91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
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import torch
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import torch.distributed
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from typing import Optional, Type
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from opentelemetry import trace
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from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBase
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from huggingface_hub import hf_hub_download
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import json
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from text_generation_server.models import CausalLM
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from text_generation_server.models.causal_lm import CausalLMBatch
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from text_generation_server.pb import generate_pb2
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from text_generation_server.models.custom_modeling.mpt_modeling import (
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MPTForCausalLM,
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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 MPTCausalLMBatch(CausalLMBatch):
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "CausalLMBatch":
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batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
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batch.keys_head_dim_last = False
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return batch
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class MPTSharded(CausalLM):
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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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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
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else:
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raise NotImplementedError("MPTSharded is only available on GPU")
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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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tokenizer.pad_token = tokenizer.eos_token
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filename = hf_hub_download(model_id, revision=revision, filename="config.json")
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with open(filename, "r") as f:
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config = json.load(f)
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config = PretrainedConfig(**config)
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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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config.quantize = quantize
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model = MPTForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(CausalLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=False,
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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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@property
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def batch_type(self) -> Type[CausalLMBatch]:
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return MPTCausalLMBatch
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