loading models in 4 bit

this enables pascal GPUS to load LLama 2 when used with --quantize bitsandbytes

server/Makefile

@@ -24,7 +24,6 @@ install: gen-server install-torch
 	pip install -e ".[bnb, accelerate]"
 
 run-dev:
-	SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
+	SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=1 text_generation_server/cli.py serve /mnt/TOFU/HF_MODELS/Llama-2-7b-chat-hf --quantize bitsandbytes
-
 export-requirements:
 	poetry export -o requirements.txt -E bnb -E quantize --without-hashes

server/text_generation_server/cli.py

@@ -141,10 +141,12 @@ def download_weights(
             if not extension == ".safetensors" or not auto_convert:
                 raise e
 
+    logger.warning("attempting to load local model")
     # Try to see if there are local pytorch weights
     try:
         # Get weights for a local model, a hub cached model and inside the WEIGHTS_CACHE_OVERRIDE
         local_pt_files = utils.weight_files(model_id, revision, ".bin")
+        print(local_pt_files)
 
     # No local pytorch weights
     except utils.LocalEntryNotFoundError:

server/text_generation_server/models/causal_lm.py

@@ -3,7 +3,7 @@ import inspect
 
 from dataclasses import dataclass
 from opentelemetry import trace
-from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
+from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Dict
 
 from text_generation_server.models import Model
@@ -481,11 +481,12 @@ class CausalLM(Model):
             device_map="auto"
             if torch.cuda.is_available() and torch.cuda.device_count() > 1
             else None,
-            load_in_8bit=quantize == "bitsandbytes",
+            load_in_4bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1:
+        ## ValueError: Calling `cuda()` is not supported for `4-bit` or `8-bit` quantized models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.
-            model = model.cuda()
+        # if torch.cuda.is_available() and torch.cuda.device_count() == 1:
+            # model = model.cuda()
 
         if tokenizer.pad_token_id is None:
             if model.config.pad_token_id is not None:

server/text_generation_server/server.py

@@ -109,6 +109,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         generations, next_batch = self.model.generate_token(batch)
         self.cache.set(next_batch)
 
+        print(generations)
         return generate_pb2.DecodeResponse(
             generations=[generation.to_pb() for generation in generations],
             batch=next_batch.to_pb() if next_batch else None,

server/text_generation_server/utils/__init__.py

@@ -1,7 +1,7 @@
 from text_generation_server.utils.convert import convert_file, convert_files
 from text_generation_server.utils.dist import initialize_torch_distributed
 from text_generation_server.utils.weights import Weights
-from text_generation_server.utils.peft import download_and_unload_peft
+from text_generation_server.utils.peft import download_and_unload_peft, load_local_peft
 from text_generation_server.utils.hub import (
     weight_files,
     weight_hub_files,
@@ -28,6 +28,7 @@ __all__ = [
     "weight_hub_files",
     "download_weights",
     "download_and_unload_peft",
+    "load_local_peft",
     "EntryNotFoundError",
     "HeterogeneousNextTokenChooser",
     "LocalEntryNotFoundError",

server/text_generation_server/utils/peft.py

@@ -6,6 +6,8 @@ import torch
 from transformers import AutoTokenizer
 from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
 
+def load_local_peft(model_id, revision, trust_remote_code):
+    return model_id
 def download_and_unload_peft(model_id, revision, trust_remote_code):
     torch_dtype = torch.float16