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Fix the problem when rnns compatible with cudnn's parameters are created again #31800

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Mar 23, 2021
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6 changes: 5 additions & 1 deletion python/paddle/fluid/framework.py
Original file line number Diff line number Diff line change
Expand Up @@ -3031,7 +3031,11 @@ def _is_inited_by(block, var):
# In startup_program, "c_broadcast" and "c_sync_comm_stream"
# are treated as initialization ops that cause error.
# Think of "c_broadcast" and "c_sync_comm_stream" as a special case here.
if op.type in ["c_broadcast", "c_sync_comm_stream"]:
# NOTE: "coalesce_tensor" is a special case for rnn with cudnn support
if op.type in [
"c_broadcast", "c_sync_comm_stream",
"coalesce_tensor"
]:
continue
init_ops.append(op)
return init_ops
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,53 @@
# 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 paddle
from unittest import TestCase


def create_model():
hidden_size = 32
bilstm = paddle.nn.LSTM(
hidden_size, hidden_size, num_layers=1, direction='bidirectional')
return bilstm


class TestRNNProgramClone(TestCase):
def setUp(self):
paddle.enable_static()

def test_rnn_with_cudnn_clone(self):
train_program = paddle.static.Program()
test_program = paddle.static.Program()
startup_prog = paddle.static.Program()

# test a typical case in static graph usage: create two nearly
# identical program with a shared startup program to share their
# parameters
#
# when creating a parameter, the name is checked. If there is already
# a parameter with the same name, which is the output of a operator
# (i.e. its creator), its re-creation is skipped.
#
# but if that parameter has been the output of more than one operator,
# an exception is raised. For special cases, white list is added.
# flattening rnn's parameters for the need to call cudnn kernel is such
# a case.
with paddle.static.program_guard(train_program, startup_prog):
with paddle.fluid.unique_name.guard():
bilstm = create_model()

with paddle.fluid.program_guard(test_program, startup_prog):
with paddle.fluid.unique_name.guard():
bilstm = create_model()