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a072660bf5
# What does this PR do? A few tokenizer_config in huggingface use LlamaTokenizer, so I think I would have selected `LlamaTokenizer` before. For a few cases where you're using a llama structure but not a llama tokenizer, why not make it to call the AutoTokenizer in exception handling. In the case of `decapoda-research/llama-7b-hf`, LLamaTokenizer is still being used in config.json, so it should be called through` LlamaTokenizer`. Also, if an exception is thrown by LlamaTokenizer, it will cause `LlamaTokenzierFast` to be called from AutoTokenizer. Fixes # 560 ## 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. - [x] 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. @Narsil
82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
import torch
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import torch.distributed
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from opentelemetry import trace
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from transformers import AutoConfig, AutoTokenizer
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from transformers.models.llama import LlamaTokenizer
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from typing import Optional
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_llama_modeling import (
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FlashLlamaForCausalLM,
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LlamaConfig,
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)
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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tracer = trace.get_tracer(__name__)
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class FlashLlama(FlashCausalLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashLlama is only available on GPU")
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try:
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tokenizer = LlamaTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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except Exception:
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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config = LlamaConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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config.quantize = quantize
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(filenames, device, dtype, process_group=self.process_group)
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if config.quantize == "gptq":
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weights._set_gptq_params(model_id)
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model = FlashLlamaForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashLlama, self).__init__(
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model=model,
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tokenizer=tokenizer,
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num_layers=len(model.model.layers),
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num_kv_heads=model.model.num_key_value_heads,
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head_size=model.model.head_size,
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dtype=dtype,
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device=device,
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rank=rank,
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world_size=world_size,
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)
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