prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Llama Reward Model is incorrectly merged
  As mentioned in #287, `merge_peft_adapter` saves the Llama RM as a `LlamaForCausalLM` see [here](https://github.com/lvwerra/trl/blob/main/examples/stack_llama/scripts/merge_peft_adapter.py#L35)
  
  But the reward model is trained and should be a `LlamaForSequenceClassification` and running `rl_training.py` gives the obvious warnings
  ```
  Some weights of the model checkpoint at ./llama-7b-se-rm were not used when initializing LlamaForSequenceClassification: ['lm_head.weight']
  - This IS expected if you are initializing LlamaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
  - This IS NOT expected if you are initializing LlamaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at /home/toolkit/huggingface/llama-7b-rm and are newly initialized: ['score.weight']
  You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
  ```
  
  We should instead check whether we are merging the rm and then save as a the correct model 
  
  Also the `score.weight` is not being loaded as mentioned in #297 , see more info below
  
  
  --- update --
  
  It seems that `merge_peft_adapter` should be using `merge_and_unload()` which correctly overrides the score. But I haven't yet managed to get good results using the adapter weights on the hub
  
  </issue>
  <code>
  [start of examples/stack_llama/scripts/merge_peft_adapter.py]
  1 from dataclasses import dataclass, field
  2 from typing import Optional
  3 
  4 import peft
  5 import torch
  6 from peft import PeftConfig, PeftModel
  7 from peft.utils import _get_submodules
  8 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
  9 
  10 
  11 DEFAULT_PAD_TOKEN = "[PAD]"
  12 DEFAULT_EOS_TOKEN = "</s>"
  13 DEFAULT_BOS_TOKEN = "</s>"
  14 DEFAULT_UNK_TOKEN = "</s>"
  15 
  16 
  17 @dataclass
  18 class ScriptArguments:
  19     """
  20     The name of the Casual LM model we wish to fine with PPO
  21     """
  22 
  23     adapter_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
  24     base_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
  25     output_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
  26 
  27 
  28 parser = HfArgumentParser(ScriptArguments)
  29 script_args = parser.parse_args_into_dataclasses()[0]
  30 assert script_args.adapter_model_name is not None, "please provide the name of the Adapter you would like to merge"
  31 assert script_args.base_model_name is not None, "please provide the name of the Base model"
  32 assert script_args.base_model_name is not None, "please provide the output name of the merged model"
  33 
  34 peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
  35 model = AutoModelForCausalLM.from_pretrained(script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16)
  36 tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
  37 config = AutoConfig.from_pretrained(script_args.base_model_name)
  38 architecture = config.architectures[0]
  39 if "Llama" in architecture:
  40     print("Setting EOS, BOS, and UNK tokens for LLama tokenizer")
  41     tokenizer.add_special_tokens(
  42         {
  43             "eos_token": DEFAULT_EOS_TOKEN,
  44             "bos_token": DEFAULT_BOS_TOKEN,
  45             "unk_token": DEFAULT_UNK_TOKEN,
  46             "pad_token": DEFAULT_PAD_TOKEN,
  47         }
  48     )
  49 
  50 # Load the Lora model
  51 model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
  52 model.eval()
  53 
  54 key_list = [key for key, _ in model.base_model.model.named_modules() if "lora" not in key]
  55 for key in key_list:
  56     parent, target, target_name = _get_submodules(model.base_model.model, key)
  57     if isinstance(target, peft.tuners.lora.Linear):
  58         bias = target.bias is not None
  59         new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
  60         model.base_model._replace_module(parent, target_name, new_module, target)
  61 
  62 model = model.base_model.model
  63 
  64 model.save_pretrained(f"{script_args.output_name}")
  65 tokenizer.save_pretrained(f"{script_args.output_name}")
  66 model.push_to_hub(f"{script_args.output_name}", use_temp_dir=False)
  67
  [end of examples/stack_llama/scripts/merge_peft_adapter.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/examples/stack_llama/scripts/merge_peft_adapter.py b/examples/stack_llama/scripts/merge_peft_adapter.py
  --- a/examples/stack_llama/scripts/merge_peft_adapter.py
  +++ b/examples/stack_llama/scripts/merge_peft_adapter.py
  @@ -1,17 +1,9 @@
   from dataclasses import dataclass, field
   from typing import Optional
   
  -import peft
   import torch
   from peft import PeftConfig, PeftModel
  -from peft.utils import _get_submodules
  -from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
  -
  -
  -DEFAULT_PAD_TOKEN = "[PAD]"
  -DEFAULT_EOS_TOKEN = "</s>"
  -DEFAULT_BOS_TOKEN = "</s>"
  -DEFAULT_UNK_TOKEN = "</s>"
  +from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
   
   
   @dataclass
  @@ -32,34 +24,23 @@
   assert script_args.base_model_name is not None, "please provide the output name of the merged model"
   
   peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
  -model = AutoModelForCausalLM.from_pretrained(script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16)
  -tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
  -config = AutoConfig.from_pretrained(script_args.base_model_name)
  -architecture = config.architectures[0]
  -if "Llama" in architecture:
  -    print("Setting EOS, BOS, and UNK tokens for LLama tokenizer")
  -    tokenizer.add_special_tokens(
  -        {
  -            "eos_token": DEFAULT_EOS_TOKEN,
  -            "bos_token": DEFAULT_BOS_TOKEN,
  -            "unk_token": DEFAULT_UNK_TOKEN,
  -            "pad_token": DEFAULT_PAD_TOKEN,
  -        }
  +if peft_config.task_type == "SEQ_CLS":
  +    # peft is for reward model so load sequence classification
  +    model = AutoModelForSequenceClassification.from_pretrained(
  +        script_args.base_model_name, num_labels=1, torch_dtype=torch.bfloat16
  +    )
  +else:
  +    model = AutoModelForCausalLM.from_pretrained(
  +        script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16
       )
   
  +tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
  +
   # Load the Lora model
   model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
   model.eval()
   
  -key_list = [key for key, _ in model.base_model.model.named_modules() if "lora" not in key]
  -for key in key_list:
  -    parent, target, target_name = _get_submodules(model.base_model.model, key)
  -    if isinstance(target, peft.tuners.lora.Linear):
  -        bias = target.bias is not None
  -        new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
  -        model.base_model._replace_module(parent, target_name, new_module, target)
  -
  -model = model.base_model.model
  +model = model.merge_and_unload()
   
   model.save_pretrained(f"{script_args.output_name}")
   tokenizer.save_pretrained(f"{script_args.output_name}")
  
verification_info: {"golden_diff": "diff --git a/examples/stack_llama/scripts/merge_peft_adapter.py b/examples/stack_llama/scripts/merge_peft_adapter.py\n--- a/examples/stack_llama/scripts/merge_peft_adapter.py\n+++ b/examples/stack_llama/scripts/merge_peft_adapter.py\n@@ -1,17 +1,9 @@\n from dataclasses import dataclass, field\n from typing import Optional\n \n-import peft\n import torch\n from peft import PeftConfig, PeftModel\n-from peft.utils import _get_submodules\n-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser\n-\n-\n-DEFAULT_PAD_TOKEN = \"[PAD]\"\n-DEFAULT_EOS_TOKEN = \"</s>\"\n-DEFAULT_BOS_TOKEN = \"</s>\"\n-DEFAULT_UNK_TOKEN = \"</s>\"\n+from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser\n \n \n @dataclass\n@@ -32,34 +24,23 @@\n assert script_args.base_model_name is not None, \"please provide the output name of the merged model\"\n \n peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)\n-model = AutoModelForCausalLM.from_pretrained(script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16)\n-tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)\n-config = AutoConfig.from_pretrained(script_args.base_model_name)\n-architecture = config.architectures[0]\n-if \"Llama\" in architecture:\n-    print(\"Setting EOS, BOS, and UNK tokens for LLama tokenizer\")\n-    tokenizer.add_special_tokens(\n-        {\n-            \"eos_token\": DEFAULT_EOS_TOKEN,\n-            \"bos_token\": DEFAULT_BOS_TOKEN,\n-            \"unk_token\": DEFAULT_UNK_TOKEN,\n-            \"pad_token\": DEFAULT_PAD_TOKEN,\n-        }\n+if peft_config.task_type == \"SEQ_CLS\":\n+    # peft is for reward model so load sequence classification\n+    model = AutoModelForSequenceClassification.from_pretrained(\n+        script_args.base_model_name, num_labels=1, torch_dtype=torch.bfloat16\n+    )\n+else:\n+    model = AutoModelForCausalLM.from_pretrained(\n+        script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16\n     )\n \n+tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)\n+\n # Load the Lora model\n model = PeftModel.from_pretrained(model, script_args.adapter_model_name)\n model.eval()\n \n-key_list = [key for key, _ in model.base_model.model.named_modules() if \"lora\" not in key]\n-for key in key_list:\n-    parent, target, target_name = _get_submodules(model.base_model.model, key)\n-    if isinstance(target, peft.tuners.lora.Linear):\n-        bias = target.bias is not None\n-        new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)\n-        model.base_model._replace_module(parent, target_name, new_module, target)\n-\n-model = model.base_model.model\n+model = model.merge_and_unload()\n \n model.save_pretrained(f\"{script_args.output_name}\")\n tokenizer.save_pretrained(f\"{script_args.output_name}\")\n", "issue": "Llama Reward Model is incorrectly merged\nAs mentioned in #287, `merge_peft_adapter` saves the Llama RM as a `LlamaForCausalLM` see [here](https://github.com/lvwerra/trl/blob/main/examples/stack_llama/scripts/merge_peft_adapter.py#L35)\r\n\r\nBut the reward model is trained and should be a `LlamaForSequenceClassification` and running `rl_training.py` gives the obvious warnings\r\n```\r\nSome weights of the model checkpoint at ./llama-7b-se-rm were not used when initializing LlamaForSequenceClassification: ['lm_head.weight']\r\n- This IS expected if you are initializing LlamaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing LlamaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of LlamaForSequenceClassification were not initialized from the model checkpoint at /home/toolkit/huggingface/llama-7b-rm and are newly initialized: ['score.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\n```\r\n\r\nWe should instead check whether we are merging the rm and then save as a the correct model \r\n\r\nAlso the `score.weight` is not being loaded as mentioned in #297 , see more info below\r\n\r\n\r\n--- update --\r\n\r\nIt seems that `merge_peft_adapter` should be using `merge_and_unload()` which correctly overrides the score. But I haven't yet managed to get good results using the adapter weights on the hub\n", "before_files": [{"content": "from dataclasses import dataclass, field\nfrom typing import Optional\n\nimport peft\nimport torch\nfrom peft import PeftConfig, PeftModel\nfrom peft.utils import _get_submodules\nfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser\n\n\nDEFAULT_PAD_TOKEN = \"[PAD]\"\nDEFAULT_EOS_TOKEN = \"</s>\"\nDEFAULT_BOS_TOKEN = \"</s>\"\nDEFAULT_UNK_TOKEN = \"</s>\"\n\n\n@dataclass\nclass ScriptArguments:\n    \"\"\"\n    The name of the Casual LM model we wish to fine with PPO\n    \"\"\"\n\n    adapter_model_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n    base_model_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n    output_name: Optional[str] = field(default=None, metadata={\"help\": \"the model name\"})\n\n\nparser = HfArgumentParser(ScriptArguments)\nscript_args = parser.parse_args_into_dataclasses()[0]\nassert script_args.adapter_model_name is not None, \"please provide the name of the Adapter you would like to merge\"\nassert script_args.base_model_name is not None, \"please provide the name of the Base model\"\nassert script_args.base_model_name is not None, \"please provide the output name of the merged model\"\n\npeft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)\nmodel = AutoModelForCausalLM.from_pretrained(script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16)\ntokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)\nconfig = AutoConfig.from_pretrained(script_args.base_model_name)\narchitecture = config.architectures[0]\nif \"Llama\" in architecture:\n    print(\"Setting EOS, BOS, and UNK tokens for LLama tokenizer\")\n    tokenizer.add_special_tokens(\n        {\n            \"eos_token\": DEFAULT_EOS_TOKEN,\n            \"bos_token\": DEFAULT_BOS_TOKEN,\n            \"unk_token\": DEFAULT_UNK_TOKEN,\n            \"pad_token\": DEFAULT_PAD_TOKEN,\n        }\n    )\n\n# Load the Lora model\nmodel = PeftModel.from_pretrained(model, script_args.adapter_model_name)\nmodel.eval()\n\nkey_list = [key for key, _ in model.base_model.model.named_modules() if \"lora\" not in key]\nfor key in key_list:\n    parent, target, target_name = _get_submodules(model.base_model.model, key)\n    if isinstance(target, peft.tuners.lora.Linear):\n        bias = target.bias is not None\n        new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)\n        model.base_model._replace_module(parent, target_name, new_module, target)\n\nmodel = model.base_model.model\n\nmodel.save_pretrained(f\"{script_args.output_name}\")\ntokenizer.save_pretrained(f\"{script_args.output_name}\")\nmodel.push_to_hub(f\"{script_args.output_name}\", use_temp_dir=False)\n", "path": "examples/stack_llama/scripts/merge_peft_adapter.py"}]}