Fix some comments and tokenizer outputs from P-Tuning to RLHF

apps/accelerate/chatllama/chatllama/llama_model.py

@@ -22,7 +22,7 @@ def __init__(self, tokenizer: Tokenizer):
     def __call__(self, texts: Union[List[str], str], *args, **kwargs):
         if isinstance(texts, str):
             text = self.tokenizer.encode(texts, bos=True, eos=True)
-            return torch.tensor(text).cuda().long()
+            tokens = torch.tensor(text).cuda().long()
         else:
             texts = [
                 self.tokenizer.encode(text, bos=True, eos=True)
@@ -36,7 +36,11 @@ def __call__(self, texts: Union[List[str], str], *args, **kwargs):
             )
             for i, text in enumerate(texts):
                 tokens[i, : len(text)] = torch.tensor(text).cuda().long()
-            return tokens
+        output = {
+            "input_ids": tokens,
+            "attention_mask": (tokens != self.tokenizer.pad_id).long(),
+        }
+        return output
 
     def decode(self, tokens):
         return self.tokenizer.decode(tokens)

apps/accelerate/chatllama/chatllama/rlhf/actor.py

@@ -14,8 +14,7 @@
 
 class ActorModel(torch.nn.Module):
     """Actor model that generates the augmented prompt from the initial
-    user_input. Its output is used as prompt for the LLM model.
-    The aim is to train this model to generate better prompts.
+    user_input. The aim is to train this model to generate better prompts.
 
     Attributes:
         model: The model from LLaMA to be used
@@ -64,7 +63,7 @@ def forward(
         Args:
             sequences (torch.Tensor): Sequences of states and actions used to
                     compute token logits for the whole list of sequences
-            attention_mask (torch.Tensor): Mask fo the sequences attention
+            attention_mask (torch.Tensor): Mask for the sequences attention
 
         Returns:
             logits (torch.Tensor): Logits for the actions taken
@@ -87,19 +86,19 @@ def generate(
             (i.e. input of the prompt generator model)
 
         Args:
-            state (torch.Tensor): State the input of the user to generate
-                the prompt (action)
-            state_mask (torch.Tensor): Mask for the state of the environment
-                (mask for the states)
+            state (torch.Tensor): the input of the user
+            state_mask (torch.Tensor): Mask for the state input (for padding)
 
         Returns:
             actions (torch.Tensor): Actions generated from the state
             sequences (torch.Tensor): Sequences generated from the
                 state as [states, actions]
         """
         max_sequence = states.shape[1]
-        max_tokens = max_sequence + self.config.max_tokens
+        max_tokens = self.config.max_tokens + max_sequence
         temperature = self.config.temperature
+        # What if the states + completion are longer than the max context of
+        # the model?
         sequences = self.model.generate(
             inputs=states,
             attention_mask=state_mask,
@@ -133,7 +132,7 @@ def load(self, path: Optional[str] = None) -> None:
                     f"The path doesn't exist."
                 )
                 return
-        # load the model and the tokenizer
+        # load the model
         if os.path.exists(path) is False:
             print(
                 f"Impossible to load the model: {path}"
@@ -163,32 +162,14 @@ class ActorDataset(Dataset):
     read a json file with the following format:
     [
         {
-            "general_info": "..."
-            "general_purpose": "..."
-            "contract_name": "..."
-            "section_key_points": "..."
-            "section_name": "..."
-            "section_number": ...
-            "text": "..."
-            "text_type": "..."
-            "generation_prompt": "..."
-            "score": ...
+            "user_input": "..."
+            "completion": "..."
         } ,
         ...
     ]
     Where:
-        general_info: general information of the contract (names, dates, ...)
-        general_purpose: purpose of the contract
-        contract_name: name of the contract
-        section_key_points: key points of this section
-        section_name: name of the section
-        section_number: number of the section (to determine the order)
-        text: text of the section
-        text_type: could be (data, davinci, curie, etc... depending on how it
-            was generated)
-        generation_prompt: prompt used to generate the text (in case it was
-            generated by a model, None if it data)
-        score: score of the section (0-5) given by the LLM
+        user_input: the input of the user
+        completion: the output of the user
     """
 
     def __init__(self, path: str, device: torch.device) -> None:
@@ -197,7 +178,7 @@ def __init__(self, path: str, device: torch.device) -> None:
         with open(path, "r") as f:
             data = json.load(f)
             self.data = [
-                d["user_input"] + "\n\n###\n\n" + d["prompt"] for d in data
+                d["user_input"] + "\n\n###\n\n" + d["completion"] for d in data
             ]
         self.len = len(self.data)
 
@@ -282,7 +263,6 @@ def train(
                 attention_mask = input_output_tokenized["attention_mask"][
                     :, :-1
                 ]
-                # TODO add check for sequence length
                 training_output = training_output.to(device)
                 training_input = training_input.to(device)
                 attention_mask = attention_mask.to(device)
@@ -312,7 +292,6 @@ def train(
                     input_output_tokenized = self.model.tokenizer(
                         input_output, return_tensors="pt", padding=True
                     )
-                    # TODO: Add check for sequence length
                     validation_output = input_output_tokenized["input_ids"][
                         :, 1:
                     ]

apps/accelerate/chatllama/chatllama/rlhf/reward.py

@@ -3,7 +3,7 @@
 
 import torch
 from beartype import beartype
-from beartype.typing import Optional, Iterable, List, Tuple
+from beartype.typing import Optional, Iterable
 from einops.layers.torch import Rearrange
 from langchain import OpenAI, LLMChain, PromptTemplate
 from torch.utils.data import Dataset, DataLoader
@@ -35,7 +35,7 @@ class RewardModel(torch.nn.Module):
 
     def __init__(self, config: ConfigReward) -> None:
         super().__init__()
-        # load the model
+        # load the model -- add here other models
         head_hidden_size = config.model_head_hidden_size
         if config.model == "gpt2-large":
             self.max_model_tokens = 1024
@@ -132,6 +132,8 @@ def forward(
         output = self.model(
             output_sequence, attention_mask=output_sequence_mask
         )
+        # What if the output_sequence is longer than the max context of
+        # the model?
         rewards = self.head(output.last_hidden_state)
         if self.config.debug:
             print("RewardModel.forward")
@@ -209,29 +211,16 @@ class RewardDataset(Dataset):
     read a json file with the following format:
     [
         {
-            "general_info": "..."
-            "general_purpose": "..."
-            "contract_name": "..."
-            "section_key_points": "..."
-            "section_name": "..."
-            "section_number": ...
-            "text": "..."
-            "text_type": "..."
+            "user_input": "...",
+            "completion": "...",
             "score": ...
-        } ,
+        },
         ...
     ]
     Where:
-        general_info: general information of the contract (names, dates, ...)
-        general_purpose: purpose of the contract
-        contract_name: name of the contract
-        section_key_points: key points of this section
-        section_name: name of the section
-        section_number: number of the section (to determine the order)
-        text: text of the section
-        text_type: could be (data, davinci, curie, etc... depending on how it
-            was generated)
-        score: score of the section (0-5) given by the LLM
+        user_input: the initial input of the user
+        completion: the completion generated by the model
+        score: the score given by the user to the completion (or by the LLM)
     """
 
     def __init__(self, path: str) -> None:
@@ -241,11 +230,9 @@ def __init__(self, path: str) -> None:
         print(f"Loaded {len(self.data)} samples")
 
     def __getitem__(self, idx: int):
-        section = self.data[idx]["section_name"]
-        contract = self.data[idx]["contract_name"]
-        purpose = self.data[idx]["general_purpose"]
-        key_points = self.data[idx]["section_key_points"]
-        item = tuple([section, contract, purpose, key_points])
+        user_input = self.data[idx]["user_input"]
+        completion = self.data[idx]["completion"]
+        item = tuple([user_input, completion])
         return item
 
     def __len__(
@@ -295,20 +282,6 @@ def __init__(self, config: ConfigReward) -> None:
         prompt_template = PromptTemplate(**REWARD_TEMPLATE)
         self.llm = LLMChain(llm=openai_llm, prompt=prompt_template)
 
-    def generate_user_input(self, inputs: Tuple) -> List[str]:
-        user_inputs = []
-        for i, input in enumerate(inputs):
-            section = input[0]
-            contract = input[1]
-            purpose = input[2]
-            key_points = input[3]
-            user_inputs.append(
-                f"Write the contract section {section} for the contract "
-                f"{contract} whose purpose is:\n{purpose}\n"
-                f"The key points for this section should be:\n{key_points}\n"
-            )
-            return user_inputs
-
     def distillate(
         self,
     ):
@@ -384,10 +357,10 @@ def train(
             self.model.train()
             for i, inputs in enumerate(train_dataloader):
 
-                user_inputs = self.generate_user_input(inputs)
+                input_text = inputs["user_input"] + inputs["completion"]
                 # tokenizer (placed here instead of dataset class)
                 input_tokens = self.model.tokenizer(
-                    user_inputs, padding=True, truncation=True
+                    input_text, padding=True, truncation=True
                 )
 
                 score = None  # TODO: load the score

apps/accelerate/chatllama/chatllama/rlhf/test.py

@@ -14,15 +14,15 @@ def test_actor_training(path=None, device=None, debug=False):
 
 
 def test_reward_training(path=None, device=None, debug=False):
-    device = torch.device("cuda:1")
+    device = torch.device("cuda:0")
     config = Config(path=path, device=device, debug=debug)
     trainer = RewardTrainer(config.reward)
     trainer.train()
     trainer.training_stats.plot()
 
 
 def test_rl_trainig(path=None, device=None, debug=False):
-    device = torch.device("cuda:1")
+    device = torch.device("cuda:0")
     config = Config(path=path, device=device, debug=debug)
     trainer = RLTrainer(config.trainer)
     trainer.distillate()
@@ -35,7 +35,8 @@ def test_rl_trainig(path=None, device=None, debug=False):
     rl_training = False
     actor_training = False
 
-    device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    # place here the path to the config.yaml file
     config_path = "/home/pierpaolo/Documents/optimapi/ptuning/config.yaml"
 
     if reward_training:

apps/accelerate/chatllama/chatllama/rlhf/trainer.py

@@ -5,7 +5,7 @@
 
 import torch
 from beartype import beartype
-from beartype.typing import Deque, Tuple, List, Dict
+from beartype.typing import Deque, Tuple, List
 from einops import rearrange
 from torch.utils.data import Dataset, DataLoader
 
@@ -91,13 +91,10 @@ def forward(
     def generate(
         self, states: torch.Tensor, state_mask: torch.Tensor
     ) -> Tuple:
-        """Generate actions, actions_logits,
-            values and sequences from states (i.e. input of the
-            prompt generator model)
+        """Generate actions, actions_logits, values and sequences from states
 
         Args:
-            states (torch.Tensor): States the input of the user to
-                generate the prompt (action)
+            states (torch.Tensor): user inputs
             state_mask (torch.Tensor): Mask for the states of the environment
 
         Returns:
@@ -183,29 +180,13 @@ class ExamplesSampler:
     read a json file with the following format:
     [
         {
-            "general_info": "..."
-            "general_purpose": "..."
-            "contract_name": "..."
-            "section_key_points": "..."
-            "section_name": "..."
-            "section_number": ...
-            "text": "..."
-            "text_type": "..."
-            "score": ...
+            "user_input" : "",
         } ,
         ...
     ]
     Where:
-        general_info: general information of the contract (names, dates, ...)
-        general_purpose: purpose of the contract
-        contract_name: name of the contract
-        section_key_points: key points of this section
-        section_name: name of the section
-        section_number: number of the section (to determine the order)
-        text: text of the section
-        text_type: could be (data, davinci, curie, etc... depending on how it
-            was generated)
-        score: score of the section (0-5)
+        user_input: is the input of the user or directly the input of the user
+            with the memory preappended (i.e. user_input + memory)
     """
 
     def __init__(
@@ -276,7 +257,6 @@ def __init__(
         self.eps = 1e-8
 
         # make models directory
-        # TODO make this more general
         if not os.path.exists("./models"):
             os.mkdir("./models")
 
@@ -489,20 +469,6 @@ def learn(self, memories: Deque[Memory]) -> None:
         self.actorcritic.eval()
         print("End Learning")
 
-    def generate_user_input(self, inputs: List[Dict]) -> List[str]:
-        user_inputs = []
-        for i, input in enumerate(inputs):
-            section = input["section_name"]
-            contract = input["contract_name"]
-            purpose = input["general_purpose"]
-            key_points = input["section_key_points"]
-            user_inputs.append(
-                f"Write the contract section {section} for the contract "
-                f"{contract} whose purpose is:\n{purpose}\n"
-                f"The key points for this section should be:\n{key_points}\n"
-            )
-        return user_inputs
-
     def train(
         self,
     ) -> None:
@@ -558,13 +524,9 @@ def train(
                 # sample num_examples examples from  example dataset
                 inputs = self.example_sampler.sample(num_examples)
 
-                # generate user input from contract
-                # section info for each example
-                user_inputs = self.generate_user_input(inputs)
-
                 # tokenize examples
                 tokenized_inputs = self.actorcritic.actor.tokenizer(
-                    user_inputs, padding=True, return_tensors="pt"
+                    inputs, padding=True, return_tensors="pt"
                 )
                 if self.debug:
                     print("RLTrainer.train()")

apps/accelerate/chatllama/generate_dataset.py

@@ -25,6 +25,8 @@ def create_conversation(human_agent: LLMChain, bot_agent: LLMChain):
 
 
 def build_agents():
+    # be aware that too long completions will not fit the sequence length
+    # of possible critic or reward models ...
     llm = OpenAI(max_tokens=2048, temperature=0.7)
     human_template = PromptTemplate(**PERSON_CHATBOT_TEMPLATE)
     human_agent = LLMChain(