#
CPMAnt

##
Overview

CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B parameters. It is also the first milestone of the live training process of CPM-Live. The training process is cost-effective and environment-friendly. CPM-Ant also achieves promising results with delta tuning on the CUGE benchmark. Besides the full model, we also provide various compressed versions to meet the requirements of different hardware configurations. See more

This model was contributed by OpenBMB. The original code can be found here.

##
Resources

- A tutorial on CPM-Live.

##
CpmAntConfig

###
class transformers.CpmAntConfig

< source >

( vocab_size: int = 30720 hidden_size: int = 4096 num_attention_heads: int = 32 dim_head: int = 128 dim_ff: int = 10240 num_hidden_layers: int = 48 dropout_p: int = 0.0 position_bias_num_buckets: int = 512 position_bias_max_distance: int = 2048 eps: int = 1e-06 init_std: float = 1.0 prompt_types: int = 32 prompt_length: int = 32 segment_types: int = 32 use_cache: bool = True **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 30720) — Vocabulary size of the CPMAnt model. Defines the number of different tokens that can be represented by the `input` passed when calling CpmAntModel.
- hidden_size (`int`, optional, defaults to 4096) — Dimension of the encoder layers.
- num_attention_heads (`int`, optional, defaults to 32) — Number of attention heads in the Transformer encoder.
- dim_head (`int`, optional, defaults to 128) — Dimension of attention heads for each attention layer in the Transformer encoder.
- dim_ff (`int`, optional, defaults to 10240) — Dimension of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
- num_hidden_layers (`int`, optional, defaults to 48) — Number of layers of the Transformer encoder.
- dropout_p (`float`, optional, defaults to 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder.
- position_bias_num_buckets (`int`, optional, defaults to 512) — The number of position_bias buckets.
- position_bias_max_distance (`int`, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
- eps (`float`, optional, defaults to 1e-06) — The epsilon used by the layer normalization layers.
- init_std (`float`, optional, defaults to 1.0) — Initialize parameters with std = init_std.
- prompt_types (`int`, optional, defaults to 32) — The type of prompt.
- prompt_length (`int`, optional, defaults to 32) — The length of prompt.
- segment_types (`int`, optional, defaults to 32) — The type of segment.
- use_cache (`bool`, optional, defaults to `True`) — Whether to use cache.

This is the configuration class to store the configuration of a CpmAntModel. It is used to instantiate an CPMAnt model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the CPMAnt openbmb/cpm-ant-10b architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import CpmAntModel, CpmAntConfig

    >>> # Initializing a CPMAnt cpm-ant-10b style configuration
    >>> configuration = CpmAntConfig()

    >>> # Initializing a model from the cpm-ant-10b style configuration
    >>> model = CpmAntModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
CpmAntTokenizer

###
class transformers.CpmAntTokenizer

< source >

( vocab_file bod_token = '<d>' eod_token = '</d>' bos_token = '<s>' eos_token = '</s>' pad_token = '<pad>' unk_token = '<unk>' line_token = '</n>' space_token = '</_>' padding_side = 'left' **kwargs )

Parameters

- vocab_file (`str`) — Path to the vocabulary file.
- bod_token (`str`, optional, defaults to `"<d>"`) — The beginning of document token.
- eod_token (`str`, optional, defaults to `"</d>"`) — The end of document token.
- bos_token (`str`, optional, defaults to `"<s>"`) — The beginning of sequence token.
- eos_token (`str`, optional, defaults to `"</s>"`) — The end of sequence token.
- pad_token (`str`, optional, defaults to `"<pad>"`) — The token used for padding.
- unk_token (`str`, optional, defaults to `"<unk>"`) — The unknown token.
- line_token (`str`, optional, defaults to `"</n>"`) — The line token.
- space_token (`str`, optional, defaults to `"</_>"`) — The space token.

Construct a CPMAnt tokenizer. Based on byte-level Byte-Pair-Encoding.

####
build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — The first tokenized sequence that special tokens will be added.
- token_ids_1 (`List[int]`) — The optional second tokenized sequence that special tokens will be added.

Returns

`List[int]`

The model input with special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A CPMAnt sequence has the following format:

- single sequence: `[BOS] Sequence`.

####
get_special_tokens_mask

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.
- already_has_special_tokens (`bool`, optional, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

Returns

`List[int]`

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method.

##
CpmAntModel

###
class transformers.CpmAntModel

< source >

( config: CpmAntConfig )

Parameters

- config (CpmAntConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Cpmant Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None use_cache: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs ) →
transformers.modeling_outputs.BaseModelOutputWithPast or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using `CPMAntTokenizer`. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- past_key_values (`Tuple[Tuple[torch.Tensor]]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPast or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPast or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (CpmAntConfig) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output.

- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The CpmAntModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
CpmAntForCausalLM

###
class transformers.CpmAntForCausalLM

< source >

( config: CpmAntConfig )

Parameters

- config (CpmAntConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The CPMAnt Model with a language modeling head on top (linear layer with weights tied to the input embeddings).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.List[typing.Tuple[torch.Tensor, torch.Tensor]]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None return_dict: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None **kwargs ) →
transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, seq_len)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using `CPMAntTokenizer`. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- past_key_values (`List[Tuple[torch.Tensor, torch.Tensor]]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

Returns

transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (CpmAntConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss (for next-token prediction).

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The CpmAntForCausalLM forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Text Generation with CpmAntForCausalLM.

    >>> from transformers import CPMAntTokenizer, CpmAntForCausalLM

    >>> texts = "今天天气不错，"
    >>> model = CpmAntForCausalLM.from_pretrained("openbmb/cpm-ant-10b")
    >>> tokenizer = CPMAntTokenizer.from_pretrained("openbmb/cpm-ant-10b")
    >>> input_ids = tokenizer(texts, return_tensors="pt")
    >>> outputs = model.generate(**input_ids)
    >>> output_texts = tokenizer.batch_decode(outputs)
    >>> print(output_texts)
    ['今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的']

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