forked from mindspore-Ecosystem/mindspore
add graph kernel testcases
This commit is contained in:
looop5
parent
d68708960e
commit
56fa56b173
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@ -65,8 +65,7 @@ def expand_tile(expand_info):
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for item in attrs:
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if 'multiples' in item:
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multiples = item['multiples']
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output_shape, input_reshape, output_reshape, shape_compatible = _get_tile_output_shape(input_desc['shape'],
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multiples)
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output_shape, _, _, shape_compatible = _get_tile_output_shape(input_desc['shape'], multiples)
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graph_builder = builder.GraphBuilder()
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# generate a graph.
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@ -77,9 +76,7 @@ def expand_tile(expand_info):
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if shape_compatible:
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result = graph_builder.emit('BroadcastTo', [input_x], attrs={'shape': output_shape})
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else:
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input_x_reshape = graph_builder.emit('Reshape', [input_x], attrs={'shape': input_reshape})
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reshape_broadcast = graph_builder.emit('BroadcastTo', [input_x_reshape], attrs={'shape': output_reshape})
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result = graph_builder.emit('Reshape', [reshape_broadcast], attrs={'shape': output_shape})
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result = graph_builder.emit('Tile', [input_x], attrs={'multiples': multiples})
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# set graph output.
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graph_scope.set_output(result)
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@ -35,7 +35,6 @@ from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertNetworkWit
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from model_zoo.official.nlp.bert.src.bert_for_pre_training import BertTrainOneStepWithLossScaleCell
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from model_zoo.official.nlp.bert.src.bert_model import BertConfig
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_current_dir = os.path.dirname(os.path.realpath(__file__))
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DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
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SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
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@ -155,13 +154,16 @@ class ModelCallback(Callback):
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self.lossscale_list.append(cb_params.net_outputs[2].asnumpy())
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print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
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class TimeMonitor(Callback):
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"""Time Monitor."""
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def __init__(self, data_size):
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super(TimeMonitor, self).__init__()
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self.data_size = data_size
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self.epoch_mseconds_list = []
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self.per_step_mseconds_list = []
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def epoch_begin(self, run_context):
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self.epoch_time = time.time()
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@ -170,18 +172,17 @@ class TimeMonitor(Callback):
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self.epoch_mseconds_list.append(epoch_mseconds)
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self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_bert_percision():
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def test_bert_percision(enable_graph_kernel=False):
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"""test bert percision"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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ds, new_repeat_count, _ = me_de_train_dataset()
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version = os.getenv('VERSION', 'large')
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config = get_config(version=version)
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netwithloss = BertNetworkWithLoss(config, True)
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lr = BertLearningRate(decay_steps=ds.get_dataset_size()*new_repeat_count,
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lr = BertLearningRate(decay_steps=ds.get_dataset_size() * new_repeat_count,
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learning_rate=5e-5, end_learning_rate=1e-9,
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power=10.0, warmup_steps=0)
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decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
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@ -239,5 +240,22 @@ def test_bert_percision():
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assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_bert_percision_graph_kernel_off():
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test_bert_percision(enable_graph_kernel=False)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_bert_percision_graph_kernel_on():
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test_bert_percision(enable_graph_kernel=True)
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if __name__ == '__main__':
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test_bert_percision()
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test_bert_percision(enable_graph_kernel=False)
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test_bert_percision(enable_graph_kernel=True)
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@ -1,215 +0,0 @@
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# Copyright 2020 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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"""train bert network without lossscale"""
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import os
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import numpy as np
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from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
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from src.bert_model import BertConfig
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine.datasets as de
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import mindspore.dataset.transforms.c_transforms as C
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from mindspore import context
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from mindspore import log as logger
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from mindspore.common.tensor import Tensor
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from mindspore.nn import learning_rate_schedule as lr_schedules
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from mindspore.nn.optim import Lamb
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from mindspore.train.callback import Callback
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from mindspore.train.loss_scale_manager import DynamicLossScaleManager
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from mindspore.train.model import Model
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DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
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SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"
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def get_config(version='base', batch_size=1):
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"""get config"""
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if version == 'base':
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bert_config = BertConfig(
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batch_size=batch_size,
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seq_length=128,
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vocab_size=21136,
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hidden_size=768,
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num_hidden_layers=2,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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use_relative_positions=True,
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input_mask_from_dataset=True,
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token_type_ids_from_dataset=True,
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dtype=mstype.float32,
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compute_type=mstype.float32)
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elif version == 'large':
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bert_config = BertConfig(
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batch_size=batch_size,
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seq_length=128,
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vocab_size=30522,
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hidden_size=1024,
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num_hidden_layers=2,
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num_attention_heads=16,
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intermediate_size=4096,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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use_relative_positions=True,
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input_mask_from_dataset=True,
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token_type_ids_from_dataset=True,
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dtype=mstype.float32,
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compute_type=mstype.float16,
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enable_fused_layernorm=True)
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else:
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bert_config = BertConfig(batch_size=batch_size)
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return bert_config
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def me_de_train_dataset():
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"""test me de train dataset"""
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# apply repeat operations
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repeat_count = 1
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ds = de.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
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"next_sentence_labels", "masked_lm_positions",
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"masked_lm_ids", "masked_lm_weights"], shuffle=False)
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type_cast_op = C.TypeCast(mstype.int32)
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ds = ds.map(operations=type_cast_op, input_columns="masked_lm_ids")
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ds = ds.map(operations=type_cast_op, input_columns="masked_lm_positions")
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ds = ds.map(operations=type_cast_op, input_columns="next_sentence_labels")
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ds = ds.map(operations=type_cast_op, input_columns="segment_ids")
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ds = ds.map(operations=type_cast_op, input_columns="input_mask")
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ds = ds.map(operations=type_cast_op, input_columns="input_ids")
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# apply batch operations
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batch_size = int(os.getenv('BATCH_SIZE', '16'))
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ds = ds.batch(batch_size, drop_remainder=True)
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ds = ds.repeat(repeat_count)
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return ds
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def weight_variable(shape):
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"""weight variable"""
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np.random.seed(1)
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ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
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return Tensor(ones)
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class BertLearningRate(lr_schedules.LearningRateSchedule):
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def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power):
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super(BertLearningRate, self).__init__()
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self.warmup_lr = lr_schedules.WarmUpLR(learning_rate, warmup_steps)
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self.decay_lr = lr_schedules.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
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self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
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self.greater = P.Greater()
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self.one = Tensor(np.array([1.0]).astype(np.float32))
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self.cast = P.Cast()
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def construct(self, global_step):
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is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32)
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warmup_lr = self.warmup_lr(global_step)
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decay_lr = self.decay_lr(global_step)
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lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr
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return lr
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class ModelCallback(Callback):
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def __init__(self):
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super(ModelCallback, self).__init__()
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self.loss_list = []
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self.overflow_list = []
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self.lossscale_list = []
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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self.loss_list.append(cb_params.net_outputs[0].asnumpy()[0])
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self.overflow_list.append(cb_params.net_outputs[1].asnumpy())
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self.lossscale_list.append(cb_params.net_outputs[2].asnumpy())
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print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
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def test_bert_tdt():
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"""test bert tdt"""
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np.random.seed(0)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", reserve_class_name_in_scope=False)
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context.set_context(enable_graph_kernel=True)
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ds = me_de_train_dataset()
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config = get_config(version='large', batch_size=16)
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netwithloss = BertNetworkWithLoss(config, True)
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lr = BertLearningRate(decay_steps=ds.get_dataset_size()*ds.get_repeat_count(), learning_rate=5e-5,
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end_learning_rate=1e-9, power=10.0, warmup_steps=0)
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decay_filter = lambda x: 'layernorm' not in x.name.lower() and 'bias' not in x.name.lower()
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no_decay_filter = lambda x: 'layernorm' in x.name.lower() or 'bias' in x.name.lower()
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decay_params = list(filter(decay_filter, net_with_loss.trainable_params()))
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other_params = list(filter(no_decay_filter, net_with_loss.trainable_params()))
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group_params = [{'params': decay_params, 'weight_decay': 0.01},
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{'params': other_params}]
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optimizer = Lamb(group_params, lr)
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scale_window = 3
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scale_manager = DynamicLossScaleManager(262144, 2, scale_window)
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netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
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scale_update_cell=scale_manager.get_update_cell())
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netwithgrads.set_train(True)
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model = Model(netwithgrads)
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callback = ModelCallback()
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netwithloss.init_parameters_data()
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params = netwithloss.trainable_params()
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for param in params:
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value = param.data
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name = param.name
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if isinstance(value, Tensor):
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if name.split('.')[-1] in ['weight']:
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if name.split('.')[-3] in ['cls2']:
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logger.info("***************** BERT param name is 1 {}".format(name))
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param.set_data(weight_variable(value.asnumpy().shape))
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else:
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logger.info("***************** BERT param name is 2 {}".format(name))
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tempshape = value.asnumpy().shape
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shape = (tempshape[1], tempshape[0])
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weight_value = weight_variable(shape).asnumpy()
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param.set_data(Tensor(np.transpose(weight_value, [1, 0])))
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else:
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logger.info("***************** BERT param name is 3 {}".format(name))
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param.set_data(weight_variable(value.asnumpy().shape))
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model.train(1, ds, callbacks=callback, dataset_sink_mode=False)
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# assertion occurs while the loss value, overflow state or loss_scale value is wrong
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loss_value = np.array(callback.loss_list)
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expect_loss_value = [12.559319, 12.333815, 12.339806, 12.350235, 12.343947, 12.830965, 12.375336, 12.973715,
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12.57929, 12.7766905]
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error = loss_value - expect_loss_value
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print("loss value: {}".format(loss_value))
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print("error value: {}".format(error))
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assert np.allclose(loss_value, expect_loss_value, 0, 0.0005)
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overflow = np.array(callback.overflow_list)
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expect_overflow = [True, True, True, True, False, False, False, True, False, False]
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print("overflow: {}".format(overflow))
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assert (overflow == expect_overflow).all()
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loss_scale = np.array(callback.lossscale_list)
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expect_loss_scale = [131072.0, 65536.0, 32768.0, 16384.0, 16384.0, 16384.0, 32768.0, 16384.0, 16384.0, 16384.0]
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print("loss scale: {}".format(loss_scale))
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assert np.allclose(loss_scale, expect_loss_scale, 0, 0)
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if __name__ == '__main__':
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test_bert_tdt()
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@ -0,0 +1,69 @@
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# Copyright 2020 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class ClipByNormNoDivSum(nn.Cell):
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def __init__(self):
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super(ClipByNormNoDivSum, self).__init__()
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self.greater = P.Greater()
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self.select = P.Select()
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self.sqrt = P.Sqrt()
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self.maximum = P.Maximum()
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def construct(self, i0, i1, i2, i3):
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greater_res = self.greater(i0, i1)
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select_res0 = self.select(greater_res, i0, i2)
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sqrt_res = self.sqrt(select_res0)
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select_res1 = self.select(greater_res, sqrt_res, i0)
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res = self.maximum(select_res1, i3)
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return res
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def get_output(x0, x1, x2, x3, enable_graph_kernel=False):
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if enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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net = ClipByNormNoDivSum()
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output = net(x0, x1, x2, x3)
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return output
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def test_clip_by_norm_no_div_sum(shape0, shape1, shape2, shape3, dtype):
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x0 = Tensor(np.random.normal(0, 1, shape0).astype(dtype))
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x1 = Tensor(np.zeros(shape1, dtype))
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x2 = Tensor(np.ones(shape2, dtype))
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x3 = Tensor(np.ones(shape3, dtype))
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expect = get_output(x0, x1, x2, x3, False)
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output = get_output(x0, x1, x2, x3, True)
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expect_np = expect.asnumpy().copy()
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output_np = output.asnumpy().copy()
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assert np.allclose(expect_np, output_np, 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_clip_by_norm_no_div_sum_ascend():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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test_clip_by_norm_no_div_sum((1, 1), (1,), (1, 1), (1,), np.float32)
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@ -0,0 +1,60 @@
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# Copyright 2020 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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#
|
||||
# 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 numpy as np
|
||||
import pytest
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.ops.operations import _grad_ops as G
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.sqrt_grad = G.SqrtGrad()
|
||||
|
||||
def construct(self, x, dout):
|
||||
return self.sqrt_grad(x, dout)
|
||||
|
||||
|
||||
def get_output(x, dout, enable_graph_kernel=False):
|
||||
if enable_graph_kernel:
|
||||
context.set_context(enable_graph_kernel=True)
|
||||
net = Net()
|
||||
output = net(x, dout)
|
||||
return output
|
||||
|
||||
|
||||
def test_sqrt_grad(shape_x, shape_dout, dtype):
|
||||
x = Tensor(np.random.normal(0, 1, shape_x).astype(dtype))
|
||||
dout = Tensor(np.random.normal(0, 1, shape_dout).astype(dtype))
|
||||
|
||||
expect = get_output(x, dout, False)
|
||||
output = get_output(x, dout, True)
|
||||
|
||||
expect_np = expect.asnumpy().copy()
|
||||
output_np = output.asnumpy().copy()
|
||||
|
||||
assert np.allclose(expect_np, output_np, 0.0001, 0.0001)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_sqrt_grad_ascend():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
test_sqrt_grad((16, 16), (16, 16), np.float16)
|
||||
test_sqrt_grad((16, 16), (16, 16), np.float32)
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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 numpy as np
|
||||
import pytest
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def __init__(self, multiples):
|
||||
super(Net, self).__init__()
|
||||
self.tile = P.Tile()
|
||||
self.multiples = multiples
|
||||
|
||||
def construct(self, x):
|
||||
return self.tile(x, self.multiples)
|
||||
|
||||
|
||||
def get_output(x, multiples, enable_graph_kernel=False):
|
||||
if enable_graph_kernel:
|
||||
context.set_context(enable_graph_kernel=True)
|
||||
net = Net(multiples)
|
||||
output = net(x)
|
||||
return output
|
||||
|
||||
|
||||
def test_tile(shape, dtype, multiples):
|
||||
x = Tensor(np.random.normal(0, 1, shape).astype(dtype))
|
||||
expect = get_output(x, multiples, False)
|
||||
output = get_output(x, multiples, True)
|
||||
|
||||
expect_np = expect.asnumpy().copy()
|
||||
output_np = output.asnumpy().copy()
|
||||
|
||||
assert np.allclose(expect_np, output_np, 0.0001, 0.0001)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_tile_ascend():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
test_tile((24, 1), np.float16, (2, 2, 2))
|
||||
test_tile((24, 1), np.float32, (1, 2))
|
||||
Loading…
Reference in New Issue