Add int8 GRU kernel (#27220)
* Add int8 GRU kernel with UTs * Lint fixes * More lint fixesdisable_ut_1
Adam
5 years ago
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# Copyright (c) 2020 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 paddle.fluid.tests.unittests.op_test import OpTest
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from paddle.fluid.tests.unittests.test_fusion_gru_op import fusion_gru
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from paddle.fluid.tests.unittests.test_fusion_lstm_op import fc, ACTIVATION
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class TestFusionGRUINT8MKLDNNOp(OpTest):
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def set_confs(self):
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pass
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def setUp(self):
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self.op_type = "fusion_gru"
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self.lod = [[2, 4, 3]]
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self.IC = 3
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self.OC = 5
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self.is_reverse = False
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self.with_h0 = False
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self.with_bias = True
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self.act_state = 'tanh'
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self.act_gate = 'sigmoid'
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self.origin_mode = True
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self.use_mkldnn = True
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self.force_fp32_output = True
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self.error_margin = 1e-5
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self.set_confs()
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# RNN dimensions
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T = sum(self.lod[0])
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N = len(self.lod[0])
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# Input data
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x_f32 = np.random.rand(T, self.IC).astype('float32') * 2 - 1
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scale_data = 63
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shift_data = 64
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x_u8 = (x_f32 * scale_data + shift_data).astype(np.uint8)
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# WeightX/WeightH data
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wx = np.random.rand(self.IC, 3 * self.OC).astype('float32') * 2 - 1
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wh = np.random.rand(self.OC, 3 * self.OC).astype('float32') * 2 - 1
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# Calculating weight scales
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# scales = 63 / max(abs(channel_wise(weightsX + weightsH)))
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# WeightX data shape in PP: [IC, 3 * OC]
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# WeightH data shape in PP: [OC, 2 * OC] + [OC, OC]
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# Scales shape in oneDNN: [3, OC]
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scale_ur = 63 / np.max(np.abs(
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np.concatenate(
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[
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wx[:, :2 * self.OC], wh.flatten()[:2 * self.OC * self.OC]
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.reshape(self.OC, 2 * self.OC)
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],
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axis=0)),
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axis=0)
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scale_o = 63 / np.max(np.abs(
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np.concatenate(
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[
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wx[:, 2 * self.OC:], wh.flatten()[2 * self.OC * self.OC:]
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.reshape(self.OC, self.OC)
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],
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axis=0)),
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axis=0)
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scale_weights = np.concatenate([scale_ur, scale_o]).astype('float')
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bias = np.random.rand(
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1, 3 * self.OC).astype('float32') if self.with_bias else np.zeros(
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(1, 3 * self.OC), dtype='float32')
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h0 = np.random.rand(
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N, self.OC).astype('float32') if self.with_h0 else np.zeros(
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(N, self.OC), dtype='float32')
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_, _, _, hidden_f32 = fusion_gru(x_f32, self.lod, h0, wx, wh, bias,
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self.is_reverse, self.origin_mode,
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ACTIVATION[self.act_state],
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ACTIVATION[self.act_gate])
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self.inputs = {'X': (x_u8, self.lod), 'WeightX': wx, 'WeightH': wh}
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if self.with_bias:
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self.inputs['Bias'] = bias
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if self.with_h0:
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self.inputs['H0'] = h0
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if self.force_fp32_output:
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self.error_margin = 1e-1
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self.outputs = {'Hidden': (hidden_f32, self.lod)}
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else:
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self.error_margin = 1
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hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
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self.outputs = {'Hidden': (hidden_u8, self.lod)}
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self.attrs = {
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'activation': self.act_state,
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'gate_activation': self.act_gate,
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'is_reverse': self.is_reverse,
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'origin_mode': self.origin_mode,
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'use_mkldnn': self.use_mkldnn,
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'force_fp32_output': self.force_fp32_output,
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'Scale_data': scale_data,
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'Shift_data': shift_data,
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'Scale_weights': scale_weights
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}
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def test_check_output(self):
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self.check_output(check_dygraph=False, atol=self.error_margin)
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class TestFusionGRUINT8MKLDNNOp2(TestFusionGRUINT8MKLDNNOp):
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def set_confs(self):
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self.force_fp32_output = False
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class TestFusionGRUINT8MKLDNNOp3(TestFusionGRUINT8MKLDNNOp):
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def set_confs(self):
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self.origin_mode = False
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class TestFusionGRUINT8MKLDNNOp4(TestFusionGRUINT8MKLDNNOp):
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def set_confs(self):
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self.with_bias = False
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class TestFusionGRUINT8MKLDNNOp5(TestFusionGRUINT8MKLDNNOp):
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def set_confs(self):
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self.with_h0 = False
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if __name__ == "__main__":
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unittest.main()
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