95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
import torch
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import torch.distributed
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from typing import List, Optional, Tuple
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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AutoProcessor,
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)
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from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
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from text_generation_server.models.custom_modeling.idefics_processing import (
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IdeficsProcessor,
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)
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from transformers import LlamaTokenizerFast
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from text_generation_server.models.custom_modeling.idefics_modeling import (
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IdeficsForVisionText2Text,
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)
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from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
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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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class IDEFICSSharded(IdeficsCausalLM):
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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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speculator: 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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# 9b seems to work correctly enough in float16, but 80b seems
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# to be really saturating for f16.
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dtype = torch.float16 if dtype is None else dtype
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else:
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device = torch.device("cpu")
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dtype = torch.float32 if dtype is None else dtype
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self.device, self.dtype = device, dtype
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config = IdeficsConfig.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code,
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)
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config.quantize = quantize
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config.speculator = speculator
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config.vision_config.quantize = quantize
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tokenizer = LlamaTokenizerFast.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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self.processor = IdeficsProcessor.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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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(
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filenames,
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device=device,
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dtype=dtype,
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process_group=self.process_group,
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)
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model = IdeficsForVisionText2Text(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(IdeficsCausalLM, self).__init__(
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model_id=model_id,
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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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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