non modeling.

server/text_generation_server/models/flash_mpt.py

@@ -1,73 +0,0 @@
-import torch
-import torch.distributed
-
-from opentelemetry import trace
-from transformers import AutoConfig, AutoTokenizer, PretrainedConfig
-from typing import Optional
-from huggingface_hub import hf_hub_download
-import json
-
-from text_generation_server.models import FlashCausalLM
-from text_generation_server.models.custom_modeling.flash_mpt_modeling import (
-    MPTForCausalLM,
-)
-from text_generation_server.utils import (
-    initialize_torch_distributed,
-    weight_files,
-    Weights,
-)
-
-tracer = trace.get_tracer(__name__)
-
-
-class MPTSharded(FlashCausalLM):
-    def __init__(
-        self,
-        model_id: str,
-        revision: Optional[str] = None,
-        quantize: Optional[str] = None,
-        trust_remote_code: bool = False,
-    ):
-        self.process_group, rank, world_size = initialize_torch_distributed()
-        if torch.cuda.is_available():
-            device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
-        else:
-            raise NotImplementedError("FlashMPT is only available on GPU")
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-
-        filename = hf_hub_download(model_id, revision=revision, filename="config.json")
-        with open(filename, "r") as f:
-            config = json.load(f)
-        config = PretrainedConfig(**config)
-        config.quantize = quantize
-        # config = AutoConfig.from_pretrained(
-        #     # model_id, revision=revision, trust_remote_code=trust_remote_code
-        #     model_id, revision=revision, trust_remote_code=False
-        # )
-
-        torch.distributed.barrier(group=self.process_group)
-
-        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
-        weights = Weights(filenames, device, dtype, process_group=self.process_group)
-
-        config.quantize = quantize
-        model = MPTForCausalLM(config, weights)
-
-        torch.distributed.barrier(group=self.process_group)
-        super(FlashCausalLM, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=False,
-            dtype=dtype,
-            device=device,
-            rank=rank,
-            world_size=world_size,
-        )