1.1.3 release

CHANGELOG.md

@@ -0,0 +1,17 @@
+# Changelog
+
+## 1.1.3 / 2023-08-02
+
+### What’s Changed
+
+- Adds subtensor to metagraph sync by @camfairchild in #79
+- Fix wandb weights format logging by @p-ferreira in #88
+- Adds netuid tag to wandb runs by @p-ferreira in #95
+- Implements GPU cleaning for optmization by @Eugene-hu in #96
+- Adds compatibility with bittensor 5.3.3 by @camfairchild in #107
+- Adds historic diversity component by @isabella618033 in #111
+- Improvements on diveristy model by @isabella618033 and @Eugene-hu in #111
+- Prompt improvements by @mrseeker in #110 and @p-ferreira in #112
+- Adds Task Validator Filter to reward pipeline by @p-ferreira in #112
+- Fix for empty data retrieval from datasets by @p-ferreira in #113
+- Deprecates pip usage by @p-ferreira in #114

README.md

@@ -2,7 +2,6 @@
 
 # **Open Validators** <!-- omit in toc -->
 [![Discord Chat](https://img.shields.io/discord/308323056592486420.svg)](https://discord.gg/bittensor)
-[![PyPI version](https://badge.fury.io/py/openvalidators.svg)](https://badge.fury.io/py/openvalidators)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 
 
 ---
@@ -19,7 +18,7 @@ It offers several functionalities, such as:
 
 The main goal of this repository is to facilitate the interaction with the Bittensor network by providing a set of
 open-source validators to the community. The current validator implementation queries the network for responses and 
-evaluations using carefully crafted prompts, that are later evaluated by a large foundation GPT-J reward model.
+evaluations using carefully crafted prompts using CoT, that are later evaluated by a pipeline of reward functions, including diversity, relevance, rlhf, among others.
 
 Additionally, the repository provides an analysis and data toolkit that allows users to analyze the data generated from
 the validator's interaction with the network. By default, the validator collects various data points, such as question 
@@ -69,14 +68,7 @@ There are currently four main avenues for engaging with this repository:
    - Serves individuals, researchers, and developers who seek to create datasets for the community's miners.
 
 # Install
-There are two ways to use OpenTensor validators:
-
-1. With pip:
-```bash
-$ pip3 install openvalidators
-```
-
-2. From source:
+From source:
 ```bash
 $ git clone https://github.com/opentensor/validators.git
 $ pip3 install -e openvalidators/

openvalidators/__init__.py

@@ -28,6 +28,6 @@
 from . import weights
 from . import event
 
-__version__ = "1.1.2"
+__version__ = "1.1.3"
 version_split = __version__.split(".")
 __spec_version__ = (1000 * int(version_split[0])) + (10 * int(version_split[1])) + (1 * int(version_split[2]))

openvalidators/config.py

@@ -241,6 +241,12 @@ def add_args(cls, parser):
         action="store_true",
         help="Dont apply the diversity reward model",
         default=False,
+    )
+    parser.add_argument(
+        "--neuron.task_validator_off",
+        action="store_true",
+        help="Dont apply the task validator reward model",
+        default=False,
     )    
 
     parser.add_argument(

openvalidators/dataset.py

@@ -27,11 +27,17 @@ def __init__(self):
         self.openwebtext = iter( load_dataset("openwebtext", split="train", streaming=True).shuffle(seed=seed, buffer_size=10000) )
         self.red_pajama = iter( load_dataset("togethercomputer/RedPajama-Data-1T", 'default', split='train', streaming=True).shuffle(seed=seed, buffer_size=10000) )
 
-    def __next__(self):
-        if random.random() < 0.5:
-            return {"text": next(self.openwebtext)["text"]}
-        else:
-            return {"text": next(self.red_pajama)["text"]}
+    def __next__(self):         
+         while True:
+            bt.logging.debug('Retrieving data from dataset...')
+            if random.random() < 0.5:
+                text = next(self.openwebtext)["text"]
+            else:
+                text = next(self.red_pajama)["text"]
+
+            # Check if the text is not empty or does not consist only of newline characters
+            if text.strip():
+                return {"text": text}
 
 
 class MockDataset(Iterator):

openvalidators/event.py

@@ -44,6 +44,7 @@ class EventSchema:
     rlhf_reward_model: Optional[List[float]]  # Output vector of the rlhf reward model
     prompt_reward_model: Optional[List[float]]  # Output vector of the prompt reward model
     relevance_filter: Optional[List[float]]  # Output vector of the relevance scoring reward model
+    task_validator_filter: Optional[List[float]]
 
     # Weights data
     set_weights: Optional[List[List[float]]]
@@ -54,6 +55,7 @@ def from_dict(event_dict: dict, disable_log_rewards: bool) -> 'EventSchema':
         rewards = {
             'dahoas_reward_model': event_dict.get(RewardModelType.dahoas.value),
             'blacklist_filter': event_dict.get(RewardModelType.blacklist.value),
+            'task_validator_filter': event_dict.get(RewardModelType.task_validator.value),
             'nsfw_filter': event_dict.get(RewardModelType.nsfw.value),
             'relevance_filter': event_dict.get(RewardModelType.relevance.value),
             'reciprocate_reward_model': event_dict.get(RewardModelType.reciprocate.value),

openvalidators/forward.py

@@ -52,15 +52,13 @@ def get_random_uids(self, k: int, exclude: List[int] = None) -> torch.LongTensor
             avail_uids.append(uid)
             if uid_is_not_excluded:
                 candidate_uids.append(uid)
-
+                
     # Check if candidate_uids contain enough for querying, if not grab all avaliable uids
-    if len(candidate_uids) > k:
-        available_uids = torch.tensor(candidate_uids, dtype=torch.int64).to(self.device)
-    else:
-        available_uids = torch.tensor(avail_uids, dtype=torch.int64).to(self.device)
-
+    available_uids = candidate_uids
+    if len(candidate_uids) < k:
+        available_uids += random.sample([uid for uid in avail_uids if uid not in candidate_uids], k-len(candidate_uids))
 
-    uids = torch.tensor(random.sample(available_uids.tolist(), k), dtype=torch.int64)
+    uids = torch.tensor(random.sample(available_uids, k), dtype=torch.int64)
     return uids
 
 

openvalidators/neuron.py

@@ -34,6 +34,7 @@
 # Load gating models
 from openvalidators.reward import (
     Blacklist,
+    TaskValidator,
     NSFWRewardModel,
     OpenAssistantRewardModel,
     ReciprocateRewardModel,
@@ -62,6 +63,10 @@ def config(cls):
     def run(self):
         run(self)
 
+    subtensor: "bt.subtensor"
+    wallet: "bt.wallet"
+    metagraph: "bt.metagraph"
+
     def __init__(self):
         self.config = neuron.config()
         self.check_config(self.config)
@@ -84,12 +89,15 @@ def __init__(self):
         self.wallet = bt.wallet(config=self.config)
         self.wallet.create_if_non_existent()
         if not self.config.wallet._mock:
-            self.wallet.reregister(subtensor=self.subtensor, netuid=self.config.netuid)
+            if not self.subtensor.is_hotkey_registered_on_subnet(hotkey_ss58=self.wallet.hotkey.ss58_address, netuid=self.config.netuid):
+                raise Exception(f'Wallet not currently registered on netuid {self.config.netuid}, please first register wallet before running')
+
         bt.logging.debug(str(self.wallet))
 
         # Init metagraph.
         bt.logging.debug("loading", "metagraph")
-        self.metagraph = bt.metagraph(netuid=self.config.netuid, network=self.subtensor.network)
+        self.metagraph = bt.metagraph(netuid=self.config.netuid, network=self.subtensor.network, sync=False) # Make sure not to sync without passing subtensor
+        self.metagraph.sync(subtensor=self.subtensor) # Sync metagraph with subtensor.
         self.hotkeys = copy.deepcopy(self.metagraph.hotkeys)
         bt.logging.debug(str(self.metagraph))
 
@@ -180,24 +188,31 @@ def __init__(self):
 
                 bt.logging.error(message)
                 raise Exception(message)
-
+
+            # Masking functions
             self.blacklist = (
                 Blacklist() if not self.config.neuron.blacklist_off else MockRewardModel(RewardModelType.blacklist.value)
             )
+            task_validator = (
+                TaskValidator() if not self.config.neuron.task_validator_off
+                else MockRewardModel(RewardModelType.task_validator.value)
+            )
+            relevance_model = (
+                RelevanceRewardModel(device=self.device) if not self.config.neuron.relevance_off
+                else MockRewardModel(RewardModelType.relevance.value)
+            )
+            diversity_model = (
+                DiversityRewardModel(device=self.device) if not self.config.neuron.diversity_off
+                else MockRewardModel(RewardModelType.diversity.value)
+            )
+            nsfw_model = (
+                NSFWRewardModel(device=self.device) if not self.config.neuron.nsfw_off
+                else MockRewardModel(RewardModelType.nsfw.value)              
+            )
 
-            self.masking_functions = [
-                self.blacklist,
-                RelevanceRewardModel(device=self.device)
-                if not self.config.neuron.relevance_off
-                else MockRewardModel(RewardModelType.relevance.value),
-                DiversityRewardModel(device=self.device)
-                if not self.config.neuron.diversity_off
-                else MockRewardModel(RewardModelType.diversity.value),
-                NSFWRewardModel(device=self.device)
-                if not self.config.neuron.nsfw_off
-                else MockRewardModel(RewardModelType.nsfw.value),
-            ]
+            self.masking_functions = [self.blacklist, task_validator, relevance_model, diversity_model, nsfw_model]
             bt.logging.debug(str(self.reward_functions))
+            bt.logging.debug(str(self.masking_functions))
 
         # Init the event loop.
         self.loop = asyncio.get_event_loop()

openvalidators/prompts.py

@@ -124,7 +124,7 @@ def find_unique_tags(input_text: str):
 
 
 # Request a follow-up question given a preceding context.
-followup_request_template = "Ask one relevant and insightful question about the preceding context"
+followup_request_template = "Ask a single relevant and insightful question about the preceding context"
 
 # Scores a summary on a scale from 0 to 10, given a context.
 augment_scoring_template = """Score the relevance, succinctness, and quality of a summary given a context. The context is within <Context></Context> tags, and the question is within <Summary></Summary> tags. Give a score between 0 and 10 in the <Score></Score> tags, where 0 means the summary is irrelevant, and 10 means it's perfectly relevant and a good summary. Include a brief explanation for your score based solely on the context-summary relationship.
@@ -348,16 +348,16 @@ def find_unique_tags(input_text: str):
 
 def followup_prompt( base_text:str, i:int = 0) -> str:
     if i == 0:
-        return f"{base_text}\n\n{followup_request_template}\n"
+        return f"{base_text}\n\n{followup_request_template}\n. Do not try to return an answer or a summary:"
     else:
-        return f"{base_text}\n\n{followup_request_template} and previous questions\n"
+        return f"{base_text}\n\n{followup_request_template} and previous questions. Do not try to return an answer or a summary:\n"
 
 
 def answer_prompt( base_text:str, followup:str ) -> str:
-    return f"{base_text}\n Question:{followup}\n Answer the question step by step and explain your thoughts"
+    return f"{base_text}\n\nQuestion:{followup}\nAnswer the question step by step and explain your thoughts. Do not include questions or summaries in your answer."
 
 augment_request_template = "Summarize the preceding context"
 
 def augment_prompt( base_text:str ) -> str:
     random_level = random.randint(4, 8)
-    return f"{base_text}\n\n{augment_request_template} in {random_level} sentences.\n\n"
+    return f"{base_text}\n\n{augment_request_template} in {random_level} sentences. Do not try to create questions or answers for your summarization.\n\n"

openvalidators/reward/__init__.py

@@ -1,4 +1,5 @@
 from .blacklist import Blacklist
+from .task_validator import TaskValidator
 from .nsfw import NSFWRewardModel
 from .open_assistant import OpenAssistantRewardModel
 from .reciprocate import ReciprocateRewardModel
@@ -8,4 +9,4 @@
 from .dahoas import DahoasRewardModel
 from .diversity import DiversityRewardModel
 from .prompt import PromptRewardModel
-from .config import RewardModelType, DefaultRewardFrameworkConfig
+from .config import RewardModelType, DefaultRewardFrameworkConfig

openvalidators/reward/config.py

@@ -28,6 +28,7 @@ class RewardModelType(Enum):
     blacklist = 'blacklist_filter'
     nsfw = 'nsfw_filter'
     relevance = 'relevance_filter'
+    task_validator = 'task_validator_filter'
 
 
 @dataclass(frozen=True)

openvalidators/reward/diversity.py

@@ -56,6 +56,9 @@ def __init__( self, device: str ):
         self.tokenizer = AutoTokenizer.from_pretrained( DiversityRewardModel.diversity_model_path )
         self.model = AutoModel.from_pretrained( DiversityRewardModel.diversity_model_path ).to(self.device)
         self.reward_quantile = torch.tensor(0.1).to(self.device)
+        self.history_reward_bottom_k = 2
+        self.historic_embeddings = torch.tensor([]).to(self.device)
+        self.history_range = (500, 15500)
 
     def get_embeddings( self, sentences: List[str] ) -> "torch.FloatTensor":
         """Runs a forward pass through the model.
@@ -86,20 +89,64 @@ def get_embeddings( self, sentences: List[str] ) -> "torch.FloatTensor":
         sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
         return sentence_embeddings
 
-    def get_rewards( self, prompt: str, completions: List[str], name: str ) -> torch.FloatTensor:
+    def update_historic_embeddings( self, embeddings: torch.FloatTensor ):
+        def unique(embeddings):
+            unique_embeddings = [embeddings[0]]
+            last_emb = embeddings[0]
+            for emb in embeddings:
+                if not torch.all(torch.eq(emb, last_emb)):
+                    unique_embeddings.append(emb)
+                last_emb = emb
+            return torch.stack(unique_embeddings)
+
+        embeddings_unique = unique(embeddings)
+        historic_embeddings = torch.cat([self.historic_embeddings, embeddings_unique])
+        self.historic_embeddings = historic_embeddings[-self.history_range[1]:, :]
+
+    def get_historic_rewards( self, embeddings: torch.FloatTensor ) -> torch.FloatTensor:
+        def regularise( rewards ):
+            # sigmoid function that cutoff at 0.05 approximately
+            return 1/(1 + torch.exp(-1000 * rewards + 50))
+
+        # Return None if history size is too small 
+        if self.historic_embeddings.shape[0] < (self.history_range[0] + self.history_reward_bottom_k):
+            return None
+
+        # Calculate the pairwise cosine similarity.
+        similarity = pairwise_cosine_similarity( embeddings, self.historic_embeddings[self.history_range[0]:] )
+
+        # Reward to be at the bottom_k smallest of the 1 - similarity score.
+        rewards = torch.topk((1 - similarity), self.history_reward_bottom_k, largest = False)[0][:, -1]
+
+        return regularise(rewards) 
+
+    def get_batch_rewards( self, embeddings: torch.FloatTensor ) -> torch.FloatTensor:
+        # Calculate the pairwise cosine similarity.
+        similarity = pairwise_cosine_similarity( embeddings, embeddings )
 
+        # Reward to be at the 10% quantile of the 1 - similarity score.
+        rewards = (1 - similarity).quantile(self.reward_quantile, dim = 1 )
+
+        return rewards 
+
+    def get_rewards( self, prompt: str, completions: List[str], name: str ) -> torch.FloatTensor:
         # Check if completions are empty, return 0 if so
         if len(completions) == 0:
             return torch.tensor([]).to(self.device)
 
         # Get embeddings for all completions.
         embeddings = self.get_embeddings( completions )
 
-        # Calculate the pairwise cosine similarity.
-        similarity = pairwise_cosine_similarity( embeddings, embeddings )
+        # Get batch rewards.
+        batch_rewards = self.get_batch_rewards(embeddings)
 
-        # Reward to be at the 10% quantile of the 1 - similarity score.
-        rewards = (1 - similarity).quantile(self.reward_quantile, dim = 1 )
+        # get historic rewards.
+        historic_rewards = self.get_historic_rewards(embeddings)
 
+        self.update_historic_embeddings(embeddings)
+
         # Return all
-        return rewards
+        if historic_rewards != None:
+            return batch_rewards * historic_rewards
+        else:
+            return batch_rewards

openvalidators/reward/reward.py

@@ -35,7 +35,7 @@ def __init__(self) -> None:
         self.count = 0
         self.mean = 0.0
         self.var = 0.0
-        self.count_limit = 1000
+        self.count_limit = 3000
 
     def normalize_rewards( self, rewards: torch.FloatTensor ) -> torch.FloatTensor:
         """