""" Main script for running OpenAI 20B model using Virtual GPU infrastructure """ import os import json from typing import Dict, List, Optional, Union, Any from pathlib import Path from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig from virtual_gpu_driver.src.driver_api import VirtualGPUDriver from virtual_gpu_driver.src.hal.hal import HardwareAbstractionLayer from virtual_gpu_driver.src.memory.memory_manager import MemoryManager from virtual_gpu_driver.src.memory_pool import MemoryPool from helium.pipeline.unified_controller import UnifiedPipelineController from helium.core.probability import ProbabilityCalculator from helium.core.pipeline import Pipeline from helium.tokenizer import HeliumTokenizer import array # Initialize HuggingFace token from environment HF_TOKEN = os.getenv("HF_TOKEN") class VGPUTensor: """VGPU tensor class""" def __init__(self, data, shape=None, dtype='float32'): self.driver = VirtualGPUDriver() self.shape = shape or self._infer_shape(data) self.dtype = dtype # Get memory from pool self.memory_pool = MemoryPool() self.addr = self.memory_pool.allocate( self._calculate_size(self.shape, dtype) ) # Transfer data if isinstance(data, (list, array.array)): self.hal.write_memory(self.addr, array.array(self._get_typecode(dtype), data)) def _infer_shape(self, data): if isinstance(data, list): shape = [len(data)] if isinstance(data[0], list): shape.extend(self._infer_shape(data[0])) return tuple(shape) return (1,) def _calculate_size(self, shape, dtype): total_elements = 1 for dim in shape: total_elements *= dim return total_elements * self._get_dtype_size(dtype) def _get_dtype_size(self, dtype): sizes = { 'float32': 4, 'float64': 8, 'int32': 4, 'int64': 8 } return sizes.get(dtype, 4) def _get_typecode(self, dtype): typecodes = { 'float32': 'f', 'float64': 'd', 'int32': 'l', 'int64': 'q' } return typecodes.get(dtype, 'f') class VGPUModule: """Base class for VGPU neural network modules""" def __init__(self): self.driver = VirtualGPUDriver() self.parameters = {} def register_parameter(self, name: str, tensor: VGPUTensor): self.parameters[name] = tensor class VGPUTransformerBlock(VGPUModule): """Transformer block implementation using VGPU""" def __init__(self, config): super().__init__() self.config = config self.attention = HeliumMultiHeadAttention(config) self.mlp = HeliumMLP(config) self.ln_1 = HeliumLayerNorm(config.hidden_size) self.ln_2 = HeliumLayerNorm(config.hidden_size) def forward(self, hidden_states, attention_mask=None): attn_output = self.attention(self.ln_1(hidden_states), attention_mask) hidden_states = hidden_states + attn_output mlp_output = self.mlp(self.ln_2(hidden_states)) return hidden_states + mlp_output class HeliumGPT(HeliumModule): """OpenAI GPT model implementation using Helium""" def __init__(self, config): super().__init__() self.config = config self.transformer_blocks = [ HeliumTransformerBlock(config) for _ in range(config.num_hidden_layers) ] self.ln_f = HeliumLayerNorm(config.hidden_size) def forward(self, input_ids, attention_mask=None): hidden_states = self.get_embeddings(input_ids) for block in self.transformer_blocks: hidden_states = block(hidden_states, attention_mask) hidden_states = self.ln_f(hidden_states) logits = self.get_logits(hidden_states) return logits def get_embeddings(self, input_ids): # Convert input_ids to embeddings using embedding table pass def get_logits(self, hidden_states): # Convert final hidden states to logits pass def load_openai_20b(): """Load OpenAI 20B model and convert to Helium format""" # Initialize Helium infrastructure controller = UnifiedPipelineController() # Model ID for OpenAI's open source 20B model model_id = "openai/gpt-oss-20b" # Load model config config = AutoConfig.from_pretrained(model_id) # Create VGPU model model = VGPUGPT(config) # Load weights and convert to VGPU format torch_model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto" ) # Convert weights to Helium tensors for name, param in torch_model.named_parameters(): vgpu_tensor = VGPUTensor(param.detach().numpy()) model.register_parameter(name, vgpu_tensor) return model, config def generate_text( model: HeliumGPT, tokenizer: HeliumTokenizer, prompt: str, max_length: int = 100, temperature: float = 0.7, top_k: int = 50, top_p: float = 0.9 ) -> str: """ Generate text using Helium infrastructure Args: model: Helium model tokenizer: Helium tokenizer prompt: Input prompt max_length: Maximum generation length temperature: Sampling temperature top_k: Top-k sampling parameter top_p: Nucleus sampling parameter Returns: Generated text """ # Initialize components controller = UnifiedPipelineController() prob_calc = ProbabilityCalculator() # Encode prompt input_ids = tokenizer.encode(prompt) # Create attention mask attention_mask = [1] * len(input_ids) # Generate tokens for _ in range(max_length): # Forward pass logits = model.forward(input_ids, attention_mask) # Get next token next_token_logits = logits[:, -1, :] # Apply temperature and sampling probs = prob_calc.compute_probabilities(next_token_logits, temperature) next_token = prob_calc.sample_from_probs(probs, top_k=top_k, top_p=top_p) # Append to sequence input_ids.append(next_token) attention_mask.append(1) # Check for end of sequence if next_token == tokenizer.special_tokens["[SEP]"]: break # Decode and return text return tokenizer.decode(input_ids) if __name__ == "__main__": # Load model print("Loading OpenAI 20B model...") model, config = load_openai_20b() # Initialize tokenizer tokenizer = HeliumTokenizer() tokenizer.load_vocabulary("path/to/vocab.json") # Example generation prompt = "Once upon a time" print(f"\nPrompt: {prompt}") generated_text = generate_text( model, tokenizer, prompt, max_length=100, temperature=0.7, top_k=50, top_p=0.9 ) print(f"\nGenerated text:\n{generated_text}")