Added int8 kernel for oneDNN LSTM op (#31894)
jakpiase
4 years ago
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GitHub
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commit
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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_lstm_op import fc, ACTIVATION, fusion_lstm
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class TestFusionLSTMINT8MKLDNNOp(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_lstm"
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self.lod = [[2, 3, 5, 4]]
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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.has_initial_state = False
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self.act_cell = 'tanh'
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self.act_gate = 'sigmoid'
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self.act_cand = 'tanh'
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self.use_peepholes = False # LSTM u8 doesn't support peepholes
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self.use_mkldnn = True
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self.force_fp32_output = False
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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 = 63.0
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shift_data = 64.0
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x_u8 = np.rint(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, 4 * self.OC).astype('float32') * 2 - 1
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wh = np.random.rand(self.OC, 4 * self.OC).astype('float32') * 2 - 1
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# Calculating weight scales
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# scales = 127 / max(abs(channel_wise(weightsX + weightsH)))
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s8_max = 127.0
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scale_weights = s8_max / np.max(
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np.abs(np.concatenate(
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[wx[:, :], wh[:, :]], axis=0)), axis=0)
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scale_weights = scale_weights.astype('float')
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if self.use_peepholes:
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b = np.random.rand(1, 7 * self.OC).astype('float32')
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else:
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b = np.random.rand(1, 4 * self.OC).astype('float32')
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w_b = np.copy(b[:, 0:4 * self.OC])
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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')
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b[0, 0:4 * self.OC] += bx[0, :]
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if self.has_initial_state:
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h0 = np.random.rand(N, self.OC).astype('float32')
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c0 = np.random.rand(N, self.OC).astype('float32')
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else:
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h0 = np.zeros((N, self.OC)).astype('float32')
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c0 = np.zeros((N, self.OC)).astype('float32')
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hidden_f32, c = fusion_lstm(
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x_f32, self.lod, wx, bx, h0, c0, wh, w_b, w_c, self.is_reverse,
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ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
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ACTIVATION[self.act_cand])
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self.inputs = {
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'X': (x_u8, self.lod),
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'WeightX': wx,
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'WeightH': wh,
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'Bias': b
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}
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if self.has_initial_state:
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self.inputs['H0'] = h0
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self.inputs['C0'] = c0
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if self.force_fp32_output:
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self.error_margin = 1e-1
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self.outputs = {
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'Hidden': (hidden_f32, self.lod),
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'Cell': (c, self.lod)
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}
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else:
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self.error_margin = 2
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hidden_u8 = np.rint(hidden_f32 * scale_data + shift_data).astype(
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np.uint8)
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self.outputs = {
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'Hidden': (hidden_u8, self.lod),
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'Cell': (c, self.lod)
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}
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self.attrs = {
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'gate_activation': self.act_gate,
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'cell_activation': self.act_cell,
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'candidate_activation': self.act_cand,
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'is_reverse': self.is_reverse,
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'use_peepholes': self.use_peepholes,
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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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for use_seq in {True, False}:
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self.attrs['use_seq'] = use_seq
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self.check_output(
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check_dygraph=False,
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no_check_set=["Cell"],
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atol=self.error_margin)
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class TestFusionLSTMINT8MKLDNNOp2(TestFusionLSTMINT8MKLDNNOp):
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def set_confs(self):
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self.force_fp32_output = True
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class TestFusionLSTMINT8MKLDNNOp4(TestFusionLSTMINT8MKLDNNOp):
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def set_confs(self):
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self.is_reverse = True
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class TestFusionLSTMINT8MKLDNNOp5(TestFusionLSTMINT8MKLDNNOp):
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def set_confs(self):
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self.has_initial_state = True
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if __name__ == "__main__":
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from paddle import enable_static
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enable_static()
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unittest.main()
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