forked from mindspore-Ecosystem/mindspore
!2578 Add case for precision of bert network
Merge pull request !2578 from ddwolf/add_case_for_precisoin_of_bert_to_r0.5
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@ -276,11 +276,11 @@ bool AkgKernelBuild::CreateInputDescJson(const AnfNodePtr &anf_node, nlohmann::j
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input_desc_json[kName] = op_input_name;
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input_desc_json[kTensorName] = "input_" + std::to_string(GetInputTensorIdxInc(anf_node, real_input_index));
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auto input_shape = AnfAlgo::GetInputDeviceShape(anf_node, real_input_index);
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if (GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
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if (anf_node->func_graph() != nullptr && anf_node->func_graph()->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL) &&
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GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
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MS_LOG(WARNING) << "we take input[" << real_input_index << "] of [" << anf_node->DebugString(2)
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<< "] as const tensor, shape: [" << Vector2Str(input_shape)
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<< "], value: " << input_desc_json[kValue];
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input_shape.clear();
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}
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if (input_shape.empty()) {
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@ -0,0 +1,194 @@
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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 pytest
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import numpy as np
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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.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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from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
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from src.bert_model import BertConfig
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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(input_columns="masked_lm_ids", operations=type_cast_op)
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ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
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ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
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ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
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ds = ds.map(input_columns="input_mask", operations=type_cast_op)
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ds = ds.map(input_columns="input_ids", operations=type_cast_op)
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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 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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@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_tdt():
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"""test bert tdt"""
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os.system(f"rm -rf kernel_meta")
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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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optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size()*ds.get_repeat_count(),
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start_learning_rate=5e-5, end_learning_rate=1e-9,
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power=10.0, warmup_steps=0, weight_decay=0.01)
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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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params = netwithloss.trainable_params()
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for param in params:
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param.init_data()
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value = param.default_input
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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.default_input = 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.default_input = 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.default_input = 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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