forked from mindspore-Ecosystem/mindspore
gpu update bert scripts
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@ -18,9 +18,11 @@ Bert evaluation script.
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"""
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import os
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import argparse
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import numpy as np
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import mindspore.common.dtype as mstype
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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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import mindspore.dataset as de
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import mindspore.dataset.transforms.c_transforms as C
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@ -105,8 +107,17 @@ def bert_predict(Evaluation):
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'''
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prediction function
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'''
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid)
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target = args_opt.device_target
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if target == "Ascend":
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid)
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elif target == "GPU":
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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if bert_net_cfg.compute_type != mstype.float32:
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logger.warning('GPU only support fp32 temporarily, run with fp32.')
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bert_net_cfg.compute_type = mstype.float32
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else:
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raise Exception("Target error, GPU or Ascend is supported.")
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dataset = get_dataset(bert_net_cfg.batch_size, 1)
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if cfg.use_crf:
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net_for_pretraining = Evaluation(bert_net_cfg, False, num_labels=len(tag_to_index), use_crf=True,
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@ -147,6 +158,9 @@ def test_eval():
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callback.acc_num / callback.total_num))
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print("==============================================================")
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parser = argparse.ArgumentParser(description='Bert eval')
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parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
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args_opt = parser.parse_args()
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if __name__ == "__main__":
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num_labels = cfg.num_labels
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test_eval()
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@ -23,6 +23,7 @@ from src.utils import BertFinetuneCell, BertCLS, BertNER, BertSquad, BertSquadCe
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from src.finetune_config import cfg, bert_net_cfg, tag_to_index
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import mindspore.common.dtype as mstype
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from mindspore import context
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from mindspore import log as logger
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import mindspore.dataset as de
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import mindspore.dataset.transforms.c_transforms as C
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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@ -105,6 +106,9 @@ def test_train():
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid)
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elif target == "GPU":
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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if bert_net_cfg.compute_type != mstype.float32:
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logger.warning('GPU only support fp32 temporarily, run with fp32.')
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bert_net_cfg.compute_type = mstype.float32
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else:
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raise Exception("Target error, GPU or Ascend is supported.")
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#BertCLSTrain for classification
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@ -86,6 +86,6 @@ def test_trainTensor(num_classes=10, epoch=15, batch_size=32):
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for i in range(0, epoch):
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data = Tensor(np.ones([batch_size, 3, 227, 227]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([batch_size]).astype(np.int32))
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loss = train_network(data, label)
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loss = train_network(data, label).asnumpy()
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losses.append(loss)
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assert (losses[-1].asnumpy() < 0.01)
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assert losses[-1] < 0.01
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@ -144,9 +144,9 @@ def test_train_lenet():
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for i in range(epoch):
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data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([net.batch_size]).astype(np.int32))
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loss = train_network(data, label)
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loss = train_network(data, label).asnumpy()
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losses.append(loss)
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print(losses)
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assert losses[-1] < 0.01
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def create_dataset(data_path, batch_size=32, repeat_size=1,
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