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from
__future__
import
annotations
import
os
import
sys
import
json
import
ctypes
import
dataclasses
import
random
import
string
from
contextlib
import
ExitStack
from
typing
import
(
Any
,
Dict
,
Iterator
,
List
,
Literal
,
Optional
,
Tuple
,
Union
,
Protocol
,
cast
,
)
import
jinja2
from
jinja2
.
sandbox
import
ImmutableSandboxedEnvironment
import
numpy
as
np
import
numpy
.
typing
as
npt
import
llama_cpp
.
llama
as
llama
import
llama_cpp
.
llama_types
as
llama_types
import
llama_cpp
.
llama_grammar
as
llama_grammar
from
.
_logger
import
logger
from
.
_utils
import
suppress_stdout_stderr
,
Singleton
### Common Chat Templates and Special Tokens ###
# Source: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json
CHATML_CHAT_TEMPLATE
=
"{% for message in messages %}{{'<|im_start|>' + message['role'] + '
\n
' + message['content'] + '<|im_end|>' + '
\n
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
\n
' }}{% endif %}"
CHATML_BOS_TOKEN
=
"<s>"
CHATML_EOS_TOKEN
=
"<|im_end|>"
# Source: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
MISTRAL_INSTRUCT_CHAT_TEMPLATE
=
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
MISTRAL_INSTRUCT_BOS_TOKEN
=
"<s>"
MISTRAL_INSTRUCT_EOS_TOKEN
=
"</s>"
# Source: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1/blob/main/tokenizer_config.json
MIXTRAL_INSTRUCT_CHAT_TEMPLATE
=
"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
# Source: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer_config.json
LLAMA3_INSTRUCT_CHAT_TEMPLATE
=
"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
\n
\n
'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
\n
\n
' }}{% endif %}"
### Chat Completion Handler ###
class
LlamaChatCompletionHandler
(
Protocol
):
"""Base Protocol for a llama chat completion handler.
Very generic protocol that can be used to implement any chat format.
The only hard requirement is that it must return a ChatCompletion when
stream=False and an iterator of ChatCompletionChunks when stream=True."""
def
__call__
(
self
,
*
,
# llama.cpp instance
llama
:
llama
.
Llama
,
# openai api parameters
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
functions
:
Optional
[
List
[
llama_types
.
ChatCompletionFunction
]]
=
None
,
function_call
:
Optional
[
llama_types
.
ChatCompletionRequestFunctionCall
]
=
None
,
tools
:
Optional
[
List
[
llama_types
.
ChatCompletionTool
]]
=
None
,
tool_choice
:
Optional
[
llama_types
.
ChatCompletionToolChoiceOption
]
=
None
,
temperature
:
float
=
0.2
,
top_p
:
float
=
0.95
,
top_k
:
int
=
40
,
stream
:
bool
=
False
,
stop
:
Optional
[
Union
[
str
,
List
[
str
]]]
=
[],
seed
:
Optional
[
int
]
=
None
,
response_format
:
Optional
[
llama_types
.
ChatCompletionRequestResponseFormat
]
=
None
,
max_tokens
:
Optional
[
int
]
=
None
,
presence_penalty
:
float
=
0.0
,
frequency_penalty
:
float
=
0.0
,
repeat_penalty
:
float
=
1.1
,
model
:
Optional
[
str
]
=
None
,
logit_bias
:
Optional
[
Dict
[
str
,
float
]]
=
None
,
# llama.cpp parameters
min_p
:
float
=
0.05
,
typical_p
:
float
=
1.0
,
tfs_z
:
float
=
1.0
,
mirostat_mode
:
int
=
0
,
mirostat_tau
:
float
=
5.0
,
mirostat_eta
:
float
=
0.1
,
logits_processor
:
Optional
[
llama
.
LogitsProcessorList
]
=
None
,
grammar
:
Optional
[
llama
.
LlamaGrammar
]
=
None
,
logprobs
:
Optional
[
bool
]
=
None
,
top_logprobs
:
Optional
[
int
]
=
None
,
**
kwargs
,
# type: ignore
)
->
Union
[
llama_types
.
CreateChatCompletionResponse
,
Iterator
[
llama_types
.
CreateChatCompletionStreamResponse
],
]: ...
class
LlamaChatCompletionHandlerNotFoundException
(
Exception
):
pass
class
LlamaChatCompletionHandlerRegistry
(
Singleton
):
_chat_handlers
:
Dict
[
str
,
LlamaChatCompletionHandler
]
=
{}
def
register_chat_completion_handler
(
self
,
name
:
str
,
chat_handler
:
LlamaChatCompletionHandler
,
overwrite
:
bool
=
False
,
):
if
not
overwrite
and
name
in
self
.
_chat_handlers
:
raise
ValueError
(
f"Formatter with name '
{
name
}
' is already registered. Use `overwrite=True` to overwrite it."
)
self
.
_chat_handlers
[
name
]
=
chat_handler
def
unregister_chat_handler
(
self
,
name
:
str
):
if
name
in
self
.
_chat_handlers
:
del
self
.
_chat_handlers
[
name
]
else
:
raise
ValueError
(
f"No formatter registered under the name '
{
name
}
'."
)
def
get_chat_completion_handler_by_name
(
self
,
name
:
str
)
->
LlamaChatCompletionHandler
:
try
:
chat_handler
=
self
.
_chat_handlers
[
name
]
return
chat_handler
except
KeyError
:
raise
LlamaChatCompletionHandlerNotFoundException
(
f"Invalid chat handler:
{
name
}
(valid formats:
{
list
(
self
.
_chat_handlers
.
keys
())
}
)"
)
def
get_chat_completion_handler
(
name
:
str
)
->
LlamaChatCompletionHandler
:
return
LlamaChatCompletionHandlerRegistry
().
get_chat_completion_handler_by_name
(
name
)
def
register_chat_completion_handler
(
name
:
str
):
def
decorator
(
f
:
LlamaChatCompletionHandler
):
LlamaChatCompletionHandlerRegistry
().
register_chat_completion_handler
(
name
,
f
)
return
f
return
decorator
### Chat Formatter ###
@
dataclasses
.
dataclass
class
ChatFormatterResponse
:
"""Dataclass that stores completion parameters for a given chat format and
create_chat_completion request.
prompt contains the formatted prompt generated from the chat format and messages.
stop contains the stop token or list of stop tokens to use for the chat format."""
prompt
:
str
stop
:
Optional
[
Union
[
str
,
List
[
str
]]]
=
None
stopping_criteria
:
Optional
[
llama
.
StoppingCriteriaList
]
=
None
added_special
:
bool
=
False
class
ChatFormatter
(
Protocol
):
"""Base Protocol for a chat formatter. A chat formatter is a function that
takes a list of messages and returns a chat format response which can be used
to generate a completion. The response can also include a stop token or list
of stop tokens to use for the completion."""
def
__call__
(
self
,
*
,
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
**
kwargs
:
Any
,
)
->
ChatFormatterResponse
: ...
class
Jinja2ChatFormatter
(
ChatFormatter
):
def
__init__
(
self
,
template
:
str
,
eos_token
:
str
,
bos_token
:
str
,
add_generation_prompt
:
bool
=
True
,
stop_token_ids
:
Optional
[
List
[
int
]]
=
None
,
):
"""A chat formatter that uses jinja2 templates to format the prompt."""
self
.
template
=
template
self
.
eos_token
=
eos_token
self
.
bos_token
=
bos_token
self
.
add_generation_prompt
=
add_generation_prompt
self
.
stop_token_ids
=
(
set
(
stop_token_ids
)
if
stop_token_ids
is
not
None
else
None
)
self
.
_environment
=
ImmutableSandboxedEnvironment
(
loader
=
jinja2
.
BaseLoader
(),
trim_blocks
=
True
,
lstrip_blocks
=
True
,
).
from_string
(
self
.
template
)
def
__call__
(
self
,
*
,
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
functions
:
Optional
[
List
[
llama_types
.
ChatCompletionFunction
]]
=
None
,
function_call
:
Optional
[
llama_types
.
ChatCompletionRequestFunctionCall
]
=
None
,
tools
:
Optional
[
List
[
llama_types
.
ChatCompletionTool
]]
=
None
,
tool_choice
:
Optional
[
llama_types
.
ChatCompletionToolChoiceOption
]
=
None
,
**
kwargs
:
Any
,
)
->
ChatFormatterResponse
:
def
raise_exception
(
message
:
str
):
raise
ValueError
(
message
)
prompt
=
self
.
_environment
.
render
(
messages
=
messages
,
eos_token
=
self
.
eos_token
,
bos_token
=
self
.
bos_token
,
raise_exception
=
raise_exception
,
add_generation_prompt
=
self
.
add_generation_prompt
,
functions
=
functions
,
function_call
=
function_call
,
tools
=
tools
,
tool_choice
=
tool_choice
,
)
stopping_criteria
=
None
if
self
.
stop_token_ids
is
not
None
:
def
stop_on_last_token
(
tokens
:
npt
.
NDArray
[
np
.
intc
],
logits
:
npt
.
NDArray
[
np
.
single
]
)
->
bool
:
return
tokens
[
-
1
]
in
self
.
stop_token_ids
stopping_criteria
=
llama
.
StoppingCriteriaList
([
stop_on_last_token
])
return
ChatFormatterResponse
(
prompt
=
prompt
,
stop
=
[
self
.
eos_token
],
stopping_criteria
=
stopping_criteria
,
added_special
=
True
,
)
def
to_chat_handler
(
self
)
->
LlamaChatCompletionHandler
:
return
chat_formatter_to_chat_completion_handler
(
self
)
def
_convert_text_completion_logprobs_to_chat
(
logprobs
:
Optional
[
llama_types
.
CompletionLogprobs
],
)
->
llama_types
.
ChatCompletionLogprobs
:
if
logprobs
is
None
:
return
None
return
{
"content"
: [
{
"token"
:
token
,
"bytes"
:
None
,
"logprob"
:
logprob
,
"top_logprobs"
: [
{
"token"
:
top_token
,
"logprob"
:
top_logprob
,
"bytes"
:
None
,
}
for
top_token
,
top_logprob
in
top_logprobs
.
items
()
],
}
for
(
token
,
logprob
,
top_logprobs
)
in
zip
(
logprobs
[
"tokens"
],
logprobs
[
"token_logprobs"
],
logprobs
[
"top_logprobs"
])
],
"refusal"
:
None
,
}
def
_convert_text_completion_to_chat
(
completion
:
llama_types
.
Completion
,
)
->
llama_types
.
ChatCompletion
:
assert
"usage"
in
completion
return
{
"id"
:
"chat"
+
completion
[
"id"
],
"object"
:
"chat.completion"
,
"created"
:
completion
[
"created"
],
"model"
:
completion
[
"model"
],
"choices"
: [
{
"index"
:
0
,
"message"
: {
"role"
:
"assistant"
,
"content"
:
completion
[
"choices"
][
0
][
"text"
],
},
"logprobs"
:
_convert_text_completion_logprobs_to_chat
(
completion
[
"choices"
][
0
][
"logprobs"
]),
"finish_reason"
:
completion
[
"choices"
][
0
][
"finish_reason"
],
}
],
"usage"
:
completion
[
"usage"
],
}
def
_convert_text_completion_chunks_to_chat
(
chunks
:
Iterator
[
llama_types
.
CreateCompletionStreamResponse
],
)
->
Iterator
[
llama_types
.
ChatCompletionChunk
]:
for
i
,
chunk
in
enumerate
(
chunks
):
if
i
==
0
:
yield
{
"id"
:
"chat"
+
chunk
[
"id"
],
"model"
:
chunk
[
"model"
],
"created"
:
chunk
[
"created"
],
"object"
:
"chat.completion.chunk"
,
"choices"
: [
{
"index"
:
0
,
"delta"
: {
"role"
:
"assistant"
,
},
"logprobs"
:
None
,
"finish_reason"
:
None
,
}
],
}
yield
{
"id"
:
"chat"
+
chunk
[
"id"
],
"model"
:
chunk
[
"model"
],
"created"
:
chunk
[
"created"
],
"object"
:
"chat.completion.chunk"
,
"choices"
: [
{
"index"
:
0
,
"delta"
: (
{
"content"
:
chunk
[
"choices"
][
0
][
"text"
],
}
if
chunk
[
"choices"
][
0
][
"finish_reason"
]
is
None
else
{}
),
"logprobs"
:
_convert_text_completion_logprobs_to_chat
(
chunk
[
"choices"
][
0
][
"logprobs"
]),
"finish_reason"
:
chunk
[
"choices"
][
0
][
"finish_reason"
],
}
],
}
def
_convert_completion_to_chat
(
completion_or_chunks
:
Union
[
llama_types
.
CreateCompletionResponse
,
Iterator
[
llama_types
.
CreateCompletionStreamResponse
],
],
stream
:
bool
=
False
,
)
->
Union
[
llama_types
.
CreateChatCompletionResponse
,
Iterator
[
llama_types
.
ChatCompletionChunk
]
]:
if
stream
:
chunks
:
Iterator
[
llama_types
.
CreateCompletionStreamResponse
]
=
completion_or_chunks
# type: ignore
return
_convert_text_completion_chunks_to_chat
(
chunks
)
else
:
completion
:
llama_types
.
Completion
=
completion_or_chunks
# type: ignore
return
_convert_text_completion_to_chat
(
completion
)
def
_convert_completion_to_chat_function
(
tool_name
:
str
,
completion_or_chunks
:
Union
[
llama_types
.
CreateCompletionResponse
,
Iterator
[
llama_types
.
CreateCompletionStreamResponse
],
],
stream
:
bool
,
):
if
not
stream
:
completion
:
llama_types
.
CreateCompletionResponse
=
completion_or_chunks
# type: ignore
assert
"usage"
in
completion
tool_id
=
"call_"
+
"_0_"
+
tool_name
+
"_"
+
completion
[
"id"
]
# TODO: Fix for legacy function calls
chat_completion
:
llama_types
.
CreateChatCompletionResponse
=
{
"id"
:
"chat"
+
completion
[
"id"
],
"object"
:
"chat.completion"
,
"created"
:
completion
[
"created"
],
"model"
:
completion
[
"model"
],
"choices"
: [
{
"index"
:
0
,
"message"
: {
"role"
:
"assistant"
,
"content"
:
None
,
"function_call"
: {
"name"
:
tool_name
,
"arguments"
:
completion
[
"choices"
][
0
][
"text"
],
},
"tool_calls"
: [
{
"id"
:
tool_id
,
"type"
:
"function"
,
"function"
: {
"name"
:
tool_name
,
"arguments"
:
completion
[
"choices"
][
0
][
"text"
],
},
}
],
},
"logprobs"
:
_convert_text_completion_logprobs_to_chat
(
completion
[
"choices"
][
0
][
"logprobs"
]),
"finish_reason"
:
"tool_calls"
,
}
],
"usage"
:
completion
[
"usage"
],
}
return
chat_completion
else
:
chunks
:
Iterator
[
llama_types
.
CreateCompletionStreamResponse
]
=
completion_or_chunks
# type: ignore
def
_stream_response_to_function_stream
(
chunks
:
Iterator
[
llama_types
.
CreateCompletionStreamResponse
],
)
->
Iterator
[
llama_types
.
CreateChatCompletionStreamResponse
]:
# blank first message
first
=
True
id_
=
None
created
=
None
model
=
None
tool_id
=
None
for
chunk
in
chunks
:
if
first
:
id_
=
"chat"
+
chunk
[
"id"
]
created
=
chunk
[
"created"
]
model
=
chunk
[
"model"
]
tool_id
=
"call_"
+
"_0_"
+
tool_name
+
"_"
+
chunk
[
"id"
]
yield
{
"id"
:
id_
,
"object"
:
"chat.completion.chunk"
,
"created"
:
created
,
"model"
:
model
,
"choices"
: [
{
"index"
:
0
,
"finish_reason"
:
None
,
"logprobs"
:
None
,
"delta"
: {
"role"
:
"assistant"
,
"content"
:
None
,
"function_call"
:
None
,
"tool_calls"
:
None
,
},
}
],
}
yield
{
"id"
:
"chat"
+
chunk
[
"id"
],
"object"
:
"chat.completion.chunk"
,
"created"
:
chunk
[
"created"
],
"model"
:
chunk
[
"model"
],
"choices"
: [
{
"index"
:
0
,
"finish_reason"
:
None
,
"logprobs"
:
_convert_text_completion_logprobs_to_chat
(
chunk
[
"choices"
][
0
][
"logprobs"
]),
"delta"
: {
"role"
:
None
,
"content"
:
None
,
"function_call"
: {
"name"
:
tool_name
,
"arguments"
:
chunk
[
"choices"
][
0
][
"text"
],
},
"tool_calls"
: [
{
"index"
:
0
,
"id"
:
tool_id
,
"type"
:
"function"
,
"function"
: {
"name"
:
tool_name
,
"arguments"
:
chunk
[
"choices"
][
0
][
"text"
],
},
}
],
},
}
],
}
first
=
False
continue
assert
tool_id
is
not
None
yield
{
"id"
:
"chat"
+
chunk
[
"id"
],
"object"
:
"chat.completion.chunk"
,
"created"
:
chunk
[
"created"
],
"model"
:
chunk
[
"model"
],
"choices"
: [
{
"index"
:
0
,
"finish_reason"
:
None
,
"logprobs"
:
_convert_text_completion_logprobs_to_chat
(
chunk
[
"choices"
][
0
][
"logprobs"
]),
"delta"
: {
"role"
:
None
,
"content"
:
None
,
"function_call"
: {
"name"
:
tool_name
,
"arguments"
:
chunk
[
"choices"
][
0
][
"text"
],
},
"tool_calls"
: [
{
"index"
:
0
,
"id"
:
tool_id
,
"type"
:
"function"
,
"function"
: {
"name"
:
tool_name
,
"arguments"
:
chunk
[
"choices"
][
0
][
"text"
],
},
}
],
},
}
],
}
if
id_
is
not
None
and
created
is
not
None
and
model
is
not
None
:
yield
{
"id"
:
id_
,
"object"
:
"chat.completion.chunk"
,
"created"
:
created
,
"model"
:
model
,
"choices"
: [
{
"index"
:
0
,
"finish_reason"
:
"tool_calls"
,
"logprobs"
:
None
,
"delta"
: {
"role"
:
None
,
"content"
:
None
,
"function_call"
:
None
,
"tool_calls"
:
None
,
},
}
],
}
return
_stream_response_to_function_stream
(
chunks
)
def
chat_formatter_to_chat_completion_handler
(
chat_formatter
:
ChatFormatter
,
)
->
LlamaChatCompletionHandler
:
def
chat_completion_handler
(
*
,
llama
:
llama
.
Llama
,
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
functions
:
Optional
[
List
[
llama_types
.
ChatCompletionFunction
]]
=
None
,
function_call
:
Optional
[
llama_types
.
ChatCompletionRequestFunctionCall
]
=
None
,
tools
:
Optional
[
List
[
llama_types
.
ChatCompletionTool
]]
=
None
,
tool_choice
:
Optional
[
llama_types
.
ChatCompletionToolChoiceOption
]
=
None
,
temperature
:
float
=
0.2
,
top_p
:
float
=
0.95
,
top_k
:
int
=
40
,
min_p
:
float
=
0.05
,
typical_p
:
float
=
1.0
,
stream
:
bool
=
False
,
stop
:
Optional
[
Union
[
str
,
List
[
str
]]]
=
[],
seed
:
Optional
[
int
]
=
None
,
response_format
:
Optional
[
llama_types
.
ChatCompletionRequestResponseFormat
]
=
None
,
max_tokens
:
Optional
[
int
]
=
None
,
presence_penalty
:
float
=
0.0
,
frequency_penalty
:
float
=
0.0
,
repeat_penalty
:
float
=
1.1
,
tfs_z
:
float
=
1.0
,
mirostat_mode
:
int
=
0
,
mirostat_tau
:
float
=
5.0
,
mirostat_eta
:
float
=
0.1
,
model
:
Optional
[
str
]
=
None
,
logits_processor
:
Optional
[
llama
.
LogitsProcessorList
]
=
None
,
grammar
:
Optional
[
llama
.
LlamaGrammar
]
=
None
,
logit_bias
:
Optional
[
Dict
[
str
,
float
]]
=
None
,
logprobs
:
Optional
[
bool
]
=
None
,
top_logprobs
:
Optional
[
int
]
=
None
,
**
kwargs
,
# type: ignore
)
->
Union
[
llama_types
.
CreateChatCompletionResponse
,
Iterator
[
llama_types
.
CreateChatCompletionStreamResponse
],
]:
result
=
chat_formatter
(
messages
=
messages
,
functions
=
functions
,
function_call
=
function_call
,
tools
=
tools
,
tool_choice
=
tool_choice
,
)
prompt
=
llama
.
tokenize
(
result
.
prompt
.
encode
(
"utf-8"
),
add_bos
=
not
result
.
added_special
,
special
=
True
,
)
if
result
.
stop
is
not
None
:
stop
=
[]
if
stop
is
None
else
[
stop
]
if
isinstance
(
stop
,
str
)
else
stop
rstop
=
result
.
stop
if
isinstance
(
result
.
stop
,
list
)
else
[
result
.
stop
]
stop
=
stop
+
rstop
stopping_criteria
=
None
if
result
.
stopping_criteria
is
not
None
:
stopping_criteria
=
result
.
stopping_criteria
if
response_format
is
not
None
and
response_format
[
"type"
]
==
"json_object"
:
grammar
=
_grammar_for_response_format
(
response_format
,
verbose
=
llama
.
verbose
)
# Convert legacy functions to tools
if
functions
is
not
None
:
tools
=
[
{
"type"
:
"function"
,
"function"
:
function
,
}
for
function
in
functions
]
# Convert legacy function_call to tool_choice
if
function_call
is
not
None
:
if
isinstance
(
function_call
,
str
)
and
(
function_call
==
"none"
or
function_call
==
"auto"
):
tool_choice
=
function_call
if
isinstance
(
function_call
,
dict
)
and
"name"
in
function_call
:
tool_choice
=
{
"type"
:
"function"
,
"function"
: {
"name"
:
function_call
[
"name"
],
},
}
tool
=
None
if
(
tool_choice
is
not
None
and
isinstance
(
tool_choice
,
dict
)
and
tools
is
not
None
):
name
=
tool_choice
[
"function"
][
"name"
]
tool
=
next
((
t
for
t
in
tools
if
t
[
"function"
][
"name"
]
==
name
),
None
)
if
tool
is
None
:
raise
ValueError
(
f"Tool choice '
{
name
}
' not found in tools."
)
schema
=
tool
[
"function"
][
"parameters"
]
try
:
# create grammar from json schema
grammar
=
llama_grammar
.
LlamaGrammar
.
from_json_schema
(
json
.
dumps
(
schema
),
verbose
=
llama
.
verbose
)
except
Exception
as
e
:
if
llama
.
verbose
:
print
(
str
(
e
),
file
=
sys
.
stderr
)
grammar
=
llama_grammar
.
LlamaGrammar
.
from_string
(
llama_grammar
.
JSON_GBNF
,
verbose
=
llama
.
verbose
)
completion_or_chunks
=
llama
.
create_completion
(
prompt
=
prompt
,
temperature
=
temperature
,
top_p
=
top_p
,
top_k
=
top_k
,
min_p
=
min_p
,
typical_p
=
typical_p
,
logprobs
=
top_logprobs
if
logprobs
else
None
,
stream
=
stream
,
stop
=
stop
,
seed
=
seed
,
max_tokens
=
max_tokens
,
presence_penalty
=
presence_penalty
,
frequency_penalty
=
frequency_penalty
,
repeat_penalty
=
repeat_penalty
,
tfs_z
=
tfs_z
,
mirostat_mode
=
mirostat_mode
,
mirostat_tau
=
mirostat_tau
,
mirostat_eta
=
mirostat_eta
,
model
=
model
,
logits_processor
=
logits_processor
,
stopping_criteria
=
stopping_criteria
,
grammar
=
grammar
,
logit_bias
=
logit_bias
,
)
if
tool
is
not
None
:
tool_name
=
tool
[
"function"
][
"name"
]
return
_convert_completion_to_chat_function
(
tool_name
,
completion_or_chunks
,
stream
)
return
_convert_completion_to_chat
(
completion_or_chunks
,
stream
=
stream
)
return
chat_completion_handler
def
hf_autotokenizer_to_chat_formatter
(
pretrained_model_name_or_path
:
Union
[
str
,
os
.
PathLike
[
str
]]
)
->
ChatFormatter
:
# https://huggingface.co/docs/transformers/main/chat_templating
# https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
# https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
from
transformers
import
AutoTokenizer
# type: ignore
tokenizer
=
AutoTokenizer
.
from_pretrained
(
pretrained_model_name_or_path
)
# type: ignore
def
format_autotokenizer
(
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
**
kwargs
:
Any
,
)
->
ChatFormatterResponse
:
tokenizer
.
use_default_system_prompt
=
False
# type: ignore
prompt
:
str
=
tokenizer
.
apply_chat_template
(
messages
,
tokenize
=
False
)
# type: ignore
assert
isinstance
(
prompt
,
str
)
# Return formatted prompt and eos token by default
return
ChatFormatterResponse
(
prompt
=
prompt
,
stop
=
tokenizer
.
eos_token
,
added_special
=
True
)
return
format_autotokenizer
def
hf_autotokenizer_to_chat_completion_handler
(
pretrained_model_name_or_path
:
Union
[
str
,
os
.
PathLike
[
str
]]
)
->
LlamaChatCompletionHandler
:
chat_formatter
=
hf_autotokenizer_to_chat_formatter
(
pretrained_model_name_or_path
)
return
chat_formatter_to_chat_completion_handler
(
chat_formatter
)
def
hf_tokenizer_config_to_chat_formatter
(
tokenizer_config
:
Dict
[
str
,
Any
],
add_generation_prompt
:
bool
=
True
,
)
->
ChatFormatter
:
assert
isinstance
(
tokenizer_config
,
dict
)
assert
"chat_template"
in
tokenizer_config
assert
isinstance
(
tokenizer_config
[
"chat_template"
],
str
)
chat_template
=
tokenizer_config
[
"chat_template"
]
assert
"bos_token"
in
tokenizer_config
assert
isinstance
(
tokenizer_config
[
"bos_token"
],
str
)
bos_token
=
tokenizer_config
[
"bos_token"
]
assert
"eos_token"
in
tokenizer_config
assert
isinstance
(
tokenizer_config
[
"eos_token"
],
str
)
eos_token
=
tokenizer_config
[
"eos_token"
]
env
=
ImmutableSandboxedEnvironment
(
trim_blocks
=
True
,
lstrip_blocks
=
True
,
).
from_string
(
chat_template
)
def
format_tokenizer_config
(
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
**
kwargs
:
Any
,
)
->
ChatFormatterResponse
:
# TODO: veryify this is correct
# Add a blank assistant message to the end of the messages to prompt the model to generate a response
if
add_generation_prompt
:
messages
=
[
*
messages
,
llama_types
.
ChatCompletionRequestAssistantMessage
(
role
=
"assistant"
,
content
=
""
),
]
prompt
=
env
.
render
(
messages
=
messages
,
bos_token
=
bos_token
,
eos_token
=
eos_token
,
)
return
ChatFormatterResponse
(
prompt
=
prompt
,
stop
=
[
eos_token
,
bos_token
],
added_special
=
True
)
return
format_tokenizer_config
def
hf_tokenizer_config_to_chat_completion_handler
(
tokenizer_config
:
Dict
[
str
,
Any
],
add_generation_prompt
:
bool
=
True
,
)
->
LlamaChatCompletionHandler
:
chat_formatter
=
hf_tokenizer_config_to_chat_formatter
(
tokenizer_config
,
add_generation_prompt
=
add_generation_prompt
)
return
chat_formatter_to_chat_completion_handler
(
chat_formatter
)
def
guess_chat_format_from_gguf_metadata
(
metadata
:
Dict
[
str
,
str
])
->
Optional
[
str
]:
if
"tokenizer.chat_template"
not
in
metadata
:
return
None
if
metadata
[
"tokenizer.chat_template"
]
==
CHATML_CHAT_TEMPLATE
:
return
"chatml"
if
(
metadata
[
"tokenizer.chat_template"
]
==
MISTRAL_INSTRUCT_CHAT_TEMPLATE
or
metadata
[
"tokenizer.chat_template"
]
==
MIXTRAL_INSTRUCT_CHAT_TEMPLATE
):
return
"mistral-instruct"
if
metadata
[
"tokenizer.chat_template"
]
==
LLAMA3_INSTRUCT_CHAT_TEMPLATE
:
return
"llama-3"
return
None
### Utility functions for formatting chat prompts ###
# TODO: Replace these with jinja2 templates
def
_get_system_message
(
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
)
->
str
:
"""Get the first system message."""
for
message
in
messages
:
if
message
[
"role"
]
==
"system"
:
return
message
[
"content"
]
or
""
return
""
def
_map_roles
(
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
role_map
:
Dict
[
str
,
str
],
)
->
List
[
Tuple
[
str
,
Optional
[
str
]]]:
"""Map the message roles."""
output
:
List
[
Tuple
[
str
,
Optional
[
str
]]]
=
[]
for
message
in
messages
:
role
=
message
[
"role"
]
if
role
in
role_map
:
content
:
str
|
None
=
(
message
[
"content"
]
if
isinstance
(
message
[
"content"
],
str
)
else
None
)
output
.
append
((
role_map
[
role
],
content
))
return
output
def
_format_llama2
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
,
sep2
:
str
)
->
str
:
"""Format the prompt with the llama2 style."""
seps
=
[
sep
,
sep2
]
ret
=
system_message
+
sep
for
i
, (
role
,
message
)
in
enumerate
(
messages
):
if
system_message
and
i
==
0
:
m
=
message
or
""
ret
+=
m
+
seps
[
i
%
2
]
elif
message
:
ret
+=
role
+
message
+
" "
+
seps
[
i
%
2
]
else
:
ret
+=
role
+
" "
return
ret
def
_format_add_colon_single
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
)
->
str
:
"""Format the prompt with the add-colon-single style."""
ret
=
system_message
+
sep
for
role
,
message
in
messages
:
if
message
:
ret
+=
role
+
": "
+
message
+
sep
else
:
ret
+=
role
+
":"
return
ret
def
_format_add_colon_two
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
,
sep2
:
str
)
->
str
:
"""Format the prompt with the add-colon-two style."""
seps
=
[
sep
,
sep2
]
ret
=
system_message
+
seps
[
0
]
for
i
, (
role
,
message
)
in
enumerate
(
messages
):
if
message
:
ret
+=
role
+
": "
+
message
+
seps
[
i
%
2
]
else
:
ret
+=
role
+
":"
return
ret
def
_format_no_colon_single
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
)
->
str
:
"""Format the prompt with the no-colon-single style."""
ret
=
system_message
for
role
,
message
in
messages
:
if
message
:
ret
+=
role
+
message
+
sep
else
:
ret
+=
role
return
ret
def
_format_add_colon_space_single
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
)
->
str
:
"""Format the prompt with the add-colon-space-single style."""
ret
=
system_message
+
sep
for
role
,
message
in
messages
:
if
message
:
ret
+=
role
+
": "
+
message
+
sep
else
:
ret
+=
role
+
": "
# must be end with a space
return
ret
def
_format_chatml
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
)
->
str
:
"""Format the prompt with the chatml style."""
ret
=
""
if
system_message
==
""
else
system_message
+
sep
+
"
\n
"
for
role
,
message
in
messages
:
if
message
:
ret
+=
role
+
"
\n
"
+
message
+
sep
+
"
\n
"
else
:
ret
+=
role
+
"
\n
"
return
ret
def
_format_chatglm3
(
system_message
:
str
,
messages
:
List
[
Tuple
[
str
,
Optional
[
str
]]],
sep
:
str
)
->
str
:
"""Format the prompt with the chatglm3 style."""
ret
=
""
if
system_message
:
ret
+=
system_message
for
role
,
message
in
messages
:
if
message
:
ret
+=
role
+
"
\n
"
+
" "
+
message
else
:
ret
+=
role
return
ret
def
_grammar_for_json
(
verbose
:
bool
=
False
):
return
llama_grammar
.
LlamaGrammar
.
from_string
(
llama_grammar
.
JSON_GBNF
,
verbose
=
verbose
)
def
_grammar_for_json_schema
(
schema
:
str
,
verbose
:
bool
=
False
,
fallback_to_json
:
bool
=
True
):
try
:
return
llama_grammar
.
LlamaGrammar
.
from_json_schema
(
schema
,
verbose
=
verbose
)
except
Exception
as
e
:
if
fallback_to_json
:
return
_grammar_for_json
(
verbose
=
verbose
)
else
:
raise
e
def
_grammar_for_response_format
(
response_format
:
llama_types
.
ChatCompletionRequestResponseFormat
,
verbose
:
bool
=
False
,
):
if
response_format
[
"type"
]
!=
"json_object"
:
return
None
if
"schema"
in
response_format
:
return
_grammar_for_json_schema
(
json
.
dumps
(
response_format
[
"schema"
]),
verbose
=
verbose
)
else
:
return
_grammar_for_json
(
verbose
=
verbose
)
### Chat Formats ###
def
register_chat_format
(
name
:
str
):
def
decorator
(
f
:
ChatFormatter
):
chat_completion_handler
=
chat_formatter_to_chat_completion_handler
(
f
)
LlamaChatCompletionHandlerRegistry
().
register_chat_completion_handler
(
name
,
chat_completion_handler
)
return
f
return
decorator
# see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
# system prompt is "embedded" in the first message
@
register_chat_format
(
"llama-2"
)
def
format_llama2
(
messages
:
List
[
llama_types
.
ChatCompletionRequestMessage
],
**
kwargs
:
Any
,
)
->
ChatFormatterResponse
:
_system_template
=
"[INST] <<SYS>>
\n
{system_message}
\n
<</SYS>>"
_roles
=
dict
(
user
=
"<s>[INST]"
,
assistant
=
"[/INST]"
)
_messages
=
_map_roles
(
messages
,
_roles
)
system_message
=
_get_system_message
(
messages
)
if
system_message
:
system_message
=
_system_template
.
format
(
system_message
=
system_message
)
_prompt
=
_format_llama2
(
system_message
,
_messages
,
" "
,
"</s>"
)
+
"[/INST]"
return
ChatFormatterResponse
(
prompt
=
_prompt
)
# Chat format for Llama-3 models, see more details at:
# https://github.com/meta-llama/llama3/blob/main/llama/tokenizer.py#L202-L229
@
register_chat_format
(
"llama-3"
)
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