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Creating doc automatically for supported models. (#1929) 

# What does this PR do?

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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?
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      to it if that's the case.
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Here are the
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guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
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docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


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Nicolas Patry 2024-05-22 16:22:57 +02:00 committed by GitHub
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6
.github/workflows/autodocs.yml vendored
View File

@ -13,11 +13,7 @@ jobs:
- name: Install Launcher
id: install-launcher
env:
REF: ${{ github.head_ref }}
REPO: ${{ github.repository }}
run: cargo install --git "https://github.com/$REPO" --branch "$REF" text-generation-launcher
run: cargo install --path launcher/
- name: Check launcher Docs are up-to-date
run: |
echo text-generation-launcher --help

48
docs/source/supported_models.md
View File

@ -1,30 +1,36 @@
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
The following models are optimized and can be served with TGI, which uses custom CUDA kernels for better inference. You can add the flag `--disable-custom-kernels` at the end of the `docker run` command if you wish to disable them.
- [BLOOM](https://huggingface.co/bigscience/bloom)
- [FLAN-T5](https://huggingface.co/google/flan-t5-xxl)
- [Galactica](https://huggingface.co/facebook/galactica-120b)
- [GPT-2](https://huggingface.co/openai-community/gpt2)
- [GPT-Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
- [Llama](https://github.com/facebookresearch/llama)
- [OPT](https://huggingface.co/facebook/opt-66b)
- [SantaCoder](https://huggingface.co/bigcode/santacoder)
- [Starcoder](https://huggingface.co/bigcode/starcoder)
- [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
- [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b)
- [MPT](https://huggingface.co/mosaicml/mpt-30b)
- [Llama V2](https://huggingface.co/meta-llama)
- [Code Llama](https://huggingface.co/codellama)
- [Idefics 2](https://huggingface.co/HuggingFaceM4/idefics2-8b) (Multimodal)
- [Llava Next (1.6)](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) (Multimodal)
- [Llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- [Phi 3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- [Gemma](https://huggingface.co/google/gemma-7b)
- [Cohere](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
- [Dbrx](https://huggingface.co/databricks/dbrx-instruct)
- [Mamba](https://huggingface.co/state-spaces/mamba-2.8b-slimpj)
- [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- [Phi](https://huggingface.co/microsoft/phi-2)
- [Idefics](HuggingFaceM4/idefics-9b-instruct) (Multimodal)
- [Llava-next](llava-hf/llava-v1.6-mistral-7b-hf) (Multimodal)
- [Mixtral](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1)
- [Gpt Bigcode](https://huggingface.co/bigcode/gpt_bigcode-santacoder)
- [Phi](https://huggingface.co/microsoft/phi-1_5)
- [Baichuan](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)
- [Falcon](https://huggingface.co/tiiuae/falcon-7b-instruct)
- [StarCoder 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
- [Qwen 2](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
- [Opt](https://huggingface.co/facebook/opt-6.7b)
- [T5](https://huggingface.co/google/flan-t5-xxl)
- [Galactica](https://huggingface.co/facebook/galactica-120b)
- [SantaCoder](https://huggingface.co/bigcode/santacoder)
- [Bloom](https://huggingface.co/bigscience/bloom-560m)
- [Mpt](https://huggingface.co/mosaicml/mpt-7b-instruct)
- [Gpt2](https://huggingface.co/openai-community/gpt2)
- [Gpt Neox](https://huggingface.co/EleutherAI/gpt-neox-20b)
- [Idefics](https://huggingface.co/HuggingFaceM4/idefics-9b) (Multimodal)
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
@ -39,4 +45,4 @@ If you wish to serve a supported model that already exists on a local folder, ju
```bash
text-generation-launcher --model-id <PATH-TO-LOCAL-BLOOM>
``````
```

181
server/text_generation_server/models/__init__.py
View File

@ -1,4 +1,5 @@
import torch
import enum
import os
from loguru import logger
@ -116,6 +117,142 @@ if MAMBA_AVAILABLE:
__all__.append(Mamba)
class ModelType(enum.Enum):
IDEFICS2 = {
"type": "idefics2",
"name": "Idefics 2",
"url": "https://huggingface.co/HuggingFaceM4/idefics2-8b",
"multimodal": True,
}
LLAVA_NEXT = {
"type": "llava_next",
"name": "Llava Next (1.6)",
"url": "https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf",
"multimodal": True,
}
LLAMA = {
"type": "llama",
"name": "Llama",
"url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
}
PHI3 = {
"type": "phi3",
"name": "Phi 3",
"url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
}
GEMMA = {
"type": "gemma",
"name": "Gemma",
"url": "https://huggingface.co/google/gemma-7b",
}
COHERE = {
"type": "cohere",
"name": "Cohere",
"url": "https://huggingface.co/CohereForAI/c4ai-command-r-plus",
}
DBRX = {
"type": "dbrx",
"name": "Dbrx",
"url": "https://huggingface.co/databricks/dbrx-instruct",
}
MAMBA = {
"type": "ssm",
"name": "Mamba",
"url": "https://huggingface.co/state-spaces/mamba-2.8b-slimpj",
}
MISTRAL = {
"type": "mistral",
"name": "Mistral",
"url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
}
MIXTRAL = {
"type": "mixtral",
"name": "Mixtral",
"url": "https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1",
}
GPT_BIGCODE = {
"type": "gpt_bigcode",
"name": "Gpt Bigcode",
"url": "https://huggingface.co/bigcode/gpt_bigcode-santacoder",
}
PHI = {
"type": "phi",
"name": "Phi",
"url": "https://huggingface.co/microsoft/phi-1_5",
}
BAICHUAN = {
"type": "baichuan",
"name": "Baichuan",
"url": "https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat",
}
FALCON = {
"type": "falcon",
"name": "Falcon",
"url": "https://huggingface.co/tiiuae/falcon-7b-instruct",
}
STARCODER2 = {
"type": "starcoder2",
"name": "StarCoder 2",
"url": "https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1",
}
QWEN2 = {
"type": "qwen2",
"name": "Qwen 2",
"url": "https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1",
}
OPT = {
"type": "opt",
"name": "Opt",
"url": "https://huggingface.co/facebook/opt-6.7b",
}
T5 = {
"type": "t5",
"name": "T5",
"url": "https://huggingface.co/google/flan-t5-xxl",
}
GALACTICA = {
"type": "galactica",
"name": "Galactica",
"url": "https://huggingface.co/facebook/galactica-120b",
}
SANTACODER = {
"type": "santacoder",
"name": "SantaCoder",
"url": "https://huggingface.co/bigcode/santacoder",
}
BLOOM = {
"type": "bloom",
"name": "Bloom",
"url": "https://huggingface.co/bigscience/bloom-560m",
}
MPT = {
"type": "mpt",
"name": "Mpt",
"url": "https://huggingface.co/mosaicml/mpt-7b-instruct",
}
GPT2 = {
"type": "gpt2",
"name": "Gpt2",
"url": "https://huggingface.co/openai-community/gpt2",
}
GPT_NEOX = {
"type": "gpt_neox",
"name": "Gpt Neox",
"url": "https://huggingface.co/EleutherAI/gpt-neox-20b",
}
IDEFICS = {
"type": "idefics",
"name": "Idefics",
"url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
"multimodal": True,
}
__GLOBALS = locals()
for data in ModelType:
__GLOBALS[data.name] = data.value["type"]
def get_model(
model_id: str,
revision: Optional[str],
@ -267,7 +404,7 @@ def get_model(
else:
logger.info(f"Unknown quantization method {method}")
if model_type == "ssm":
if model_type == MAMBA:
return Mamba(
model_id,
revision,
@ -288,8 +425,8 @@ def get_model(
)
if (
model_type == "gpt_bigcode"
or model_type == "gpt2"
model_type == GPT_BIGCODE
or model_type == GPT2
and model_id.startswith("bigcode/")
):
if FLASH_ATTENTION:
@ -315,7 +452,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "bloom":
if model_type == BLOOM:
return BLOOMSharded(
model_id,
revision,
@ -324,7 +461,7 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "mpt":
elif model_type == MPT:
return MPTSharded(
model_id,
revision,
@ -333,7 +470,7 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "gpt2":
elif model_type == GPT2:
if FLASH_ATTENTION:
return FlashGPT2(
model_id,
@ -354,7 +491,7 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
elif model_type == "gpt_neox":
elif model_type == GPT_NEOX:
if FLASH_ATTENTION:
return FlashNeoXSharded(
model_id,
@ -383,7 +520,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
elif model_type == "phi":
elif model_type == PHI:
if FLASH_ATTENTION:
return FlashPhi(
model_id,
@ -418,7 +555,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3":
elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
if FLASH_ATTENTION:
return FlashLlama(
model_id,
@ -439,7 +576,7 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "gemma":
if model_type == GEMMA:
if FLASH_ATTENTION:
return FlashGemma(
model_id,
@ -461,7 +598,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "cohere":
if model_type == COHERE:
if FLASH_ATTENTION:
return FlashCohere(
model_id,
@ -483,7 +620,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "dbrx":
if model_type == DBRX:
if FLASH_ATTENTION:
return FlashDbrx(
model_id,
@ -505,7 +642,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
if model_type in ["RefinedWeb", "RefinedWebModel", FALCON]:
if sharded:
if FLASH_ATTENTION:
if config_dict.get("alibi", False):
@ -539,7 +676,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "mistral":
if model_type == MISTRAL:
sliding_window = config_dict.get("sliding_window", -1)
if (
((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
@ -566,7 +703,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "mixtral":
if model_type == MIXTRAL:
sliding_window = config_dict.get("sliding_window", -1)
if (
((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
@ -593,7 +730,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "starcoder2":
if model_type == STARCODER2:
sliding_window = config_dict.get("sliding_window", -1)
if (
((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
@ -621,7 +758,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "qwen2":
if model_type == QWEN2:
sliding_window = config_dict.get("sliding_window", -1)
if (
((sliding_window is None or sliding_window == -1) and FLASH_ATTENTION)
@ -647,7 +784,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "opt":
if model_type == OPT:
return OPTSharded(
model_id,
revision,
@ -657,7 +794,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == "t5":
if model_type == T5:
return T5Sharded(
model_id,
revision,
@ -666,7 +803,7 @@ def get_model(
dtype=dtype,
trust_remote_code=trust_remote_code,
)
if model_type == "idefics":
if model_type == IDEFICS:
if FLASH_ATTENTION:
return IDEFICSSharded(
model_id,
@ -678,7 +815,7 @@ def get_model(
)
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "idefics2":
if model_type == IDEFICS2:
if FLASH_ATTENTION:
return Idefics2(
model_id,
@ -703,7 +840,7 @@ def get_model(
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "llava_next":
if model_type == LLAVA_NEXT:
if FLASH_ATTENTION:
return LlavaNext(
model_id,

88
update_doc.py
View File

@ -1,13 +1,34 @@
import subprocess
import argparse
import ast
TEMPLATE = """
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
SUPPORTED_MODELS
If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models:
```python
# for causal LMs/text-generation models
AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
# or, for text-to-text generation models
AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")
```
If you wish to serve a supported model that already exists on a local folder, just point to the local folder.
```bash
text-generation-launcher --model-id <PATH-TO-LOCAL-BLOOM>
```
"""
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
def check_cli(check: bool):
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
"utf-8"
)
@ -41,7 +62,7 @@ def main():
block = []
filename = "docs/source/basic_tutorials/launcher.md"
if args.check:
if check:
with open(filename, "r") as f:
doc = f.read()
if doc != final_doc:
@ -53,12 +74,63 @@ def main():
).stdout.decode("utf-8")
print(diff)
raise Exception(
"Doc is not up-to-date, run `python update_doc.py` in order to update it"
"Cli arguments Doc is not up-to-date, run `python update_doc.py` in order to update it"
)
else:
with open(filename, "w") as f:
f.write(final_doc)
def check_supported_models(check: bool):
filename = "server/text_generation_server/models/__init__.py"
with open(filename, "r") as f:
tree = ast.parse(f.read())
enum_def = [
x for x in tree.body if isinstance(x, ast.ClassDef) and x.name == "ModelType"
][0]
_locals = {}
_globals = {}
exec(f"import enum\n{ast.unparse(enum_def)}", _globals, _locals)
ModelType = _locals["ModelType"]
list_string = ""
for data in ModelType:
list_string += f"- [{data.value['name']}]({data.value['url']})"
if data.value.get("multimodal", None):
list_string += " (Multimodal)"
list_string += "\n"
final_doc = TEMPLATE.replace("SUPPORTED_MODELS", list_string)
filename = "docs/source/supported_models.md"
if check:
with open(filename, "r") as f:
doc = f.read()
if doc != final_doc:
tmp = "supported.md"
with open(tmp, "w") as g:
g.write(final_doc)
diff = subprocess.run(
["diff", tmp, filename], capture_output=True
).stdout.decode("utf-8")
print(diff)
raise Exception(
"Supported models is not up-to-date, run `python update_doc.py` in order to update it"
)
else:
with open(filename, "w") as f:
f.write(final_doc)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
check_cli(args.check)
check_supported_models(args.check)
if __name__ == "__main__":
main()