| import torch
|
| from .decent_torch import DecentModel
|
| from .openpeer import OpenPeerClient
|
| from .grammar import LonScriptGrammar
|
| from .modeling_openpeer import OpenPeerLLM
|
| from .configuration_openpeer import OpenPeerConfig
|
| from .tokenization_openpeer import OpenPeerTokenizer
|
| import asyncio
|
| from typing import Dict, Any, Optional
|
|
|
| class DecentralizedLLM(DecentModel):
|
| def __init__(self, network_url: str = "ws://localhost:8000"):
|
| super().__init__()
|
|
|
| self.config = OpenPeerConfig()
|
| self.model = OpenPeerLLM(self.config)
|
| self.tokenizer = OpenPeerTokenizer()
|
| self.peer_client = OpenPeerClient(network_url)
|
| self.grammar = LonScriptGrammar()
|
| self._ensure_model_on_device()
|
|
|
| def _ensure_model_on_device(self):
|
| """Ensure model is on the correct device"""
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| self.model = self.model.to(device)
|
|
|
| def forward(self, input_text: str) -> str:
|
|
|
| inputs = self.tokenizer(input_text, return_tensors="pt")
|
| inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
|
|
|
|
|
| with torch.no_grad():
|
| outputs = self.model(**inputs)
|
| logits = outputs["logits"]
|
|
|
|
|
| next_token_logits = logits[:, -1, :]
|
| next_tokens = torch.argmax(next_token_logits, dim=-1)
|
|
|
| generated_ids = [inputs["input_ids"][0].tolist()]
|
| for _ in range(100):
|
| curr_input = torch.tensor([generated_ids[-1]], device=self.model.device)
|
| with torch.no_grad():
|
| outputs = self.model(curr_input)
|
| next_token_logits = outputs["logits"][:, -1, :]
|
| next_token = torch.argmax(next_token_logits, dim=-1).item()
|
|
|
| generated_ids.append([next_token])
|
|
|
| if next_token == self.tokenizer.eos_token_id:
|
| break
|
|
|
|
|
| decoded_output = self.tokenizer.decode(torch.tensor(generated_ids).flatten(), skip_special_tokens=True)
|
| return decoded_output
|
| from .grammar import LonScriptGrammar
|
| from .modeling_openpeer import OpenPeerLLM
|
| from .configuration_openpeer import OpenPeerConfig
|
| from .tokenization_openpeer import OpenPeerTokenizer
|
| import asyncio
|
| from typing import Dict, Any, Optional
|
|
|
| class DecentralizedLLM(DecentModel):
|
| def __init__(self, network_url: str = "ws://localhost:8000"):
|
| super().__init__()
|
|
|
| self.config = OpenPeerConfig()
|
| self.model = OpenPeerLLM(self.config)
|
| self.tokenizer = OpenPeerTokenizer()
|
| self.peer_client = OpenPeerClient(network_url)
|
| self.grammar = LonScriptGrammar()
|
| self._ensure_model_on_device()
|
|
|
| def _ensure_model_on_device(self):
|
| """Ensure model is on the correct device"""
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
| self.model = self.model.to(device)
|
|
|
| async def connect_to_network(self):
|
| """Connect to the peer network"""
|
| await self.peer_client.connect(self.peer_id)
|
| asyncio.create_task(self._handle_peer_updates())
|
|
|
| async def _handle_peer_updates(self):
|
| """Handle incoming updates from peers"""
|
| async for update in self.peer_client.receive_updates():
|
| if update["type"] == "model_update":
|
| await self._process_model_update(update)
|
|
|
| async def _process_model_update(self, update: Dict[str, Any]):
|
| """Process received model updates"""
|
| state_dict = {k: torch.tensor(v) for k, v in update["state"].items()}
|
| self.state_updates[update["peer_id"]] = state_dict
|
| self.aggregate_states()
|
|
|
| def forward(self, input_text: str) -> str:
|
| """Generate response for input text"""
|
|
|
| inputs = self.tokenizer(input_text, return_tensors="pt")
|
| inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
|
|
|
|
|
| with torch.no_grad():
|
| outputs = self.model.generate(
|
| **inputs,
|
| max_length=100,
|
| num_return_sequences=1,
|
| pad_token_id=self.tokenizer.eos_token_id
|
| )
|
|
|
|
|
| decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
|
| return decoded_output
|
|
|
| async def train_step(self, batch: Dict[str, torch.Tensor]):
|
| """Perform a training step and share updates with peers"""
|
|
|
| outputs = self.model(**batch)
|
| loss = outputs.loss
|
|
|
|
|
| loss.backward()
|
|
|
|
|
|
|
|
|
|
|
| await self.peer_client.send_model_update(self.model.state_dict())
|
|
|
| def reason(self, context: str, query: str) -> str:
|
| """Implement deep reasoning capabilities with grammar enhancement"""
|
|
|
| prompt = f"Context: {context}\nQuery: {query}\nReasoned response:"
|
|
|
|
|
| initial_response = self.forward(prompt)
|
|
|
|
|
| enhanced_response = self.grammar.apply_grammar_rules(initial_response)
|
|
|
| return enhanced_response |
