Remove useless modification

Signed-off-by: yuanwu <yuan.wu@intel.com>

server/text_generation_server/models/flash_causal_lm.py

@@ -10,12 +10,7 @@ import numpy as np
 from loguru import logger
 from dataclasses import dataclass
 from opentelemetry import trace
-from transformers import (
+from transformers import PreTrainedTokenizerBase
-    PreTrainedTokenizerBase,
-    AutoConfig,
-    AutoTokenizer,
-    GenerationConfig,
-)
 from typing import Optional, Tuple, List, Type, Dict
 
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
@@ -24,11 +19,6 @@ from text_generation_server.models import Model
 from text_generation_server.utils.tokens import batch_top_tokens
 from text_generation_server.utils.dist import RANK
 from text_generation_server.utils.speculate import get_speculate
-from text_generation_server.utils import (
-    initialize_torch_distributed,
-    weight_files,
-    Weights,
-)
 from text_generation_server.models.types import (
     Batch,
     Tokens,
@@ -696,97 +686,20 @@ class FlashCausalLMBatch(Batch):
 class FlashCausalLM(Model):
     def __init__(
         self,
-        model_id: str,
+        model: torch.nn.Module,
-        model_class,
+        tokenizer: PreTrainedTokenizerBase,
-        revision: Optional[str] = None,
+        num_layers: int,
-        quantize: Optional[str] = None,
+        num_kv_heads: int,
-        speculator: Optional[str] = None,
+        head_size: int,
-        dtype: Optional[torch.dtype] = None,
+        dtype: torch.dtype,
-        trust_remote_code: bool = False,
+        device: torch.device,
-        lora_adapter_ids: Optional[list] = [],
+        rank: int = 0,
-        tokenizer_class: PreTrainedTokenizerBase = AutoTokenizer,
+        world_size: int = 1,
-        config_class: PreTrainedTokenizerBase = AutoConfig,
+        sliding_window: Optional[int] = None,
-        default_dtype=torch.bfloat16,
-        aliases=None,
-        # Used for Santacoder override of config
-        num_kv_heads: Optional[int] = None,
-        # Deepseek V2 uses different QK and V dims.
-        head_size: Optional[int] = None,
-        skip_special_tokens: bool = True,
     ):
-        
+        self.num_layers = num_layers
-        # Create model
+        self.num_kv_heads = num_kv_heads
-        world_size = int(os.getenv("WORLD_SIZE", "1"))
+        self.head_size = head_size
-        rank = int(os.getenv("RANK", "0"))
-        dtype = torch.bfloat16 if dtype is None else dtype
-        device = torch.device("hpu")
-
-        tokenizer = tokenizer_class.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        try:
-            generation_config = GenerationConfig.from_pretrained(
-                model_id, revision=revision, trust_remote_code=trust_remote_code
-            )
-            if isinstance(generation_config.eos_token_id, (list, set)):
-                # TODO Huge hack
-                tokenizer._eos_token_ids = set(generation_config.eos_token_id)
-        except Exception:
-            pass
-
-        config = config_class.from_pretrained(
-            model_id, revision=revision, trust_remote_code=trust_remote_code
-        )
-        config.quantize = quantize
-        config.speculator = speculator
-
-
-        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
-        weights = Weights(filenames, device, dtype)
-
-        
-
-        prefix = ""
-        model = model_class(prefix, config, weights)
-
-        # VLM models define the config we care about in their text_config
-        text_config = getattr(config, "text_config", None)
-        if text_config is not None:
-            config = text_config
-
-
-        self.num_layers = config.num_hidden_layers
-        # Validation is done in the model itself
-        if num_kv_heads is None:
-            num_kv_heads = getattr(config, "num_key_value_heads", None)
-            # GPT-2 workaround
-            if num_kv_heads is None:
-                num_kv_heads = getattr(config, "n_head", None)
-        if num_kv_heads is None:
-            raise ValueError("Cannot get the number of key/value heads")
-        self.num_kv_heads = num_kv_heads (
-            num_kv_heads // self.process_group.size()
-            if num_kv_heads > 1
-            else num_kv_heads
-        )
-        assert self.num_kv_heads > 0
-
-        if head_size is None:
-            # Some models use GQA and different sizes for o_proj
-            # and q_proj, that allows for that.
-            if hasattr(config, "head_dim"):
-                self.head_size = config.head_dim
-            else:
-                self.head_size = config.hidden_size // config.num_attention_heads
-        else:
-            self.head_size = head_size
-
-        self.cuda_graphs = {}
-        self.kv_cache = []
 
         self.cuda_graphs = {}
 
@@ -798,7 +711,7 @@ class FlashCausalLM(Model):
             device=device,
             rank=rank,
             world_size=world_size,
-            sliding_window=None,
+            sliding_window=sliding_window,
         )
 
     @property