""" AI Accelerator Module This module implements AI-specific operations, treating the vGPU as a tensor engine and leveraging the simulated parallelism of 50,000 cores and 800 SMs. """ import numpy as np import time from typing import Dict, Any, Optional, Tuple, Union, List from enum import Enum class VectorOperation(Enum): """Enumeration of supported vector operations.""" ADD = "add" SUBTRACT = "subtract" MULTIPLY = "multiply" DIVIDE = "divide" DOT_PRODUCT = "dot_product" CROSS_PRODUCT = "cross_product" NORMALIZE = "normalize" MAGNITUDE = "magnitude" class AIAccelerator: """ AI Accelerator that simulates GPU-based AI computations. This class leverages NumPy's optimized operations to simulate the parallel processing capabilities of the vGPU for AI workloads. """ def __init__(self, vram=None, num_sms: int = 800, cores_per_sm: int = 62): self.vram = vram self.num_sms = num_sms self.cores_per_sm = cores_per_sm self.total_cores = num_sms * cores_per_sm # AI operation statistics self.operations_performed = 0 self.total_compute_time = 0.0 self.flops_performed = 0 # Floating point operations # Matrix registry for storing matrices in VRAM self.matrix_registry: Dict[str, str] = {} # matrix_id -> vram_address self.matrix_counter = 0 def set_vram(self, vram): """Set the VRAM reference.""" self.vram = vram def allocate_matrix(self, shape: Tuple[int, ...], dtype=np.float32, name: Optional[str] = None) -> str: """Allocate a matrix in VRAM and return its ID.""" if not self.vram: raise RuntimeError("VRAM not available") if name is None: name = f"matrix_{self.matrix_counter}" self.matrix_counter += 1 # Create matrix data matrix_data = np.zeros(shape, dtype=dtype) # Store in VRAM as a texture (reusing texture storage mechanism) matrix_id = self.vram.load_texture(matrix_data, name) self.matrix_registry[name] = matrix_id return name def load_matrix(self, matrix_data: np.ndarray, name: Optional[str] = None) -> str: """Load matrix data into VRAM and return its ID.""" if not self.vram: raise RuntimeError("VRAM not available") if name is None: name = f"matrix_{self.matrix_counter}" self.matrix_counter += 1 # Store in VRAM matrix_id = self.vram.load_texture(matrix_data, name) self.matrix_registry[name] = matrix_id return name def get_matrix(self, matrix_id: str) -> Optional[np.ndarray]: """Retrieve matrix data from VRAM.""" if not self.vram or matrix_id not in self.matrix_registry: return None vram_id = self.matrix_registry[matrix_id] return self.vram.get_texture(vram_id) def matrix_multiply(self, matrix_a_id: str, matrix_b_id: str, result_id: Optional[str] = None) -> Optional[str]: """Perform matrix multiplication using simulated GPU parallelism.""" start_time = time.time() # Retrieve matrices from VRAM matrix_a = self.get_matrix(matrix_a_id) matrix_b = self.get_matrix(matrix_b_id) if matrix_a is None or matrix_b is None: print(f"Error: Could not retrieve matrices {matrix_a_id} or {matrix_b_id}") return None try: # Check if matrices can be multiplied if matrix_a.shape[-1] != matrix_b.shape[0]: print(f"Error: Matrix dimensions incompatible for multiplication: " f"{matrix_a.shape} x {matrix_b.shape}") return None # Simulate parallel processing by breaking down the operation # In a real GPU, this would be distributed across SMs and cores result = self._simulate_parallel_matmul(matrix_a, matrix_b) # Store result in VRAM if result_id is None: result_id = f"result_{self.matrix_counter}" self.matrix_counter += 1 result_matrix_id = self.load_matrix(result, result_id) # Update statistics compute_time = time.time() - start_time self.total_compute_time += compute_time self.operations_performed += 1 # Calculate FLOPs (2 * M * N * K for matrix multiplication) m, k = matrix_a.shape k2, n = matrix_b.shape flops = 2 * m * n * k self.flops_performed += flops print(f"Matrix multiplication completed: {matrix_a.shape} x {matrix_b.shape} " f"= {result.shape} in {compute_time:.4f}s") print(f"Simulated {flops:,} FLOPs across {self.total_cores} cores") return result_matrix_id except Exception as e: print(f"Error in matrix multiplication: {e}") return None def _simulate_parallel_matmul(self, matrix_a: np.ndarray, matrix_b: np.ndarray) -> np.ndarray: """Simulate parallel matrix multiplication across SMs.""" # Use NumPy's optimized matrix multiplication # In a real implementation, this would be broken down into blocks # and distributed across the simulated SMs # For demonstration, we can show how the work would be distributed m, k = matrix_a.shape k2, n = matrix_b.shape # Calculate work distribution total_output_elements = m * n elements_per_sm = max(1, total_output_elements // self.num_sms) print(f"Distributing {total_output_elements:,} output elements across " f"{self.num_sms} SMs ({elements_per_sm} elements per SM)") # Perform the actual computation using NumPy result = np.dot(matrix_a, matrix_b) return result def vector_operation(self, operation: VectorOperation, vector_a_id: str, vector_b_id: Optional[str] = None, result_id: Optional[str] = None) -> Optional[str]: """Perform vector operations using simulated GPU parallelism.""" start_time = time.time() # Retrieve vectors from VRAM vector_a = self.get_matrix(vector_a_id) if vector_a is None: print(f"Error: Could not retrieve vector {vector_a_id}") return None vector_b = None if vector_b_id: vector_b = self.get_matrix(vector_b_id) if vector_b is None: print(f"Error: Could not retrieve vector {vector_b_id}") return None try: result = None flops = 0 if operation == VectorOperation.ADD: if vector_b is None: raise ValueError("Vector B required for addition") result = vector_a + vector_b flops = vector_a.size elif operation == VectorOperation.SUBTRACT: if vector_b is None: raise ValueError("Vector B required for subtraction") result = vector_a - vector_b flops = vector_a.size elif operation == VectorOperation.MULTIPLY: if vector_b is None: raise ValueError("Vector B required for multiplication") result = vector_a * vector_b flops = vector_a.size elif operation == VectorOperation.DIVIDE: if vector_b is None: raise ValueError("Vector B required for division") result = vector_a / vector_b flops = vector_a.size elif operation == VectorOperation.DOT_PRODUCT: if vector_b is None: raise ValueError("Vector B required for dot product") result = np.dot(vector_a.flatten(), vector_b.flatten()) flops = 2 * vector_a.size elif operation == VectorOperation.CROSS_PRODUCT: if vector_b is None: raise ValueError("Vector B required for cross product") result = np.cross(vector_a, vector_b) flops = 6 # Approximate for 3D cross product elif operation == VectorOperation.NORMALIZE: magnitude = np.linalg.norm(vector_a) result = vector_a / magnitude if magnitude > 0 else vector_a flops = vector_a.size * 2 # Division + magnitude calculation elif operation == VectorOperation.MAGNITUDE: result = np.array([np.linalg.norm(vector_a)]) flops = vector_a.size * 2 # Squares and sum else: raise ValueError(f"Unsupported vector operation: {operation}") # Store result in VRAM if result_id is None: result_id = f"vector_result_{self.matrix_counter}" self.matrix_counter += 1 result_vector_id = self.load_matrix(result, result_id) # Update statistics compute_time = time.time() - start_time self.total_compute_time += compute_time self.operations_performed += 1 self.flops_performed += flops print(f"Vector operation {operation.value} completed in {compute_time:.4f}s") return result_vector_id except Exception as e: print(f"Error in vector operation {operation.value}: {e}") return None def convolution_2d(self, input_id: str, kernel_id: str, stride: int = 1, padding: int = 0, result_id: Optional[str] = None) -> Optional[str]: """Perform 2D convolution operation.""" start_time = time.time() # Retrieve input and kernel from VRAM input_data = self.get_matrix(input_id) kernel = self.get_matrix(kernel_id) if input_data is None or kernel is None: print(f"Error: Could not retrieve input or kernel") return None try: # Simple 2D convolution implementation # In a real GPU implementation, this would be highly optimized # and distributed across many cores if len(input_data.shape) == 2: input_h, input_w = input_data.shape channels = 1 else: input_h, input_w, channels = input_data.shape kernel_h, kernel_w = kernel.shape[:2] # Calculate output dimensions output_h = (input_h + 2 * padding - kernel_h) // stride + 1 output_w = (input_w + 2 * padding - kernel_w) // stride + 1 # Initialize output if channels == 1: output = np.zeros((output_h, output_w)) else: output = np.zeros((output_h, output_w, channels)) # Pad input if necessary if padding > 0: if channels == 1: padded_input = np.pad(input_data, padding, mode='constant') else: padded_input = np.pad(input_data, ((padding, padding), (padding, padding), (0, 0)), mode='constant') else: padded_input = input_data # Perform convolution flops = 0 for y in range(0, output_h): for x in range(0, output_w): y_start = y * stride x_start = x * stride if channels == 1: patch = padded_input[y_start:y_start+kernel_h, x_start:x_start+kernel_w] output[y, x] = np.sum(patch * kernel) flops += kernel_h * kernel_w * 2 # Multiply and add else: for c in range(channels): patch = padded_input[y_start:y_start+kernel_h, x_start:x_start+kernel_w, c] output[y, x, c] = np.sum(patch * kernel) flops += kernel_h * kernel_w * 2 # Store result in VRAM if result_id is None: result_id = f"conv_result_{self.matrix_counter}" self.matrix_counter += 1 result_conv_id = self.load_matrix(output, result_id) # Update statistics compute_time = time.time() - start_time self.total_compute_time += compute_time self.operations_performed += 1 self.flops_performed += flops print(f"2D Convolution completed: {input_data.shape} * {kernel.shape} " f"= {output.shape} in {compute_time:.4f}s") print(f"Simulated {flops:,} FLOPs") return result_conv_id except Exception as e: print(f"Error in 2D convolution: {e}") return None def get_stats(self) -> Dict[str, Any]: """Get AI accelerator statistics.""" avg_compute_time = self.total_compute_time / max(1, self.operations_performed) flops_per_second = self.flops_performed / max(0.001, self.total_compute_time) return { "operations_performed": self.operations_performed, "total_compute_time": self.total_compute_time, "avg_compute_time": avg_compute_time, "flops_performed": self.flops_performed, "flops_per_second": flops_per_second, "matrices_in_memory": len(self.matrix_registry), "simulated_cores": self.total_cores, "simulated_sms": self.num_sms } def reset_stats(self) -> None: """Reset AI accelerator statistics.""" self.operations_performed = 0 self.total_compute_time = 0.0 self.flops_performed = 0 if __name__ == "__main__": # Test the AI accelerator from vram import VRAM # Create VRAM and AI accelerator vram = VRAM(memory_size_gb=1) ai = AIAccelerator(vram) print("Testing AI Accelerator...") # Test matrix operations # Create test matrices matrix_a = np.random.rand(100, 50).astype(np.float32) matrix_b = np.random.rand(50, 75).astype(np.float32) # Load matrices into VRAM a_id = ai.load_matrix(matrix_a, "test_matrix_a") b_id = ai.load_matrix(matrix_b, "test_matrix_b") # Perform matrix multiplication result_id = ai.matrix_multiply(a_id, b_id, "multiplication_result") if result_id: result = ai.get_matrix(result_id) print(f"Matrix multiplication result shape: {result.shape}") # Verify result expected = np.dot(matrix_a, matrix_b) if np.allclose(result, expected): print("Matrix multiplication result is correct!") else: print("Matrix multiplication result is incorrect!") # Test vector operations vector_a = np.random.rand(1000).astype(np.float32) vector_b = np.random.rand(1000).astype(np.float32) va_id = ai.load_matrix(vector_a, "vector_a") vb_id = ai.load_matrix(vector_b, "vector_b") # Test vector addition add_result_id = ai.vector_operation(VectorOperation.ADD, va_id, vb_id) if add_result_id: add_result = ai.get_matrix(add_result_id) expected_add = vector_a + vector_b if np.allclose(add_result, expected_add): print("Vector addition result is correct!") # Test dot product dot_result_id = ai.vector_operation(VectorOperation.DOT_PRODUCT, va_id, vb_id) if dot_result_id: dot_result = ai.get_matrix(dot_result_id) expected_dot = np.dot(vector_a, vector_b) if np.allclose(dot_result[0], expected_dot): print("Dot product result is correct!") # Test 2D convolution input_image = np.random.rand(32, 32).astype(np.float32) kernel = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]], dtype=np.float32) # Sobel edge detector img_id = ai.load_matrix(input_image, "test_image") kernel_id = ai.load_matrix(kernel, "sobel_kernel") conv_result_id = ai.convolution_2d(img_id, kernel_id) if conv_result_id: conv_result = ai.get_matrix(conv_result_id) print(f"Convolution result shape: {conv_result.shape}") # Print final statistics stats = ai.get_stats() print(f"AI Accelerator stats: {stats}") print("AI Accelerator test completed!")