CodeGen
Last updated
Last updated
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong.
CodeGen is an autoregressive language model for program synthesis trained sequentially on , BigQuery, and BigPython.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI’s Codex on the HumanEval benchmark. We make the training library JaxFormer including checkpoints available as open source contribution: .
This model was contributed by . The original code can be found .
CodeGen model are available on different pre-training data with variable sizes.
The format is: Salesforce/codegen-{size}-{data}, where
size: 350M, 2B, 6B, 16B
data:
nl: Pre-trained on the Pile
multi: Initialized with nl, then further pre-trained on multiple programming languages data
mono: Initialized with multi, then further pre-trained on Python data
For example, Salesforce/codegen-350M-mono offers a 350 million-parameter checkpoint pre-trained sequentially on the Pile, multiple programming languages, and Python.
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( vocab_size = 50400n_positions = 2048n_ctx = 2048n_embd = 4096n_layer = 28n_head = 16rotary_dim = 64n_inner = Noneactivation_function = 'gelu_new'resid_pdrop = 0.0embd_pdrop = 0.0attn_pdrop = 0.0layer_norm_epsilon = 1e-05initializer_range = 0.02use_cache = Truebos_token_id = 50256eos_token_id = 50256tie_word_embeddings = False**kwargs )
Parameters
n_positions (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).
n_embd (int, optional, defaults to 4096) — Dimensionality of the embeddings and hidden states.
n_layer (int, optional, defaults to 28) — Number of hidden layers in the Transformer encoder.
n_head (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (int, optional, defaults to 64) — Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (int, optional, defaults to None) — Dimensionality of the inner feed-forward layers. None will set it to 4 times n_embd
activation_function (str, optional, defaults to "gelu_new") — Activation function, to be selected in the list ["relu", "silu", "gelu", "tanh", "gelu_new"].
resid_pdrop (float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (int, optional, defaults to 0.1) — The dropout ratio for the embeddings.
attn_pdrop (float, optional, defaults to 0.1) — The dropout ratio for the attention.
layer_norm_epsilon (float, optional, defaults to 1e-5) — The epsilon to use in the layer normalization layers.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models).
Example:
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( vocab_filemerges_fileerrors = 'replace'unk_token = '<|endoftext|>'bos_token = '<|endoftext|>'eos_token = '<|endoftext|>'pad_token = Noneadd_prefix_space = Falseadd_bos_token = False**kwargs )
Parameters
vocab_file (str) — Path to the vocabulary file.
merges_file (str) — Path to the merges file.
unk_token (str, optional, defaults to <|endoftext|>) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
bos_token (str, optional, defaults to <|endoftext|>) — The beginning of sequence token.
eos_token (str, optional, defaults to <|endoftext|>) — The end of sequence token.
add_prefix_space (bool, optional, defaults to False) — Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space).
Construct a CodeGen tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
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You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one).
save_vocabulary
( save_directory: strfilename_prefix: typing.Optional[str] = None )
( vocab_file = Nonemerges_file = Nonetokenizer_file = Noneunk_token = '<|endoftext|>'bos_token = '<|endoftext|>'eos_token = '<|endoftext|>'add_prefix_space = False**kwargs )
Parameters
vocab_file (str) — Path to the vocabulary file.
merges_file (str) — Path to the merges file.
unk_token (str, optional, defaults to <|endoftext|>) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
bos_token (str, optional, defaults to <|endoftext|>) — The beginning of sequence token.
eos_token (str, optional, defaults to <|endoftext|>) — The end of sequence token.
add_prefix_space (bool, optional, defaults to False) — Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (CodeGen tokenizer detect beginning of words by the preceding space).
trim_offsets (bool, optional, defaults to True) — Whether or not the post-processing step should trim offsets to avoid including whitespaces.
Construct a “fast” CodeGen tokenizer (backed by BOINCAI’s tokenizers library). Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
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You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer, but since the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True.
decode
( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_up_tokenization_spaces: bool = Nonetruncate_before_pattern: typing.Optional[typing.List[str]] = None**kwargs ) → str
Parameters
token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces (available in the tokenizer_config).
truncate_before_pattern (List[str], optional, defaults to None) — A list of regular expression strings that will be used to truncate the returned string. This can be used to remove extra pieces of code (e.g. truncate if observing a comment symbol ”#” at the beginning of a new line). An example pattern could be `[”^#”, re.escape(”<|endoftext|>”), ”^'''”, ”
Returns
str
The decoded sentence.
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces.
Similar to doing self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids)).
”]`. kwargs (additional keyword arguments, optional): Will be passed to the underlying model specific decode method.
( config )
Parameters
forward
Parameters
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.
attention_mask (torch.FloatTensor 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.
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
head_mask (torch.FloatTensor of shape (num_attention_heads,) or (n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
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.
Returns
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.
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:
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( config )
Parameters
The CodeGen Model transformer with a language modeling head on top.
forward
Parameters
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.
attention_mask (torch.FloatTensor 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.
token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
head_mask (torch.FloatTensor of shape (num_attention_heads,) or (n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
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.LongTensor of shape (batch_size, sequence_length), optional) — Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can set labels = input_ids Indices are selected in [-100, 0, ..., config.vocab_size] All labels set to -100 are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size]
Returns
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.
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:
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vocab_size (int, optional, defaults to 50400) — Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling .
This is the configuration class to store the configuration of a . It is used to instantiate a CodeGen 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 CodeGen architecture. Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.
errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. See for more information.
This tokenizer inherits from which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. See for more information.
This tokenizer inherits from which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
config () — 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 method to load the model weights.
The bare CodeGen Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( input_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)
Indices can be obtained using AutoProcenizer. See and for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
or tuple(torch.FloatTensor)
A 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 () and inputs.
The forward method, overrides the __call__ special method.
config () — 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 method to load the model weights.
This model is a PyTorch sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( input_ids: typing.Optional[torch.LongTensor] = Nonepast_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = Noneattention_mask: typing.Optional[torch.FloatTensor] = Nonetoken_type_ids: typing.Optional[torch.LongTensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Nonehead_mask: typing.Optional[torch.FloatTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)
Indices can be obtained using AutoProcenizer. See and for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
or tuple(torch.FloatTensor)
A 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 () and inputs.
The forward method, overrides the __call__ special method.