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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__()
# Initialize our custom LLM
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:
# Tokenize input
inputs = self.tokenizer(input_text, return_tensors="pt")
inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
# Generate response using our custom LLM
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs["logits"]
# Get next token predictions
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): # max length
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
# Decode and return results
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__()
# Initialize our custom LLM
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"""
# Tokenize input
inputs = self.tokenizer(input_text, return_tensors="pt")
inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
# Generate response
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=100,
num_return_sequences=1,
pad_token_id=self.tokenizer.eos_token_id
)
# Decode and return results
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"""
# Forward pass
outputs = self.model(**batch)
loss = outputs.loss
# Backward pass
loss.backward()
# Optimizer step would go here
# self.optimizer.step()
# Share updated model state with peers
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"""
# Combine context and query
prompt = f"Context: {context}\nQuery: {query}\nReasoned response:"
# Generate initial response
initial_response = self.forward(prompt)
# Apply grammar rules for enhanced understanding
enhanced_response = self.grammar.apply_grammar_rules(initial_response)
return enhanced_response |