mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 14:52:20 +00:00
211b54ac41
# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil --> --------- Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
508 lines
19 KiB
Python
508 lines
19 KiB
Python
import json
|
|
import requests
|
|
|
|
from aiohttp import ClientSession, ClientTimeout
|
|
from pydantic import ValidationError
|
|
from typing import Dict, Optional, List, AsyncIterator, Iterator
|
|
|
|
from text_generation.types import (
|
|
StreamResponse,
|
|
Response,
|
|
Request,
|
|
Parameters,
|
|
)
|
|
from text_generation.errors import parse_error
|
|
|
|
|
|
class Client:
|
|
"""Client to make calls to a text-generation-inference instance
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from text_generation import Client
|
|
|
|
>>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
|
|
>>> client.generate("Why is the sky blue?").generated_text
|
|
' Rayleigh scattering'
|
|
|
|
>>> result = ""
|
|
>>> for response in client.generate_stream("Why is the sky blue?"):
|
|
>>> if not response.token.special:
|
|
>>> result += response.token.text
|
|
>>> result
|
|
' Rayleigh scattering'
|
|
```
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
base_url: str,
|
|
headers: Optional[Dict[str, str]] = None,
|
|
cookies: Optional[Dict[str, str]] = None,
|
|
timeout: int = 10,
|
|
):
|
|
"""
|
|
Args:
|
|
base_url (`str`):
|
|
text-generation-inference instance base url
|
|
headers (`Optional[Dict[str, str]]`):
|
|
Additional headers
|
|
cookies (`Optional[Dict[str, str]]`):
|
|
Cookies to include in the requests
|
|
timeout (`int`):
|
|
Timeout in seconds
|
|
"""
|
|
self.base_url = base_url
|
|
self.headers = headers
|
|
self.cookies = cookies
|
|
self.timeout = timeout
|
|
|
|
def generate(
|
|
self,
|
|
prompt: str,
|
|
do_sample: bool = False,
|
|
max_new_tokens: int = 20,
|
|
best_of: Optional[int] = None,
|
|
repetition_penalty: Optional[float] = None,
|
|
return_full_text: bool = False,
|
|
seed: Optional[int] = None,
|
|
stop_sequences: Optional[List[str]] = None,
|
|
temperature: Optional[float] = None,
|
|
top_k: Optional[int] = None,
|
|
top_p: Optional[float] = None,
|
|
truncate: Optional[int] = None,
|
|
typical_p: Optional[float] = None,
|
|
watermark: bool = False,
|
|
decoder_input_details: bool = False,
|
|
top_n_tokens: Optional[int] = None,
|
|
) -> Response:
|
|
"""
|
|
Given a prompt, generate the following text
|
|
|
|
Args:
|
|
prompt (`str`):
|
|
Input text
|
|
do_sample (`bool`):
|
|
Activate logits sampling
|
|
max_new_tokens (`int`):
|
|
Maximum number of generated tokens
|
|
best_of (`int`):
|
|
Generate best_of sequences and return the one if the highest token logprobs
|
|
repetition_penalty (`float`):
|
|
The parameter for repetition penalty. 1.0 means no penalty. See [this
|
|
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
|
|
return_full_text (`bool`):
|
|
Whether to prepend the prompt to the generated text
|
|
seed (`int`):
|
|
Random sampling seed
|
|
stop_sequences (`List[str]`):
|
|
Stop generating tokens if a member of `stop_sequences` is generated
|
|
temperature (`float`):
|
|
The value used to module the logits distribution.
|
|
top_k (`int`):
|
|
The number of highest probability vocabulary tokens to keep for top-k-filtering.
|
|
top_p (`float`):
|
|
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
|
|
higher are kept for generation.
|
|
truncate (`int`):
|
|
Truncate inputs tokens to the given size
|
|
typical_p (`float`):
|
|
Typical Decoding mass
|
|
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
|
watermark (`bool`):
|
|
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
|
decoder_input_details (`bool`):
|
|
Return the decoder input token logprobs and ids
|
|
top_n_tokens (`int`):
|
|
Return the `n` most likely tokens at each step
|
|
|
|
Returns:
|
|
Response: generated response
|
|
"""
|
|
# Validate parameters
|
|
parameters = Parameters(
|
|
best_of=best_of,
|
|
details=True,
|
|
do_sample=do_sample,
|
|
max_new_tokens=max_new_tokens,
|
|
repetition_penalty=repetition_penalty,
|
|
return_full_text=return_full_text,
|
|
seed=seed,
|
|
stop=stop_sequences if stop_sequences is not None else [],
|
|
temperature=temperature,
|
|
top_k=top_k,
|
|
top_p=top_p,
|
|
truncate=truncate,
|
|
typical_p=typical_p,
|
|
watermark=watermark,
|
|
decoder_input_details=decoder_input_details,
|
|
top_n_tokens=top_n_tokens
|
|
)
|
|
request = Request(inputs=prompt, stream=False, parameters=parameters)
|
|
|
|
resp = requests.post(
|
|
self.base_url,
|
|
json=request.dict(),
|
|
headers=self.headers,
|
|
cookies=self.cookies,
|
|
timeout=self.timeout,
|
|
)
|
|
payload = resp.json()
|
|
if resp.status_code != 200:
|
|
raise parse_error(resp.status_code, payload)
|
|
return Response(**payload[0])
|
|
|
|
def generate_stream(
|
|
self,
|
|
prompt: str,
|
|
do_sample: bool = False,
|
|
max_new_tokens: int = 20,
|
|
repetition_penalty: Optional[float] = None,
|
|
return_full_text: bool = False,
|
|
seed: Optional[int] = None,
|
|
stop_sequences: Optional[List[str]] = None,
|
|
temperature: Optional[float] = None,
|
|
top_k: Optional[int] = None,
|
|
top_p: Optional[float] = None,
|
|
truncate: Optional[int] = None,
|
|
typical_p: Optional[float] = None,
|
|
watermark: bool = False,
|
|
top_n_tokens: Optional[int] = None,
|
|
) -> Iterator[StreamResponse]:
|
|
"""
|
|
Given a prompt, generate the following stream of tokens
|
|
|
|
Args:
|
|
prompt (`str`):
|
|
Input text
|
|
do_sample (`bool`):
|
|
Activate logits sampling
|
|
max_new_tokens (`int`):
|
|
Maximum number of generated tokens
|
|
repetition_penalty (`float`):
|
|
The parameter for repetition penalty. 1.0 means no penalty. See [this
|
|
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
|
|
return_full_text (`bool`):
|
|
Whether to prepend the prompt to the generated text
|
|
seed (`int`):
|
|
Random sampling seed
|
|
stop_sequences (`List[str]`):
|
|
Stop generating tokens if a member of `stop_sequences` is generated
|
|
temperature (`float`):
|
|
The value used to module the logits distribution.
|
|
top_k (`int`):
|
|
The number of highest probability vocabulary tokens to keep for top-k-filtering.
|
|
top_p (`float`):
|
|
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
|
|
higher are kept for generation.
|
|
truncate (`int`):
|
|
Truncate inputs tokens to the given size
|
|
typical_p (`float`):
|
|
Typical Decoding mass
|
|
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
|
watermark (`bool`):
|
|
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
|
top_n_tokens (`int`):
|
|
Return the `n` most likely tokens at each step
|
|
|
|
Returns:
|
|
Iterator[StreamResponse]: stream of generated tokens
|
|
"""
|
|
# Validate parameters
|
|
parameters = Parameters(
|
|
best_of=None,
|
|
details=True,
|
|
decoder_input_details=False,
|
|
do_sample=do_sample,
|
|
max_new_tokens=max_new_tokens,
|
|
repetition_penalty=repetition_penalty,
|
|
return_full_text=return_full_text,
|
|
seed=seed,
|
|
stop=stop_sequences if stop_sequences is not None else [],
|
|
temperature=temperature,
|
|
top_k=top_k,
|
|
top_p=top_p,
|
|
truncate=truncate,
|
|
typical_p=typical_p,
|
|
watermark=watermark,
|
|
top_n_tokens=top_n_tokens,
|
|
)
|
|
request = Request(inputs=prompt, stream=True, parameters=parameters)
|
|
|
|
resp = requests.post(
|
|
self.base_url,
|
|
json=request.dict(),
|
|
headers=self.headers,
|
|
cookies=self.cookies,
|
|
timeout=self.timeout,
|
|
stream=True,
|
|
)
|
|
|
|
if resp.status_code != 200:
|
|
raise parse_error(resp.status_code, resp.json())
|
|
|
|
# Parse ServerSentEvents
|
|
for byte_payload in resp.iter_lines():
|
|
# Skip line
|
|
if byte_payload == b"\n":
|
|
continue
|
|
|
|
payload = byte_payload.decode("utf-8")
|
|
|
|
# Event data
|
|
if payload.startswith("data:"):
|
|
# Decode payload
|
|
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
|
|
# Parse payload
|
|
try:
|
|
response = StreamResponse(**json_payload)
|
|
except ValidationError:
|
|
# If we failed to parse the payload, then it is an error payload
|
|
raise parse_error(resp.status_code, json_payload)
|
|
yield response
|
|
|
|
|
|
class AsyncClient:
|
|
"""Asynchronous Client to make calls to a text-generation-inference instance
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from text_generation import AsyncClient
|
|
|
|
>>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
|
|
>>> response = await client.generate("Why is the sky blue?")
|
|
>>> response.generated_text
|
|
' Rayleigh scattering'
|
|
|
|
>>> result = ""
|
|
>>> async for response in client.generate_stream("Why is the sky blue?"):
|
|
>>> if not response.token.special:
|
|
>>> result += response.token.text
|
|
>>> result
|
|
' Rayleigh scattering'
|
|
```
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
base_url: str,
|
|
headers: Optional[Dict[str, str]] = None,
|
|
cookies: Optional[Dict[str, str]] = None,
|
|
timeout: int = 10,
|
|
):
|
|
"""
|
|
Args:
|
|
base_url (`str`):
|
|
text-generation-inference instance base url
|
|
headers (`Optional[Dict[str, str]]`):
|
|
Additional headers
|
|
cookies (`Optional[Dict[str, str]]`):
|
|
Cookies to include in the requests
|
|
timeout (`int`):
|
|
Timeout in seconds
|
|
"""
|
|
self.base_url = base_url
|
|
self.headers = headers
|
|
self.cookies = cookies
|
|
self.timeout = ClientTimeout(timeout * 60)
|
|
|
|
async def generate(
|
|
self,
|
|
prompt: str,
|
|
do_sample: bool = False,
|
|
max_new_tokens: int = 20,
|
|
best_of: Optional[int] = None,
|
|
repetition_penalty: Optional[float] = None,
|
|
return_full_text: bool = False,
|
|
seed: Optional[int] = None,
|
|
stop_sequences: Optional[List[str]] = None,
|
|
temperature: Optional[float] = None,
|
|
top_k: Optional[int] = None,
|
|
top_p: Optional[float] = None,
|
|
truncate: Optional[int] = None,
|
|
typical_p: Optional[float] = None,
|
|
watermark: bool = False,
|
|
decoder_input_details: bool = False,
|
|
top_n_tokens: Optional[int] = None,
|
|
) -> Response:
|
|
"""
|
|
Given a prompt, generate the following text asynchronously
|
|
|
|
Args:
|
|
prompt (`str`):
|
|
Input text
|
|
do_sample (`bool`):
|
|
Activate logits sampling
|
|
max_new_tokens (`int`):
|
|
Maximum number of generated tokens
|
|
best_of (`int`):
|
|
Generate best_of sequences and return the one if the highest token logprobs
|
|
repetition_penalty (`float`):
|
|
The parameter for repetition penalty. 1.0 means no penalty. See [this
|
|
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
|
|
return_full_text (`bool`):
|
|
Whether to prepend the prompt to the generated text
|
|
seed (`int`):
|
|
Random sampling seed
|
|
stop_sequences (`List[str]`):
|
|
Stop generating tokens if a member of `stop_sequences` is generated
|
|
temperature (`float`):
|
|
The value used to module the logits distribution.
|
|
top_k (`int`):
|
|
The number of highest probability vocabulary tokens to keep for top-k-filtering.
|
|
top_p (`float`):
|
|
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
|
|
higher are kept for generation.
|
|
truncate (`int`):
|
|
Truncate inputs tokens to the given size
|
|
typical_p (`float`):
|
|
Typical Decoding mass
|
|
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
|
watermark (`bool`):
|
|
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
|
decoder_input_details (`bool`):
|
|
Return the decoder input token logprobs and ids
|
|
top_n_tokens (`int`):
|
|
Return the `n` most likely tokens at each step
|
|
|
|
Returns:
|
|
Response: generated response
|
|
"""
|
|
# Validate parameters
|
|
parameters = Parameters(
|
|
best_of=best_of,
|
|
details=True,
|
|
decoder_input_details=decoder_input_details,
|
|
do_sample=do_sample,
|
|
max_new_tokens=max_new_tokens,
|
|
repetition_penalty=repetition_penalty,
|
|
return_full_text=return_full_text,
|
|
seed=seed,
|
|
stop=stop_sequences if stop_sequences is not None else [],
|
|
temperature=temperature,
|
|
top_k=top_k,
|
|
top_p=top_p,
|
|
truncate=truncate,
|
|
typical_p=typical_p,
|
|
watermark=watermark,
|
|
top_n_tokens=top_n_tokens,
|
|
)
|
|
request = Request(inputs=prompt, stream=False, parameters=parameters)
|
|
|
|
async with ClientSession(
|
|
headers=self.headers, cookies=self.cookies, timeout=self.timeout
|
|
) as session:
|
|
async with session.post(self.base_url, json=request.dict()) as resp:
|
|
payload = await resp.json()
|
|
|
|
if resp.status != 200:
|
|
raise parse_error(resp.status, payload)
|
|
return Response(**payload[0])
|
|
|
|
async def generate_stream(
|
|
self,
|
|
prompt: str,
|
|
do_sample: bool = False,
|
|
max_new_tokens: int = 20,
|
|
repetition_penalty: Optional[float] = None,
|
|
return_full_text: bool = False,
|
|
seed: Optional[int] = None,
|
|
stop_sequences: Optional[List[str]] = None,
|
|
temperature: Optional[float] = None,
|
|
top_k: Optional[int] = None,
|
|
top_p: Optional[float] = None,
|
|
truncate: Optional[int] = None,
|
|
typical_p: Optional[float] = None,
|
|
watermark: bool = False,
|
|
top_n_tokens: Optional[int] = None,
|
|
) -> AsyncIterator[StreamResponse]:
|
|
"""
|
|
Given a prompt, generate the following stream of tokens asynchronously
|
|
|
|
Args:
|
|
prompt (`str`):
|
|
Input text
|
|
do_sample (`bool`):
|
|
Activate logits sampling
|
|
max_new_tokens (`int`):
|
|
Maximum number of generated tokens
|
|
repetition_penalty (`float`):
|
|
The parameter for repetition penalty. 1.0 means no penalty. See [this
|
|
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
|
|
return_full_text (`bool`):
|
|
Whether to prepend the prompt to the generated text
|
|
seed (`int`):
|
|
Random sampling seed
|
|
stop_sequences (`List[str]`):
|
|
Stop generating tokens if a member of `stop_sequences` is generated
|
|
temperature (`float`):
|
|
The value used to module the logits distribution.
|
|
top_k (`int`):
|
|
The number of highest probability vocabulary tokens to keep for top-k-filtering.
|
|
top_p (`float`):
|
|
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
|
|
higher are kept for generation.
|
|
truncate (`int`):
|
|
Truncate inputs tokens to the given size
|
|
typical_p (`float`):
|
|
Typical Decoding mass
|
|
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
|
|
watermark (`bool`):
|
|
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
|
|
top_n_tokens (`int`):
|
|
Return the `n` most likely tokens at each step
|
|
|
|
Returns:
|
|
AsyncIterator[StreamResponse]: stream of generated tokens
|
|
"""
|
|
# Validate parameters
|
|
parameters = Parameters(
|
|
best_of=None,
|
|
details=True,
|
|
decoder_input_details=False,
|
|
do_sample=do_sample,
|
|
max_new_tokens=max_new_tokens,
|
|
repetition_penalty=repetition_penalty,
|
|
return_full_text=return_full_text,
|
|
seed=seed,
|
|
stop=stop_sequences if stop_sequences is not None else [],
|
|
temperature=temperature,
|
|
top_k=top_k,
|
|
top_p=top_p,
|
|
truncate=truncate,
|
|
typical_p=typical_p,
|
|
watermark=watermark,
|
|
top_n_tokens=top_n_tokens,
|
|
)
|
|
request = Request(inputs=prompt, stream=True, parameters=parameters)
|
|
|
|
async with ClientSession(
|
|
headers=self.headers, cookies=self.cookies, timeout=self.timeout
|
|
) as session:
|
|
async with session.post(self.base_url, json=request.dict()) as resp:
|
|
|
|
if resp.status != 200:
|
|
raise parse_error(resp.status, await resp.json())
|
|
|
|
# Parse ServerSentEvents
|
|
async for byte_payload in resp.content:
|
|
# Skip line
|
|
if byte_payload == b"\n":
|
|
continue
|
|
|
|
payload = byte_payload.decode("utf-8")
|
|
|
|
# Event data
|
|
if payload.startswith("data:"):
|
|
# Decode payload
|
|
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
|
|
# Parse payload
|
|
try:
|
|
response = StreamResponse(**json_payload)
|
|
except ValidationError:
|
|
# If we failed to parse the payload, then it is an error payload
|
|
raise parse_error(resp.status, json_payload)
|
|
yield response
|