Remove dead code.

server/text_generation_server/models/idefics.py

@@ -1,113 +0,0 @@
-import torch
-import torch.distributed
-
-from typing import Optional
-
-
-from transformers import AutoConfig, AutoProcessor, AutoTokenizer
-from text_generation_server.models.custom_modeling.idefics_modeling import (
-    IdeficsForVisionText2Text,
-)
-from text_generation_server.models.custom_modeling.mllama import (
-    MllamaForConditionalGeneration,
-)
-from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
-from text_generation_server.utils import (
-    initialize_torch_distributed,
-    weight_files,
-    Weights,
-)
-from text_generation_server.utils.quantization import get_loader
-
-from text_generation_server.utils.import_utils import SYSTEM
-
-
-class IDEFICSSharded(IdeficsCausalLM):
-    def __init__(
-        self,
-        model_id: str,
-        revision: Optional[str] = None,
-        quantize: Optional[str] = None,
-        speculator: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-        trust_remote_code: bool = False,
-    ):
-        self.quantize = quantize
-        self.process_group, rank, world_size = initialize_torch_distributed()
-        if torch.cuda.is_available():
-            device = torch.device(f"cuda:{rank}")
-            # 9b seems to work correctly enough in float16, but 80b seems
-            # to be really saturating for f16.
-            dtype = torch.float16 if dtype is None else dtype
-        elif SYSTEM == "ipex":
-            if hasattr(torch, "xpu") and torch.xpu.is_available():
-                device = torch.device(f"xpu:{rank}")
-                dtype = torch.float16 if dtype is None else dtype
-            else:
-                device = torch.device("cpu")
-                # Float16 doesn't exist on target.
-                dtype = torch.bfloat16 if dtype is None else dtype
-        else:
-            device = torch.device("cpu")
-            dtype = torch.float32 if dtype is None else dtype
-        self.device, self.dtype = device, dtype
-
-        config = AutoConfig.from_pretrained(
-            model_id,
-            revision=revision,
-            trust_remote_code=trust_remote_code,
-        )
-        config.quantize = quantize
-        config.speculator = speculator
-        config.vision_config.quantize = quantize
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        self.processor = AutoProcessor.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-
-        weights_loader = get_loader(
-            quantize=quantize, model_id=model_id, revision=revision
-        )
-        torch.distributed.barrier(group=self.process_group)
-        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
-        weights = Weights(
-            filenames,
-            device=device,
-            dtype=dtype,
-            process_group=self.process_group,
-            weights_loader=weights_loader,
-        )
-
-        if config.model_type == "idefics":
-            model = IdeficsForVisionText2Text(config, weights)
-        elif config.model_type == "mllama":
-            model = MllamaForConditionalGeneration(
-                prefix="", config=config, weights=weights
-            )
-        else:
-            raise RuntimeError(f"Unsupported model type {config.model_type}")
-
-        self.config = config
-
-        torch.distributed.barrier(group=self.process_group)
-        super(IdeficsCausalLM, self).__init__(
-            model_id=model_id,
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-            rank=rank,
-            world_size=world_size,
-        )