# Copyright 2024 Cohere team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is based on the tokenization_llama.py file in transformers
from typing import Literal
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
from tokenizers.models import BPE
from ...tokenization_utils_tokenizers import TokenizersBackend
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"tokenizer_file": {
"Cohere/Command-nightly": "https://huggingface.co/Cohere/Command-nightly/blob/main/tokenizer.json",
},
}
# fmt: off
DEFAULT_SYSTEM_PROMPT = "You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere."
DEFAULT_RAG_PREAMBLE = """## Task and Context
You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.
## Style Guide
Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling."""
# fmt: on
class CohereTokenizer(TokenizersBackend):
"""
Construct a Cohere tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and NFC normalization.
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
>>> tokenizer.encode("Hello this is a test")
[5, 28339, 2075, 1801, 1671, 3282]
```
If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
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`.
This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
[tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
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` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|END_OF_TURN_TOKEN|>"`):
The end of sequence token.
add_bos_token (`bool`, *optional*, defaults to `True`):
Whether or not to add an `bos_token` at the start of sequences.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
use_default_system_prompt (`bool`, *optional*, defaults to `False`):
Whether or not the default system prompt for Cohere tokenizer should be used.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not the tokenizer should automatically add a prefix space
vocab (`str`, `dict` or `list`, *optional*):
Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.
merges (`str` or `list[str]`, *optional*):
Custom merges list. If not provided, merges are loaded from `merges_file`.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"]
model = BPE
# No `max_model_input_sizes`
def __init__(
self,
vocab: str | dict[str, int] | None = None,
merges: str | list[str] | None = None,
errors: str = "replace",
unk_token: str = "",
bos_token: str = "",
eos_token: str = "<|END_OF_TURN_TOKEN|>",
pad_token: str = "",
cls_token: str = "",
sep_token: str = "",
mask_token: str = "",
use_default_system_prompt: bool = False,
add_prefix_space: bool = False,
**kwargs,
):
self.use_default_system_prompt = use_default_system_prompt
self.add_prefix_space = add_prefix_space
self.grounded_generation_template = kwargs.pop("grounded_generation_template", None)
self.tool_use_template = kwargs.pop("tool_use_template", None)
self._vocab = (
vocab
if vocab is not None
else {
str(pad_token): 0,
str(unk_token): 1,
str(cls_token): 2,
str(sep_token): 3,
str(mask_token): 4,
str(bos_token): 5,
}
)
self._merges = merges or []
self._tokenizer = Tokenizer(
BPE(
vocab=self._vocab,
merges=self._merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
self._tokenizer.normalizer = normalizers.NFC()
self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Digits(individual_digits=True),
pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space, trim_offsets=True),
]
)
self._tokenizer.decoder = decoders.ByteLevel(add_prefix_space=add_prefix_space, trim_offsets=True)
super().__init__(
errors=errors,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
cls_token=cls_token,
sep_token=sep_token,
mask_token=mask_token,
use_default_system_prompt=use_default_system_prompt,
add_prefix_space=add_prefix_space,
**kwargs,
)
self._post_init()
def apply_tool_use_template(
self,
conversation: list[dict[str, str]],
tools: list[dict],
**kwargs,
) -> str | list[int]:
"""Create a Command-R tool-use prompt.
Once rendered, the prompt instructs the model to generate a list of actions to perform on a set of user supplied tools
to help carry out the user's requests.
Conceptually, this works in the same way as `apply_chat_format`, but takes an additional `tools` parameter.
Converts a chat in the form of a list of dictionaries with `"role"` and `"content"` keys and a list of available
tools for the model to use into a prompt string, or a list of token ids.
This method will use the tokenizer's `default_tool_use_template` template specified at the class level.
You can override the default template using the `tool_use_template` kwarg but the quality of your results may decrease.
Args:
conversation (list[dict[str, str]]): A list of dicts
with "role" and "content" keys, representing the chat history so far.
tools (list[Dict]): a list of tools to render into the prompt for the model to choose from.
See an example at the bottom of the docstring.
The format should be:
* name (str): The name of the tool to be called. Valid names contain only the characters a-z,
A-Z, 0-9, _ and must not begin with a digit.
* description (str): The description of what the tool does, the model uses the description to
choose when and how to call the function.
* parameter_definitions (list[Dict]): The input parameters of the tool. Accepts a dictionary
where the key is the name of the parameter and the value is the parameter spec.
Valid parameter names contain only the characters a-z, A-Z, 0-9, _ and must not begin with a digit.
Parameter specs are as follows:
* description (str): The description of the parameter.
* type (str): the type of the parameter - most effective for python builtin data types, such as 'str', 'bool'
* required: boolean: Denotes whether the parameter is always present (required) or not. Defaults to not required.
add_generation_prompt (bool, *optional*): Whether to end the prompt with the token(s) that indicate
the start of an assistant message. This is useful when you want to generate a response from the model.
Note that this argument will be passed to the chat template, and so it must be supported in the
template for this argument to have any effect.
tokenize (`bool`, defaults to `True`):
Whether to tokenize the output. If `False`, the output will be a string.
padding (`bool`, defaults to `False`):
Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`.
truncation (`bool`, defaults to `False`):
Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.
max_length (`int`, *optional*):
Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If
not specified, the tokenizer's `max_length` attribute will be used as a default.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable
values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
return_dict (`bool`, *optional*, defaults to `False`):
Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
**tokenizer_kwargs: Additional kwargs to pass to the tokenizer.
Returns:
`str`: A rendered prompt string.
or if tokenize=True:
`list[int]`: A list of token ids representing the tokenized chat so far, including control tokens. This
output is ready to pass to the model, either directly or via methods like `generate()`.
Examples:
```python
>> tokenizer = CohereTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01")
>> tools = [
{
"name": "internet_search",
"description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
"parameter_definitions": {
"query": {
"description": "Query to search the internet with",
"type": "str",
"required": True,
}
},
},
{
"name": "directly_answer",
"description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history",
"parameter_definitions": {},
},
]
>> conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"},
]
>> # Render the prompt, ready for user to inspect, or for input into the model
>> prompt = tokenizer.apply_tool_use_template(conversation, tools=tools, tokenize=False, add_generation_prompt=True)
>> print(prompt)
>> inputs = tokenizer.encode(grounded_generation_prompt, add_special_tokens=False, return_tensors='pt')
>> outputs = model.generate(inputs, max_new_tokens=128)
>> print(tokenizer.decode(outputs[0]))
[
{
"tool_name": "internet_search",
"parameters": {
"query": "biggest penguin in the world"
}
}
]
```
"""
return self.apply_chat_template(
conversation,
chat_template="tool_use",
tools=tools,
**kwargs,
)
def apply_grounded_generation_template(
self,
conversation: list[dict[str, str]],
documents: list[dict],
citation_mode: Literal["fast", "accurate"] = "accurate",
**kwargs,
) -> str | list[int]:
"""Create a Command-R grounded generation (aka RAG) prompt.
Once rendered, the prompt instructs the model to generate a response with citations in, based on supplied documents.
Conceptually, this works in the same way as `apply_chat_format`, but takes additional `documents`
and parameter `citation_mode` parameters.
Converts a list of dictionaries with `"role"` and `"content"` keys and a list of
documents for the model to ground its response on into a prompt string, or a list of token ids.
This method will use the tokenizer's `grounded_generation_template` template specified at the class level.
You can override the default template using the `grounded_generation_template` kwarg but the quality of your results may decrease.
Args:
conversation (list[dict[str, str]]): A list of dicts
with "role" and "content" keys, representing the chat history so far.
documents (list[dict[str, str]): A list of dicts, representing documents or tool outputs to ground your
generation on. A document is a semistructured dict, with a string to string mapping. Common fields are
`url`, `title`, `snippet` etc but should be descriptive of the key. They will get rendered into the prompt.
citation_mode: either "accurate" (prompt the model to generate an answer first, then rewrite it with citation
spans in) or "fast", where the prompt instructs the model to generate an answer with citations in directly.
The former has higher quality citations, the latter requires fewer tokens to be generated.
add_generation_prompt (bool, *optional*): Whether to end the prompt with the token(s) that indicate
the start of an assistant message. This is useful when you want to generate a response from the model.
Note that this argument will be passed to the chat template, and so it must be supported in the
template for this argument to have any effect.
tokenize (`bool`, defaults to `True`):
Whether to tokenize the output. If `False`, the output will be a string.
padding (`bool`, defaults to `False`):
Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`.
truncation (`bool`, defaults to `False`):
Whether to truncate sequences at the maximum length. Has no effect if tokenize is `False`.
max_length (`int`, *optional*):
Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is `False`. If
not specified, the tokenizer's `max_length` attribute will be used as a default.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable
values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
return_dict (`bool`, *optional*, defaults to `False`):
Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
**tokenizer_kwargs: Additional kwargs to pass to the tokenizer.
Returns:
`str`: A rendered prompt string.
or if tokenize=True:
`list[int]`: A list of token ids representing the tokenized chat so far, including control tokens. This
output is ready to pass to the model, either directly or via methods like `generate()`.
Examples:
```python
>> tokenizer = CohereTokenizer.from_pretrained('CohereForAI/c4ai-command-r-v01')
>> # define documents:
>> documents = [
{ "title": "Tall penguins", "text": "Emperor penguins are the tallest." },
{ "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}
]
>> # define a conversation:
>> conversation = [
{"role": "user", "content": "Whats the biggest penguin in the world?"}
]
>> # render the prompt, ready for user to inspect, or for input into the model:
>> grounded_generation_prompt = tokenizer.apply_grounded_generation_template(conversation, documents=documents, tokenize=False, add_generation_prompt=True)
>> print(grounded_generation_prompt)
>> inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
>> outputs = model.generate(inputs, max_new_tokens=128)
>> print(tokenizer.decode(outputs[0]))
```
"""
return self.apply_chat_template(
conversation,
chat_template="rag",
documents=documents,
citation_mode=citation_mode,
**kwargs,
)
__all__ = ["CohereTokenizer"]