virtual_gpu_setup/virtual_gpu/ai.py

"""
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!")