Added LSTM BF16 and fixed GRU BF16 (#31234)
jakpiase
4 years ago
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GitHub
parent
7cdf6ea770
commit
5b4f8aac82
@ -0,0 +1,159 @@
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# Copyright (c) 2021 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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from __future__ import print_function
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import unittest
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import numpy as np
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import struct
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import paddle.fluid.core as core
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from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16, convert_uint16_to_float
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from paddle.fluid.tests.unittests.test_fusion_lstm_op import TestFusionLSTMOp, fc, ACTIVATION, fusion_lstm
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from paddle.fluid.tests.unittests.test_fusion_gru_op import fusion_gru
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@unittest.skipIf(not core.supports_bfloat16(),
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"place does not support BF16 evaluation")
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class TestFusionLSTMBF16ONEDNNOp(OpTest):
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def set_confs(self):
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self.mkldnn_data_type = False
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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(check_dygraph=False, no_check_set=["Cell"])
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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.M = 8
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self.D = 16
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self.has_initial_state = False
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self.use_peepholes = False
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self.is_reverse = False
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self._cpu_only = True
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self.act_gate = 'sigmoid'
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self.act_cell = 'tanh'
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self.act_cand = 'tanh'
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self.use_mkldnn = True
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self.force_fp32_output = False
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self.weights_dtype = 'fp32'
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self.set_confs()
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T = sum(self.lod[0])
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bs = len(self.lod[0])
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# fp32 X input for reference implementation and
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# corressponding bf16 data as input to LSTM oneDNN bf16 kernel
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x = np.random.normal(size=(T, self.M)).astype('float32')
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x_bf16 = convert_float_to_uint16(x)
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if self.has_initial_state:
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h0 = np.random.normal(size=(bs, self.D)).astype('float32')
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c0 = np.random.normal(size=(bs, self.D)).astype('float32')
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else:
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h0 = np.zeros((bs, self.D)).astype('float32')
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c0 = np.zeros((bs, self.D)).astype('float32')
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wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float32')
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h0_bf16 = convert_float_to_uint16(h0)
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if self.use_peepholes:
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b = np.random.normal(size=(1, 7 * self.D)).astype('float32')
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else:
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b = np.random.normal(size=(1, 4 * self.D)).astype('float32')
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w_b = np.copy(b[:, 0:4 * self.D])
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w_c = b[:, 4 * self.D:] if self.use_peepholes else None
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wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float32')
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wx_bf16 = convert_float_to_uint16(wx)
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wh_bf16 = convert_float_to_uint16(wh)
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bx = np.random.normal(size=(1, 4 * self.D)).astype('float32')
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b[0, 0:4 * self.D] += bx[0, :]
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hidden, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
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self.is_reverse, ACTIVATION[self.act_gate],
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ACTIVATION[self.act_cell],
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ACTIVATION[self.act_cand])
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hidden = hidden.astype('float32')
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hidden_bf16 = convert_float_to_uint16(hidden)
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if self.weights_dtype == 'bf16':
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self.inputs = {
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'X': (x_bf16, self.lod),
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'WeightX': wx_bf16,
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'WeightH': wh_bf16,
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'Bias': b
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}
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elif self.weights_dtype == 'fp32':
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self.inputs = {
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'X': (x_bf16, 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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if self.weights_dtype == 'bf16':
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self.inputs['H0'] = h0_bf16
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elif self.weights_dtype == 'fp32':
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self.inputs['H0'] = h0
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self.inputs['C0'] = c0
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self.outputs = {
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'Hidden': (hidden, self.lod),
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'Cell': (c, self.lod),
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}
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self.attrs = {
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'use_peepholes': self.use_peepholes,
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'is_reverse': self.is_reverse,
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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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'force_fp32_output': self.force_fp32_output,
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'use_mkldnn': self.use_mkldnn
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}
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class TestFusionLSTMBF16ONEDNNPeepholesOp(TestFusionLSTMBF16ONEDNNOp):
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def set_confs(self):
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self.use_peepholes = True
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class TestFusionLSTMBF16ONEDNNInitializedStateOp(TestFusionLSTMBF16ONEDNNOp):
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def set_confs(self):
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self.has_initial_state = True
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class TestFusionLSTMBF16ONEDNNReverseOp(TestFusionLSTMBF16ONEDNNOp):
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def set_confs(self):
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self.is_reverse = True
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class TestFusionLSTMBF16ONEDNNBF16WeightsOp(TestFusionLSTMBF16ONEDNNOp):
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def set_confs(self):
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self.weights_dtype = 'bf16'
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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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