Clip gradients. Inputs: clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'. clip_value (float): Specifies how muc

Clip gradients. Inputs: clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'. clip_value (float): Specifies how muc

get placeholders of data_dy, data_x, data_variance, data_mean and data_gamma

A wrapper to massage inputs/outputs from pipeline.

Update parameters by AdamWeightDecay op.

r""" Add checkpoint policy to callback.

Code generation arguments.

Code generation arguments.

Code generation arguments.

Add token-type embedding. This function is provided so we can add token-type embeddings in case the pretrained model does not have it.

Add token-type embedding. This function is provided so we can add token-type embeddings in case the pretrained model does not have it.

Broadcast data from rank zero of each model parallel group to the members of the same model parallel group. Arguments: keys: list of

Evaluates the functional correctness of a completion by running the test suite provided in the problem.

Checkpoint a model or part of the model. This has been directly copied from torch.utils.checkpoint.

Cleans up the generated code text.

Compile helper function ar runtime. Make sure this is invoked on a single process.

r""" The forward process of the block.

r"""forward pass of the model

r"""Forward process of the pangu alpha model

evaluation net

evaluation net

r"""Forward process of the pangu alpha model

Calculate global norm construct

Clip grads by global norm construct

dynamic learning rate

Defines the computation performed.

Defines the computation performed.

Defines the computation performed.

Defines the computation performed.

r""" The forward process of the block.

r"""forward pass of the model

r"""Forward process of the pangu alpha model

evaluation net

AdamWeightDecayOp

Converts a sequence of ids in BPE tokens using the vocab.

Creates the predictions for the masked LM objective. Note: Tokens here are vocab ids and not text tokens.

Merge segments A and B, add [CLS] and [SEP] and build tokentypes.

Set the groups to none.

Method saves list of dicts into jsonl file. :param data: (list) list of dicts to be stored, :param filename: (str) path to the output fil