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- # Copyright (c) 2021 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.
-
- import unittest
-
- import numpy as np
- from op_test import OpTest, convert_float_to_uint16
- from test_fusion_lstm_op import ACTIVATION, fusion_lstm
-
- from paddle.base import core
-
-
- @unittest.skipIf(
- not core.supports_bfloat16(), "place does not support BF16 evaluation"
- )
- class TestFusionLSTMBF16ONEDNNOp(OpTest):
- def set_confs(self):
- pass
-
- 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=2e-2,
- check_pir_onednn=True,
- )
-
- def setUp(self):
- self.op_type = 'fusion_lstm'
- self.lod = [[2, 3, 5, 4]]
- self.M = 8
- self.D = 16
- self.has_initial_state = False
- self.use_peepholes = False
- self.is_reverse = False
- self._cpu_only = True
- self.act_gate = 'sigmoid'
- self.act_cell = 'tanh'
- self.act_cand = 'tanh'
- self.use_mkldnn = True
- self.mkldnn_data_type = "bfloat16"
- self.force_fp32_output = False
- self.weights_dtype = 'fp32'
- self.set_confs()
-
- T = sum(self.lod[0])
- bs = len(self.lod[0])
-
- # fp32 X input for reference implementation and
- # corresponding bf16 data as input to LSTM oneDNN bf16 kernel
- x = np.random.normal(size=(T, self.M)).astype('float32')
-
- x_bf16 = convert_float_to_uint16(x)
-
- if self.has_initial_state:
- h0 = np.random.normal(size=(bs, self.D)).astype('float32')
- c0 = np.random.normal(size=(bs, self.D)).astype('float32')
- else:
- h0 = np.zeros((bs, self.D)).astype('float32')
- c0 = np.zeros((bs, self.D)).astype('float32')
-
- wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float32')
-
- h0_bf16 = convert_float_to_uint16(h0)
-
- if self.use_peepholes:
- b = np.random.normal(size=(1, 7 * self.D)).astype('float32')
- else:
- b = np.random.normal(size=(1, 4 * self.D)).astype('float32')
- w_b = np.copy(b[:, 0 : 4 * self.D])
- w_c = b[:, 4 * self.D :] if self.use_peepholes else None
-
- wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float32')
-
- wx_bf16 = convert_float_to_uint16(wx)
- wh_bf16 = convert_float_to_uint16(wh)
-
- bx = np.random.normal(size=(1, 4 * self.D)).astype('float32')
- b[0, 0 : 4 * self.D] += bx[0, :]
-
- hidden, c = fusion_lstm(
- x,
- 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],
- )
-
- hidden = hidden.astype('float32')
- hidden_bf16 = convert_float_to_uint16(hidden)
-
- if self.weights_dtype == 'bf16':
- self.inputs = {
- 'X': (x_bf16, self.lod),
- 'WeightX': wx_bf16,
- 'WeightH': wh_bf16,
- 'Bias': b,
- }
- elif self.weights_dtype == 'fp32':
- self.inputs = {
- 'X': (x_bf16, self.lod),
- 'WeightX': wx,
- 'WeightH': wh,
- 'Bias': b,
- }
-
- if self.has_initial_state:
- if self.weights_dtype == 'bf16':
- self.inputs['H0'] = h0_bf16
- elif self.weights_dtype == 'fp32':
- self.inputs['H0'] = h0
-
- self.inputs['C0'] = c0
-
- self.outputs = {
- 'Hidden': (hidden, self.lod),
- 'Cell': (c, self.lod),
- }
-
- self.attrs = {
- 'use_peepholes': self.use_peepholes,
- 'is_reverse': self.is_reverse,
- 'gate_activation': self.act_gate,
- 'cell_activation': self.act_cell,
- 'candidate_activation': self.act_cand,
- 'force_fp32_output': self.force_fp32_output,
- 'use_mkldnn': self.use_mkldnn,
- 'mkldnn_data_type': self.mkldnn_data_type,
- }
-
-
- class TestFusionLSTMBF16ONEDNNPeepholesOp(TestFusionLSTMBF16ONEDNNOp):
- def set_confs(self):
- self.use_peepholes = True
-
-
- class TestFusionLSTMBF16ONEDNNInitializedStateOp(TestFusionLSTMBF16ONEDNNOp):
- def set_confs(self):
- self.has_initial_state = True
-
-
- class TestFusionLSTMBF16ONEDNNReverseOp(TestFusionLSTMBF16ONEDNNOp):
- def set_confs(self):
- self.is_reverse = True
-
-
- class TestFusionLSTMBF16ONEDNNBF16WeightsOp(TestFusionLSTMBF16ONEDNNOp):
- def set_confs(self):
- self.weights_dtype = 'bf16'
-
-
- if __name__ == "__main__":
- from paddle import enable_static
-
- enable_static()
- unittest.main()
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