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bce5e22444
Co-Authored-By: Victor Sanh <victorsanh@gmail.com> # 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: Victor Sanh <victorsanh@gmail.com>
312 lines
9.6 KiB
Python
312 lines
9.6 KiB
Python
import os
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import torch
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from loguru import logger
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from transformers.configuration_utils import PretrainedConfig
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from transformers.models.auto import modeling_auto
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from typing import Optional
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from text_generation_server.models.model import Model
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from text_generation_server.models.causal_lm import CausalLM
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from text_generation_server.models.flash_causal_lm import FlashCausalLM
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from text_generation_server.models.bloom import BLOOMSharded
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from text_generation_server.models.mpt import MPTSharded
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from text_generation_server.models.seq2seq_lm import Seq2SeqLM
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from text_generation_server.models.rw import RW
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from text_generation_server.models.opt import OPTSharded
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from text_generation_server.models.galactica import GalacticaSharded
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from text_generation_server.models.santacoder import SantaCoder
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from text_generation_server.models.t5 import T5Sharded
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from text_generation_server.models.gpt_neox import GPTNeoxSharded
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# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
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# in PyTorch 1.12 and later.
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torch.backends.cuda.matmul.allow_tf32 = True
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# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
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torch.backends.cudnn.allow_tf32 = True
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# Disable gradients
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torch.set_grad_enabled(False)
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__all__ = [
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"Model",
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"BLOOMSharded",
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"CausalLM",
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"FlashCausalLM",
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"GalacticaSharded",
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"Seq2SeqLM",
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"SantaCoder",
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"OPTSharded",
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"T5Sharded",
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"get_model",
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]
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FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
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FLASH_ATTENTION = True
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try:
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from text_generation_server.models.flash_rw import FlashRWSharded
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from text_generation_server.models.flash_neox import FlashNeoXSharded
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from text_generation_server.models.flash_llama import (
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FlashLlama,
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)
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from text_generation_server.models.flash_santacoder import (
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FlashSantacoderSharded,
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)
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from text_generation_server.models.idefics import IDEFICSSharded
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except ImportError as e:
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logger.warning(f"Could not import Flash Attention enabled models: {e}")
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FLASH_ATTENTION = False
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if FLASH_ATTENTION:
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__all__.append(FlashNeoXSharded)
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__all__.append(FlashRWSharded)
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__all__.append(FlashSantacoderSharded)
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__all__.append(FlashLlama)
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__all__.append(IDEFICSSharded)
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def get_model(
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model_id: str,
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revision: Optional[str],
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sharded: bool,
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quantize: Optional[str],
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dtype: Optional[str],
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trust_remote_code: bool,
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) -> Model:
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if dtype is None:
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dtype = torch.float16
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elif dtype == "float16":
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dtype = torch.float16
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elif dtype == "bfloat16":
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dtype = torch.bfloat16
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else:
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raise RuntimeError(f"Unknown dtype {dtype}")
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if "facebook/galactica" in model_id:
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return GalacticaSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_id.startswith("bigcode/"):
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if FLASH_ATTENTION:
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return FlashSantacoderSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(
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FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
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)
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else:
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return SantaCoder(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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config_dict, _ = PretrainedConfig.get_config_dict(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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model_type = config_dict["model_type"]
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if model_type == "gpt_bigcode":
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if FLASH_ATTENTION:
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return FlashSantacoderSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(
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FLASH_ATT_ERROR_MESSAGE.format("Sharded Santacoder")
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)
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else:
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return SantaCoder(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type == "bloom":
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return BLOOMSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == "mpt":
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return MPTSharded(
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model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
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)
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elif model_type == "gpt_neox":
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if FLASH_ATTENTION:
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return FlashNeoXSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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return GPTNeoxSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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else:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == "llama":
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if FLASH_ATTENTION:
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return FlashLlama(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
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else:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
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if sharded:
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if FLASH_ATTENTION:
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if config_dict.get("alibi", False):
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raise NotImplementedError("sharded is not supported for this model")
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return FlashRWSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format(f"Sharded Falcon"))
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else:
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if FLASH_ATTENTION and not config_dict.get("alibi", False):
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return FlashRWSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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else:
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return RW(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == "opt":
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return OPTSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == "t5":
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return T5Sharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif model_type == "idefics":
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if FLASH_ATTENTION:
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return IDEFICSSharded(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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else:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
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if sharded:
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raise ValueError("sharded is not supported for AutoModel")
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if quantize == "gptq":
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raise ValueError(
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"gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
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)
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elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
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raise ValueError(
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"4bit quantization is not supported for AutoModel"
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)
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if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
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return Seq2SeqLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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auto_map = config_dict.get("auto_map", None)
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if trust_remote_code and auto_map is not None:
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if "AutoModelForCausalLM" in auto_map.keys():
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if "AutoModelForSeq2SeqLM" in auto_map.keys():
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return Seq2SeqLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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raise ValueError(f"Unsupported model type {model_type}")
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