forked from phoenix/litellm-mirror
Merge pull request #3571 from BerriAI/litellm_hf_classifier_support
Huggingface classifier support
This commit is contained in:
Krish Dholakia
GitHub
commit
1aa567f3b5
6 changed files with 415 additions and 64 deletions
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@ -21,6 +21,11 @@ This is done by adding the "huggingface/" prefix to `model`, example `completion
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<Tabs>
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<TabItem value="tgi" label="Text-generation-interface (TGI)">
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By default, LiteLLM will assume a huggingface call follows the TGI format.
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<Tabs>
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<TabItem value="sdk" label="SDK">
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```python
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import os
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from litellm import completion
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@ -40,9 +45,58 @@ response = completion(
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print(response)
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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1. Add models to your config.yaml
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```yaml
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model_list:
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- model_name: wizard-coder
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litellm_params:
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model: huggingface/WizardLM/WizardCoder-Python-34B-V1.0
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api_key: os.environ/HUGGINGFACE_API_KEY
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api_base: "https://my-endpoint.endpoints.huggingface.cloud"
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```
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2. Start the proxy
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```bash
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$ litellm --config /path/to/config.yaml --debug
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```
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3. Test it!
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
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--header 'Authorization: Bearer sk-1234' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "wizard-coder",
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"messages": [
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{
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"role": "user",
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"content": "I like you!"
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}
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],
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}'
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```
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</TabItem>
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</Tabs>
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</TabItem>
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<TabItem value="conv" label="Conversational-task (BlenderBot, etc.)">
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Append `conversational` to the model name
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e.g. `huggingface/conversational/<model-name>`
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<Tabs>
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<TabItem value="sdk" label="SDK">
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```python
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import os
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from litellm import completion
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@ -54,7 +108,7 @@ messages = [{ "content": "There's a llama in my garden 😱 What should I do?","
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# e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/facebook/blenderbot-400M-distill",
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model="huggingface/conversational/facebook/blenderbot-400M-distill",
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messages=messages,
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api_base="https://my-endpoint.huggingface.cloud"
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)
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@ -62,7 +116,123 @@ response = completion(
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print(response)
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```
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</TabItem>
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<TabItem value="none" label="Non TGI/Conversational-task LLMs">
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<TabItem value="proxy" label="PROXY">
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1. Add models to your config.yaml
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```yaml
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model_list:
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- model_name: blenderbot
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litellm_params:
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model: huggingface/conversational/facebook/blenderbot-400M-distill
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api_key: os.environ/HUGGINGFACE_API_KEY
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api_base: "https://my-endpoint.endpoints.huggingface.cloud"
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```
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2. Start the proxy
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```bash
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$ litellm --config /path/to/config.yaml --debug
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```
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3. Test it!
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
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--header 'Authorization: Bearer sk-1234' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "blenderbot",
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"messages": [
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{
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"role": "user",
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"content": "I like you!"
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}
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],
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}'
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```
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</TabItem>
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</Tabs>
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</TabItem>
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<TabItem value="classification" label="Text Classification">
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Append `text-classification` to the model name
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e.g. `huggingface/text-classification/<model-name>`
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<Tabs>
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<TabItem value="sdk" label="SDK">
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```python
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import os
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from litellm import completion
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# [OPTIONAL] set env var
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os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
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messages = [{ "content": "I like you, I love you!","role": "user"}]
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# e.g. Call 'shahrukhx01/question-vs-statement-classifier' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/text-classification/shahrukhx01/question-vs-statement-classifier",
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messages=messages,
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api_base="https://my-endpoint.endpoints.huggingface.cloud",
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)
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print(response)
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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1. Add models to your config.yaml
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```yaml
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model_list:
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- model_name: bert-classifier
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litellm_params:
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model: huggingface/text-classification/shahrukhx01/question-vs-statement-classifier
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api_key: os.environ/HUGGINGFACE_API_KEY
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api_base: "https://my-endpoint.endpoints.huggingface.cloud"
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```
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2. Start the proxy
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```bash
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$ litellm --config /path/to/config.yaml --debug
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```
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3. Test it!
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
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--header 'Authorization: Bearer sk-1234' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "bert-classifier",
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"messages": [
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{
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"role": "user",
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"content": "I like you!"
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}
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],
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}'
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```
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</TabItem>
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</Tabs>
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</TabItem>
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<TabItem value="none" label="Text Generation (NOT TGI)">
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Append `text-generation` to the model name
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e.g. `huggingface/text-generation/<model-name>`
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```python
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import os
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@ -75,7 +245,7 @@ messages = [{ "content": "There's a llama in my garden 😱 What should I do?","
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# e.g. Call 'roneneldan/TinyStories-3M' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/roneneldan/TinyStories-3M",
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model="huggingface/text-generation/roneneldan/TinyStories-3M",
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messages=messages,
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api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud",
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)
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@ -6,10 +6,12 @@ import httpx, requests
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from .base import BaseLLM
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import time
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import litellm
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from typing import Callable, Dict, List, Any
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from typing import Callable, Dict, List, Any, Literal
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from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage
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from typing import Optional
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from .prompt_templates.factory import prompt_factory, custom_prompt
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from litellm.types.completion import ChatCompletionMessageToolCallParam
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import enum
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class HuggingfaceError(Exception):
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@ -39,11 +41,29 @@ class HuggingfaceError(Exception):
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) # Call the base class constructor with the parameters it needs
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hf_task_list = [
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"text-generation-inference",
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"conversational",
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"text-classification",
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"text-generation",
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]
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hf_tasks = Literal[
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"text-generation-inference",
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"conversational",
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"text-classification",
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"text-generation",
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]
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class HuggingfaceConfig:
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"""
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Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate
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"""
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hf_task: Optional[hf_tasks] = (
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None # litellm-specific param, used to know the api spec to use when calling huggingface api
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)
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best_of: Optional[int] = None
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decoder_input_details: Optional[bool] = None
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details: Optional[bool] = True # enables returning logprobs + best of
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@ -101,6 +121,51 @@ class HuggingfaceConfig:
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and v is not None
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}
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def get_supported_openai_params(self):
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return [
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"stream",
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"temperature",
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"max_tokens",
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"top_p",
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"stop",
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"n",
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"echo",
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]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict
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) -> dict:
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for param, value in non_default_params.items():
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# temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
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if param == "temperature":
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if value == 0.0 or value == 0:
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# hugging face exception raised when temp==0
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# Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
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value = 0.01
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optional_params["temperature"] = value
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if param == "top_p":
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optional_params["top_p"] = value
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if param == "n":
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optional_params["best_of"] = value
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optional_params["do_sample"] = (
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True # Need to sample if you want best of for hf inference endpoints
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)
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if param == "stream":
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optional_params["stream"] = value
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if param == "stop":
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optional_params["stop"] = value
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if param == "max_tokens":
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# HF TGI raises the following exception when max_new_tokens==0
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# Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
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if value == 0:
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value = 1
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optional_params["max_new_tokens"] = value
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if param == "echo":
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# https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details
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# Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False
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optional_params["decoder_input_details"] = True
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return optional_params
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def output_parser(generated_text: str):
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"""
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@ -162,16 +227,18 @@ def read_tgi_conv_models():
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return set(), set()
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def get_hf_task_for_model(model):
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def get_hf_task_for_model(model: str) -> hf_tasks:
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# read text file, cast it to set
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# read the file called "huggingface_llms_metadata/hf_text_generation_models.txt"
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if model.split("/")[0] in hf_task_list:
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return model.split("/")[0] # type: ignore
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tgi_models, conversational_models = read_tgi_conv_models()
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if model in tgi_models:
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return "text-generation-inference"
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elif model in conversational_models:
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return "conversational"
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elif "roneneldan/TinyStories" in model:
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return None
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return "text-generation"
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else:
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return "text-generation-inference" # default to tgi
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@ -202,7 +269,7 @@ class Huggingface(BaseLLM):
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self,
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completion_response,
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model_response,
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task,
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task: hf_tasks,
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optional_params,
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encoding,
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input_text,
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@ -270,6 +337,10 @@ class Huggingface(BaseLLM):
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)
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choices_list.append(choice_obj)
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model_response["choices"].extend(choices_list)
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elif task == "text-classification":
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model_response["choices"][0]["message"]["content"] = json.dumps(
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completion_response
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)
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else:
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if len(completion_response[0]["generated_text"]) > 0:
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model_response["choices"][0]["message"]["content"] = output_parser(
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@ -333,6 +404,12 @@ class Huggingface(BaseLLM):
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try:
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headers = self.validate_environment(api_key, headers)
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task = get_hf_task_for_model(model)
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## VALIDATE API FORMAT
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if task is None or not isinstance(task, str) or task not in hf_task_list:
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raise Exception(
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"Invalid hf task - {}. Valid formats - {}.".format(task, hf_tasks)
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)
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print_verbose(f"{model}, {task}")
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completion_url = ""
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input_text = ""
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@ -433,14 +510,15 @@ class Huggingface(BaseLLM):
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inference_params.pop("return_full_text")
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data = {
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"inputs": prompt,
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"parameters": inference_params,
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"stream": ( # type: ignore
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}
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if task == "text-generation-inference":
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data["parameters"] = inference_params
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data["stream"] = ( # type: ignore
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True
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if "stream" in optional_params
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and optional_params["stream"] == True
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else False
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),
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}
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)
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input_text = prompt
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## LOGGING
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logging_obj.pre_call(
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@ -531,10 +609,10 @@ class Huggingface(BaseLLM):
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isinstance(completion_response, dict)
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and "error" in completion_response
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):
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print_verbose(f"completion error: {completion_response['error']}")
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print_verbose(f"completion error: {completion_response['error']}") # type: ignore
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print_verbose(f"response.status_code: {response.status_code}")
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raise HuggingfaceError(
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message=completion_response["error"],
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message=completion_response["error"], # type: ignore
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status_code=response.status_code,
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)
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return self.convert_to_model_response_object(
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@ -563,7 +641,7 @@ class Huggingface(BaseLLM):
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data: dict,
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headers: dict,
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model_response: ModelResponse,
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task: str,
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task: hf_tasks,
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encoding: Any,
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input_text: str,
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model: str,
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|
|
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@ -13,6 +13,7 @@ import litellm
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from litellm import embedding, completion, completion_cost, Timeout
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from litellm import RateLimitError
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from litellm.llms.prompt_templates.factory import anthropic_messages_pt
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from unittest.mock import patch, MagicMock
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# litellm.num_retries=3
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litellm.cache = None
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@ -1137,7 +1138,7 @@ def test_get_hf_task_for_model():
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model = "roneneldan/TinyStories-3M"
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model_type = litellm.llms.huggingface_restapi.get_hf_task_for_model(model)
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print(f"model:{model}, model type: {model_type}")
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assert model_type == None
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assert model_type == "text-generation"
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# test_get_hf_task_for_model()
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@ -1145,12 +1146,89 @@ def test_get_hf_task_for_model():
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# ################### Hugging Face TGI models ########################
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# # TGI model
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# # this is a TGI model https://huggingface.co/glaiveai/glaive-coder-7b
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def hf_test_completion_tgi():
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# litellm.set_verbose=True
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def tgi_mock_post(url, data=None, json=None, headers=None):
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.headers = {"Content-Type": "application/json"}
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mock_response.json.return_value = [
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{
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"generated_text": "<|assistant|>\nI'm",
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"details": {
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"finish_reason": "length",
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"generated_tokens": 10,
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"seed": None,
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"prefill": [],
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"tokens": [
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{
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"id": 28789,
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"text": "<",
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"logprob": -0.025222778,
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"special": False,
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},
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{
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"id": 28766,
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"text": "|",
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"logprob": -0.000003695488,
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"special": False,
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},
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{
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"id": 489,
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"text": "ass",
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"logprob": -0.0000019073486,
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"special": False,
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},
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{
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"id": 11143,
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"text": "istant",
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"logprob": -0.000002026558,
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"special": False,
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},
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{
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"id": 28766,
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"text": "|",
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"logprob": -0.0000015497208,
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"special": False,
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},
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{
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"id": 28767,
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"text": ">",
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"logprob": -0.0000011920929,
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"special": False,
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},
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{
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"id": 13,
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"text": "\n",
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"logprob": -0.00009703636,
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"special": False,
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},
|
||||
{"id": 28737, "text": "I", "logprob": -0.1953125, "special": False},
|
||||
{
|
||||
"id": 28742,
|
||||
"text": "'",
|
||||
"logprob": -0.88183594,
|
||||
"special": False,
|
||||
},
|
||||
{
|
||||
"id": 28719,
|
||||
"text": "m",
|
||||
"logprob": -0.00032639503,
|
||||
"special": False,
|
||||
},
|
||||
],
|
||||
},
|
||||
}
|
||||
]
|
||||
return mock_response
|
||||
|
||||
|
||||
def test_hf_test_completion_tgi():
|
||||
litellm.set_verbose = True
|
||||
try:
|
||||
with patch("requests.post", side_effect=tgi_mock_post):
|
||||
response = completion(
|
||||
model="huggingface/HuggingFaceH4/zephyr-7b-beta",
|
||||
messages=[{"content": "Hello, how are you?", "role": "user"}],
|
||||
max_tokens=10,
|
||||
)
|
||||
# Add any assertions here to check the response
|
||||
print(response)
|
||||
|
|
@ -1191,6 +1269,40 @@ def hf_test_completion_tgi():
|
|||
# except Exception as e:
|
||||
# pytest.fail(f"Error occurred: {e}")
|
||||
# hf_test_completion_none_task()
|
||||
|
||||
|
||||
def mock_post(url, data=None, json=None, headers=None):
|
||||
mock_response = MagicMock()
|
||||
mock_response.status_code = 200
|
||||
mock_response.headers = {"Content-Type": "application/json"}
|
||||
mock_response.json.return_value = [
|
||||
[
|
||||
{"label": "LABEL_0", "score": 0.9990691542625427},
|
||||
{"label": "LABEL_1", "score": 0.0009308889275416732},
|
||||
]
|
||||
]
|
||||
return mock_response
|
||||
|
||||
|
||||
def test_hf_classifier_task():
|
||||
try:
|
||||
with patch("requests.post", side_effect=mock_post):
|
||||
litellm.set_verbose = True
|
||||
user_message = "I like you. I love you"
|
||||
messages = [{"content": user_message, "role": "user"}]
|
||||
response = completion(
|
||||
model="huggingface/text-classification/shahrukhx01/question-vs-statement-classifier",
|
||||
messages=messages,
|
||||
)
|
||||
print(f"response: {response}")
|
||||
assert isinstance(response, litellm.ModelResponse)
|
||||
assert isinstance(response.choices[0], litellm.Choices)
|
||||
assert response.choices[0].message.content is not None
|
||||
assert isinstance(response.choices[0].message.content, str)
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {str(e)}")
|
||||
|
||||
|
||||
########################### End of Hugging Face Tests ##############################################
|
||||
# def test_completion_hf_api():
|
||||
# # failing on circle ci commenting out
|
||||
|
|
|
|||
|
|
@ -3,7 +3,27 @@ from litellm import get_optional_params
|
|||
|
||||
litellm.add_function_to_prompt = True
|
||||
optional_params = get_optional_params(
|
||||
tools= [{'type': 'function', 'function': {'description': 'Get the current weather in a given location', 'name': 'get_current_weather', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}}],
|
||||
tool_choice= 'auto',
|
||||
model="",
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"description": "Get the current weather in a given location",
|
||||
"name": "get_current_weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
tool_choice="auto",
|
||||
)
|
||||
assert optional_params is not None
|
||||
|
|
@ -86,6 +86,7 @@ def test_azure_optional_params_embeddings():
|
|||
def test_azure_gpt_optional_params_gpt_vision():
|
||||
# for OpenAI, Azure all extra params need to get passed as extra_body to OpenAI python. We assert we actually set extra_body here
|
||||
optional_params = litellm.utils.get_optional_params(
|
||||
model="",
|
||||
user="John",
|
||||
custom_llm_provider="azure",
|
||||
max_tokens=10,
|
||||
|
|
@ -125,6 +126,7 @@ def test_azure_gpt_optional_params_gpt_vision():
|
|||
def test_azure_gpt_optional_params_gpt_vision_with_extra_body():
|
||||
# if user passes extra_body, we should not over write it, we should pass it along to OpenAI python
|
||||
optional_params = litellm.utils.get_optional_params(
|
||||
model="",
|
||||
user="John",
|
||||
custom_llm_provider="azure",
|
||||
max_tokens=10,
|
||||
|
|
@ -167,6 +169,7 @@ def test_azure_gpt_optional_params_gpt_vision_with_extra_body():
|
|||
|
||||
def test_openai_extra_headers():
|
||||
optional_params = litellm.utils.get_optional_params(
|
||||
model="",
|
||||
user="John",
|
||||
custom_llm_provider="openai",
|
||||
max_tokens=10,
|
||||
|
|
|
|||
|
|
@ -4871,6 +4871,7 @@ def get_optional_params_embeddings(
|
|||
def get_optional_params(
|
||||
# use the openai defaults
|
||||
# https://platform.openai.com/docs/api-reference/chat/create
|
||||
model: str,
|
||||
functions=None,
|
||||
function_call=None,
|
||||
temperature=None,
|
||||
|
|
@ -4884,7 +4885,6 @@ def get_optional_params(
|
|||
frequency_penalty=None,
|
||||
logit_bias=None,
|
||||
user=None,
|
||||
model=None,
|
||||
custom_llm_provider="",
|
||||
response_format=None,
|
||||
seed=None,
|
||||
|
|
@ -4913,7 +4913,7 @@ def get_optional_params(
|
|||
|
||||
passed_params[k] = v
|
||||
|
||||
optional_params = {}
|
||||
optional_params: Dict = {}
|
||||
|
||||
common_auth_dict = litellm.common_cloud_provider_auth_params
|
||||
if custom_llm_provider in common_auth_dict["providers"]:
|
||||
|
|
@ -5187,41 +5187,9 @@ def get_optional_params(
|
|||
model=model, custom_llm_provider=custom_llm_provider
|
||||
)
|
||||
_check_valid_arg(supported_params=supported_params)
|
||||
# temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
|
||||
if temperature is not None:
|
||||
if temperature == 0.0 or temperature == 0:
|
||||
# hugging face exception raised when temp==0
|
||||
# Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
|
||||
temperature = 0.01
|
||||
optional_params["temperature"] = temperature
|
||||
if top_p is not None:
|
||||
optional_params["top_p"] = top_p
|
||||
if n is not None:
|
||||
optional_params["best_of"] = n
|
||||
optional_params["do_sample"] = (
|
||||
True # Need to sample if you want best of for hf inference endpoints
|
||||
optional_params = litellm.HuggingfaceConfig().map_openai_params(
|
||||
non_default_params=non_default_params, optional_params=optional_params
|
||||
)
|
||||
if stream is not None:
|
||||
optional_params["stream"] = stream
|
||||
if stop is not None:
|
||||
optional_params["stop"] = stop
|
||||
if max_tokens is not None:
|
||||
# HF TGI raises the following exception when max_new_tokens==0
|
||||
# Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
|
||||
if max_tokens == 0:
|
||||
max_tokens = 1
|
||||
optional_params["max_new_tokens"] = max_tokens
|
||||
if n is not None:
|
||||
optional_params["best_of"] = n
|
||||
if presence_penalty is not None:
|
||||
optional_params["repetition_penalty"] = presence_penalty
|
||||
if "echo" in passed_params:
|
||||
# https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details
|
||||
# Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False
|
||||
optional_params["decoder_input_details"] = special_params["echo"]
|
||||
passed_params.pop(
|
||||
"echo", None
|
||||
) # since we handle translating echo, we should not send it to TGI request
|
||||
elif custom_llm_provider == "together_ai":
|
||||
## check if unsupported param passed in
|
||||
supported_params = get_supported_openai_params(
|
||||
|
|
@ -6181,7 +6149,7 @@ def get_supported_openai_params(model: str, custom_llm_provider: str):
|
|||
"seed",
|
||||
]
|
||||
elif custom_llm_provider == "huggingface":
|
||||
return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
|
||||
return litellm.HuggingfaceConfig().get_supported_openai_params()
|
||||
elif custom_llm_provider == "together_ai":
|
||||
return [
|
||||
"stream",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue