusion_lstm( | ||
| x_f32, self.lod, wx, bx, h0, c0, wh, w_b, w_c, self.is_reverse, | ||
| ACTIVATION[self.act_gate], ACTIVATION[self.act_cell], | ||
| ACTIVATION[self.act_cand]) | ||
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| self.inputs = { | ||
| 'X': (x_u8, self.lod), | ||
| 'WeightX': wx, | ||
| 'WeightH': wh, | ||
| 'Bias': b | ||
| } | ||
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| if self.has_initial_state: | ||
| self.inputs['H0'] = h0 | ||
| self.inputs['C0'] = c0 | ||
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| if self.force_fp32_output: | ||
| self.error_margin = 1e-1 | ||
| self.outputs = { | ||
| 'Hidden': (hidden_f32, self.lod), | ||
| 'Cell': (c, self.lod) | ||
| } | ||
| else: | ||
| self.error_margin = 2 | ||
| hidden_u8 = np.rint(hidden_f32 * scale_data + shift_data).astype( | ||
| np.uint8) | ||
| self.outputs = { | ||
| 'Hidden': (hidden_u8, self.lod), | ||
| 'Cell': (c, self.lod) | ||
| } | ||
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| self.attrs = { | ||
| 'gate_activation': self.act_gate, | ||
| 'cell_activation': self.act_cell, | ||
| 'candidate_activation': self.act_cand, | ||
| 'is_reverse': self.is_reverse, | ||
| 'use_peepholes': self.use_peepholes, | ||
| 'use_mkldnn': self.use_mkldnn, | ||
| 'force_fp32_output': self.force_fp32_output, | ||
| 'Scale_data': scale_data, | ||
| 'Shift_data': shift_data, | ||
| 'Scale_weights': scale_weights | ||
| } | ||
|
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| def test_check_output(self): | ||
| for use_seq in {True, False}: | ||
| self.attrs['use_seq'] = use_seq | ||
| self.check_output( | ||
| check_dygraph=False, | ||
| no_check_set=["Cell"], | ||
| atol=self.error_margin) | ||
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| class TestFusionLSTMINT8MKLDNNOp2(TestFusionLSTMINT8MKLDNNOp): | ||
| def set_confs(self): | ||
| self.force_fp32_output = True | ||
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| class TestFusionLSTMINT8MKLDNNOp4(TestFusionLSTMINT8MKLDNNOp): | ||
| def set_confs(self): | ||
| self.is_reverse = True | ||
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| class TestFusionLSTMINT8MKLDNNOp5(TestFusionLSTMINT8MKLDNNOp): | ||
| def set_confs(self): | ||
| self.has_initial_state = True | ||
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| if __name__ == "__main__": | ||
| from paddle import enable_static | ||
| enable_static() | ||
| unittest.main() |
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