
 
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
Tips:
This model was contributed by OpenBMB. The original code can be found here.
⚙️ Training & Inference
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 0.1) —
The dropout probabilitiy 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-6) —
The epsilon used by the layer normalization layers.
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.
init_std (float, optional, defaults to 1.0) —
Initialize parameters with std = init_std.
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:
	Copied
 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.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
)
The bare CPMAnt Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters
config (~CpmAntConfig): Model configuration class with all the parameters of the
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.
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?
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
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.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers.
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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — 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)) and optionally if
config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
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.
Example:
	Copied
 from transformers import AutoTokenizer, CpmAntModel
 import torch
 tokenizer = AutoTokenizer.from_pretrained("openbmb/cpm-ant-10b")
 model = CpmAntModel.from_pretrained("openbmb/cpm-ant-10b")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
CpmAntForCausalLM
	
class transformers.CpmAntForCausalLM
<
source
>
(
config: CpmAntConfig
)
The CPMAnt Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters
config (~CpmAntConfig): Model configuration class with all the parameters of the
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.
forward
<
source
>
(
input_ids: typing.Optional[torch.Tensor] = None
past_key_values: typing.Union[typing.List[typing.Tuple[torch.Tensor, torch.Tensor]], NoneType] = 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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
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.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Args —
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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True):
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
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.
output_hidden_states (bool, optional):
Whether or not to return the hidden states of all layers.
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):
CPMAnt will process attention mask automatically, this parameter is a dummy parameter for
text-generation pipeline.
Example —
Text Generation with CpmAntForCausalLM. —
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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — 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))
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:
	Copied
 import torch
 from transformers import AutoTokenizer, CpmAntForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("openbmb/cpm-ant-10b")
 model = CpmAntForCausalLM.from_pretrained("openbmb/cpm-ant-10b")
 inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])
 loss = outputs.loss
 logits = outputs.logits
←CPM
CTRL→
CPMAnt
Overview
CpmAntConfig
CpmAntTokenizer
CpmAntModel
CpmAntForCausalLM
