fix alignment-handbook version

alignment/model_utils.py

@@ -0,0 +1,128 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from pathlib import Path
+from typing import Dict
+
+import torch
+from transformers import AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizer
+from transformers.trainer_utils import get_last_checkpoint
+
+from accelerate import Accelerator
+from huggingface_hub import list_repo_files
+from huggingface_hub.utils._errors import RepositoryNotFoundError
+from huggingface_hub.utils._validators import HFValidationError
+from peft import LoraConfig, PeftConfig
+
+from .configs import DataArguments, DPOConfig, ModelArguments, SFTConfig
+from .data import DEFAULT_CHAT_TEMPLATE
+
+
+def get_current_device() -> int:
+    """Get the current device. For GPU we return the local process index to enable multiple GPU training."""
+    return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"
+
+
+def get_kbit_device_map() -> Dict[str, int] | None:
+    """Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
+    return {"": get_current_device()} if torch.cuda.is_available() else None
+
+
+def get_quantization_config(model_args: ModelArguments) -> BitsAndBytesConfig | None:
+    if model_args.load_in_4bit:
+        compute_dtype = torch.float16
+        if model_args.torch_dtype not in {"auto", None}:
+            compute_dtype = getattr(torch, model_args.torch_dtype)
+
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=compute_dtype,
+            bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
+            bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
+            bnb_4bit_quant_storage=model_args.bnb_4bit_quant_storage,
+        )
+    elif model_args.load_in_8bit:
+        quantization_config = BitsAndBytesConfig(
+            load_in_8bit=True,
+        )
+    else:
+        quantization_config = None
+
+    return quantization_config
+
+
+def get_tokenizer(
+    model_args: ModelArguments, data_args: DataArguments, auto_set_chat_template: bool = True
+) -> PreTrainedTokenizer:
+    """Get the tokenizer for the model."""
+    tokenizer = AutoTokenizer.from_pretrained(
+        (
+            model_args.model_name_or_path
+            if model_args.tokenizer_name_or_path is None
+            else model_args.tokenizer_name_or_path
+        ),
+        revision=model_args.model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    if data_args.truncation_side is not None:
+        tokenizer.truncation_side = data_args.truncation_side
+
+    # Set reasonable default for models without max length
+    if tokenizer.model_max_length > 100_000:
+        tokenizer.model_max_length = 2048
+
+    if data_args.chat_template is not None:
+        tokenizer.chat_template = data_args.chat_template
+    elif auto_set_chat_template and tokenizer.chat_template is None and tokenizer.default_chat_template is None:
+        tokenizer.chat_template = DEFAULT_CHAT_TEMPLATE
+
+    return tokenizer
+
+
+def get_peft_config(model_args: ModelArguments) -> PeftConfig | None:
+    if model_args.use_peft is False:
+        return None
+
+    peft_config = LoraConfig(
+        r=model_args.lora_r,
+        lora_alpha=model_args.lora_alpha,
+        lora_dropout=model_args.lora_dropout,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules=model_args.lora_target_modules,
+        modules_to_save=model_args.lora_modules_to_save,
+    )
+
+    return peft_config
+
+
+def is_adapter_model(model_name_or_path: str, revision: str = "main") -> bool:
+    try:
+        # Try first if model on a Hub repo
+        repo_files = list_repo_files(model_name_or_path, revision=revision)
+    except (HFValidationError, RepositoryNotFoundError):
+        # If not, check local repo
+        repo_files = os.listdir(model_name_or_path)
+    return "adapter_model.safetensors" in repo_files or "adapter_model.bin" in repo_files
+
+
+def get_checkpoint(training_args: SFTConfig | DPOConfig) -> Path | None:
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir):
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+    return last_checkpoint