forked from huawei/mindspore2022
222 lines
9.5 KiB
Python
222 lines
9.5 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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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# ============================================================================
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""" test global norm test """
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import re
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import os
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import shutil
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import glob
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.dataset as ds
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from mindspore import Tensor, Parameter, Model
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from mindspore.train import DynamicLossScaleManager
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from mindspore.nn.wrap.cell_wrapper import _VirtualDatasetCell, MicroBatchInterleaved, PipelineCell
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from mindspore.nn.optim import AdamWeightDecay
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore import context
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class OneParameterNet(nn.Cell):
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"""Net definition"""
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def __init__(self, param_type, strategy1, strategy2):
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super(OneParameterNet, self).__init__()
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self.fc1 = P.MatMul().shard(strategy1)
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self.p1 = Parameter(Tensor(np.ones([48, 16]).astype(param_type)), name="weight1")
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self.sub = P.Sub().shard(strategy2)
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def construct(self, x, y):
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x = P.Cast()(x, ms.float16)
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p1 = P.Cast()(self.p1, ms.float16)
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x = self.fc1(x, p1)
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return self.sub(x, 0)
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self, param_type, strategy1, strategy2):
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super(Net, self).__init__()
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self.fc1 = P.MatMul().shard(strategy1)
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self.fc2 = P.MatMul().shard(strategy2)
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self.p1 = Parameter(Tensor(np.ones([48, 64]).astype(param_type)), name="weight1")
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self.p2 = Parameter(Tensor(np.ones([64, 16]).astype(param_type)), name="weight2", parallel_optimizer=False)
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self.sub = P.Sub()
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def construct(self, x, y):
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x = P.Cast()(x, ms.float16)
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p1 = P.Cast()(self.p1, ms.float16)
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p2 = P.Cast()(self.p2, ms.float16)
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x = self.fc1(x, p1)
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x = self.fc2(x, p2)
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return self.sub(x, y)
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class Net2(nn.Cell):
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"""Net definition"""
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def __init__(self, param_type, strategy1, strategy2):
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super(Net2, self).__init__()
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self.net1 = Net(param_type, strategy1, strategy2)
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self.net2 = Net(param_type, strategy1, strategy2)
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self.net1.pipeline_stage = 0
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self.net2.pipeline_stage = 1
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self.sub = P.Sub()
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def construct(self, x, y):
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out1 = self.net1(x, y)
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out2 = self.net2(x, y)
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return self.sub(out1, out2)
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def get_dataset():
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inputs = np.ones([64, 48]).astype(np.float32)
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label = np.zeros([64, 16]).astype(np.float32)
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def dataset_generator():
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for _ in range(10):
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yield inputs, label
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dataset = ds.GeneratorDataset(dataset_generator, column_names=["inputs", "label"])
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return dataset
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class CustomOptimizer(AdamWeightDecay):
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def __init__(self, params):
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super(CustomOptimizer, self).__init__(params)
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self.optimizer = super(CustomOptimizer, self).construct
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def construct(self, gradients):
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grads = C.clip_by_global_norm(gradients)
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return self.optimizer(grads)
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def auto_parallel_compile_net(mode, dev_num, net, strategy1=None, strategy2=None,
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interleaved_batch=2, stages=1, micro_size=1, param_type=np.float32,
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loss_scale_manager=None):
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode=mode, device_num=dev_num, enable_parallel_optimizer=True,
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parallel_optimizer_config={"parallel_optimizer_threshold": 1},
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pipeline_stages=stages)
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net = MicroBatchInterleaved(net(param_type, strategy1, strategy2), interleaved_batch)
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if stages > 1:
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net = PipelineCell(net, micro_size=micro_size)
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net = _VirtualDatasetCell(net).set_comm_fusion(4)
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parameters = net.trainable_params() if stages == 1 else net.infer_param_pipeline_stage()
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optimizer = CustomOptimizer(parameters)
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if loss_scale_manager:
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model = Model(net, optimizer=optimizer, loss_scale_manager=loss_scale_manager)
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else:
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model = Model(net, optimizer=optimizer)
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dataset = get_dataset()
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model.train(1, dataset)
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class TestGlobalNormInserted:
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def setup_method(self):
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self.output_path = './graphs' + self.__str__()
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context.set_context(save_graphs=True,
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save_graphs_path=self.output_path)
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def teardown_method(self):
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shutil.rmtree(self.output_path)
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def run_count_check(self, target_count, pattern):
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"""
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This function will check the target_key counts with the golden one.
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:param target_count: The gold float16 count in the Ir files.
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:param pattern: The generated keyword in the Ir files.
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"""
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# Find the step_parallel_end
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ir_files = glob.glob(os.path.join(self.output_path, 'rank_0', 'step_parallel_end*.ir'))
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assert len(ir_files) == 1
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appear_count = 0
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with open(ir_files[0], 'r') as fp:
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for line in fp:
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res = re.findall(pattern, line)
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if len(res) >= 1:
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appear_count += 1
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assert appear_count == target_count
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def test_nonpipeline_global_norm_one_parameter(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm using one parameter, there should be only one allreduce
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Expectation:When there is no PARALLEL_GLOBALNORM_IN_STAGES inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 8, OneParameterNet, ((1, 8), (8, 1)), ((8, 1), ()),
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interleaved_batch=1, param_type=np.float32)
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self.run_count_check(target_count=1, pattern=r"PARALLEL_GLOBALNORM_IN_STAGES")
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def test_nonpipeline_global_norm(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm when running in semi auto parallel mode, scale for data parallel should be 8
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Expectation:When there is no real div inserted or AllReduce inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 8, Net, ((8, 1), (1, 1)), ((8, 1), (1, 1)),
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interleaved_batch=1, param_type=np.float32)
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self.run_count_check(target_count=1, pattern=r"=8.*PARALLEL_GLOBALNORM_DIV")
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self.run_count_check(target_count=2, pattern=r"PARALLEL_GLOBALNORM")
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def test_pipeline_global_norm(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm when running in pipeline mode, scale for data parallel should be 8
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Expectation: When there is no real div inserted or AllReduce inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 32, Net2, ((8, 1), (1, 1)), ((8, 1), (1, 1)),
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interleaved_batch=1, stages=2, micro_size=2, param_type=np.float32)
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self.run_count_check(target_count=1, pattern=r"=16.*PARALLEL_GLOBALNORM_DIV")
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self.run_count_check(target_count=3, pattern=r"PARALLEL_GLOBALNORM")
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def test_pipeline_global_norm_loss_scale(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm when running in pipeline mode, scale for data parallel should be 8
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Expectation: When there is no real div inserted or AllReduce inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 32, Net2, ((8, 1), (1, 1)), ((8, 1), (1, 1)),
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interleaved_batch=1, stages=2, micro_size=2, param_type=np.float32,
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loss_scale_manager=DynamicLossScaleManager())
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self.run_count_check(target_count=1, pattern=r"=16.*PARALLEL_GLOBALNORM_DIV")
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self.run_count_check(target_count=3, pattern=r"PARALLEL_GLOBALNORM")
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def test_pipeline_global_norm_fp16(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm when running in pipeline mode, scale for data parallel should be 8
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Expectation: When there is no real div inserted or AllReduce inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 32, Net2, ((8, 1), (1, 1)), ((8, 1), (1, 1)),
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interleaved_batch=1, stages=2, micro_size=2, param_type=np.float16)
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self.run_count_check(target_count=1, pattern=r"=16.*PARALLEL_GLOBALNORM_DIV")
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self.run_count_check(target_count=3, pattern=r"PARALLEL_GLOBALNORM")
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def test_pipeline_global_norm_loss_scale_fp16(self):
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"""
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Feature: Parallel ClipByGlobalNorm
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Description: Test the global norm when running in pipeline mode, scale for data parallel should be 8
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Expectation: When there is no real div inserted or AllReduce inserted
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"""
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auto_parallel_compile_net("semi_auto_parallel", 32, Net2, ((8, 1), (1, 1)), ((8, 1), (1, 1)),
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interleaved_batch=1, stages=2, micro_size=2, param_type=np.float16,
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loss_scale_manager=DynamicLossScaleManager())
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self.run_count_check(target_count=1, pattern=r"=16.*PARALLEL_GLOBALNORM_DIV")
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self.run_count_check(target_count=3, pattern=r"PARALLEL_GLOBALNORM")
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