import numpy as np from collections import OrderedDict from typing import Dict, Any, Optional, Tuple, Union from dataclasses import dataclass import time @dataclass class MemoryBlock: """Represents a block of memory in the symbolic VRAM.""" address: int size: int data: Optional[Any] allocated_time: float last_accessed: float class Framebuffer: """Represents a 2D drawing surface in VRAM.""" def __init__(self, width: int, height: int, channels: int = 3, dtype=np.uint8): self.width = width self.height = height self.channels = channels self.dtype = dtype # Create the pixel buffer symbolically to avoid large allocations # The actual pixel data will be managed by the MemoryManager self.pixel_buffer_address: Optional[int] = None self.pixel_buffer_size: int = width * height * channels * np.dtype(dtype).itemsize self.pixel_buffer = np.zeros((height, width, channels), dtype=dtype) self.vram_address: Optional[int] = None # This is the address in the MemoryManager def resize(self, new_width: int, new_height: int) -> None: # No actual data to resize, just update symbolic size self.width = new_width self.height = new_height self.pixel_buffer_size = new_width * new_height * self.channels * np.dtype(self.dtype).itemsize def clear(self, color: Tuple[int, int, int]) -> None: self.pixel_buffer[:, :] = color def get_pixel(self, x: int, y: int) -> np.ndarray: if 0 <= x < self.width and 0 <= y < self.height: return self.pixel_buffer[y, x] return np.zeros(self.channels, dtype=self.dtype) def set_pixel(self, x: int, y: int, color: Tuple[int, int, int]) -> None: if 0 <= x < self.width and 0 <= y < self.height: self.pixel_buffer[y, x] = color[:self.channels] def get_memory_usage(self) -> int: """Get the memory usage of this framebuffer in bytes.""" return self.pixel_buffer_size class MemoryManager: """Manages the symbolic 500GB GDDR7 memory space.""" def __init__(self, total_memory_gb: int = 500, block_size_kb: int = 4): self.total_memory_bytes = total_memory_gb * 1024 * 1024 * 1024 # 500GB self.block_size_bytes = block_size_kb * 1024 # 4KB blocks self.total_blocks = self.total_memory_bytes // self.block_size_bytes # Symbolic memory space - only allocated blocks are stored self.memory_blocks: Dict[int, MemoryBlock] = {} # Free block tracking - use a list of free block ranges instead of a set of all blocks self.free_block_ranges = [(0, self.total_blocks - 1)] # (start_block_id, end_block_id) self.allocated_blocks = set() # Still track allocated blocks for quick lookup # Address allocation counter self.next_address = 0 def allocate_block(self, size_bytes: int) -> Optional[int]: """Allocate a block of memory and return its address.""" blocks_needed = (size_bytes + self.block_size_bytes - 1) // self.block_size_bytes # Find a suitable contiguous block range for i, (start, end) in enumerate(self.free_block_ranges): available_blocks = end - start + 1 if available_blocks >= blocks_needed: # Found a suitable range base_block_id = start # Update free_block_ranges new_start = start + blocks_needed if new_start <= end: self.free_block_ranges[i] = (new_start, end) else: self.free_block_ranges.pop(i) # Add to allocated_blocks for j in range(blocks_needed): self.allocated_blocks.add(base_block_id + j) # Create memory block base_address = base_block_id * self.block_size_bytes memory_block = MemoryBlock( address=base_address, size=size_bytes, data=bytearray(size_bytes), # Allocate actual bytearray for data allocated_time=time.time(), last_accessed=time.time() ) self.memory_blocks[base_address] = memory_block return base_address return None # Out of memory def deallocate_block(self, address: int) -> bool: """Deallocate a block of memory.""" if address in self.memory_blocks: memory_block = self.memory_blocks[address] blocks_to_free = (memory_block.size + self.block_size_bytes - 1) // self.block_size_bytes base_block_id = address // self.block_size_bytes for i in range(blocks_to_free): block_id = base_block_id + i if block_id in self.allocated_blocks: self.allocated_blocks.remove(block_id) # Add back to free_block_ranges (simple merge for now) self.free_block_ranges.append((block_id, block_id)) self.free_block_ranges.sort() # Keep sorted for efficient merging del self.memory_blocks[address] return True return False def read_data(self, address: int, size: int) -> Optional[np.ndarray]: """Read data from memory.""" if address in self.memory_blocks: memory_block = self.memory_blocks[address] if memory_block.data is not None and size <= memory_block.size: return np.frombuffer(memory_block.data[:size], dtype=np.uint8) # Return as numpy array return None def write_data(self, address: int, data: Union[np.ndarray, bytes]) -> bool: """Write data to memory.""" if address in self.memory_blocks: memory_block = self.memory_blocks[address] if memory_block.data is not None: if isinstance(data, np.ndarray): data_bytes = data.tobytes() elif isinstance(data, bytes): data_bytes = data else: raise TypeError("Data must be a NumPy array or bytes.") if len(data_bytes) <= memory_block.size: memory_block.data[:len(data_bytes)] = data_bytes return True return False def get_memory_stats(self) -> Dict[str, Any]: """Get memory usage statistics.""" allocated_bytes = sum(block.size for block in self.memory_blocks.values()) free_bytes = self.total_memory_bytes - allocated_bytes return { "total_memory_gb": self.total_memory_bytes / (1024**3), "allocated_bytes": allocated_bytes, "free_bytes": free_bytes, "allocated_blocks_count": len(self.allocated_blocks), "free_block_ranges_count": len(self.free_block_ranges), "utilization_percent": (allocated_bytes / self.total_memory_bytes) * 100 if self.total_memory_bytes > 0 else 0 } class VRAM: """ Main VRAM class that provides the interface for the 500GB GDDR7 memory. This class combines the MemoryManager for low-level memory operations with higher-level abstractions like Framebuffers. """ def __init__(self, memory_size_gb: int = 500): self.memory_manager = MemoryManager(memory_size_gb) # Cache for frequently accessed data (simulates L1/L2 cache) self.cache_size = 1000 # Number of cache entries self.cache = OrderedDict() # Framebuffer registry self.framebuffers: Dict[str, Framebuffer] = {} self.framebuffer_counter = 0 # Texture registry self.textures: Dict[str, np.ndarray] = {} self.texture_counter = 0 def create_framebuffer(self, width: int, height: int, channels: int = 3, name: Optional[str] = None) -> str: """Create a new framebuffer and return its ID.""" if name is None: name = f"framebuffer_{self.framebuffer_counter}" self.framebuffer_counter += 1 framebuffer = Framebuffer(width, height, channels) # Allocate memory for the framebuffer memory_size = framebuffer.get_memory_usage() address = self.memory_manager.allocate_block(memory_size) if address is not None: framebuffer.vram_address = address self.framebuffers[name] = framebuffer return name else: raise MemoryError("Failed to allocate memory for framebuffer") def get_framebuffer(self, name: str) -> Optional[Framebuffer]: """Get a framebuffer by name.""" return self.framebuffers.get(name) def delete_framebuffer(self, name: str) -> bool: """Delete a framebuffer and free its memory.""" if name in self.framebuffers: framebuffer = self.framebuffers[name] if framebuffer.vram_address is not None: self.memory_manager.deallocate_block(framebuffer.vram_address) del self.framebuffers[name] return True return False def load_texture(self, texture_data: Union[np.ndarray, bytes], name: Optional[str] = None) -> str: """Load texture data into VRAM and return its ID.""" if name is None: name = f"texture_{self.texture_counter}" self.texture_counter += 1 size_bytes = 0 if isinstance(texture_data, np.ndarray): size_bytes = texture_data.nbytes elif isinstance(texture_data, bytes): size_bytes = len(texture_data) else: raise TypeError("Texture data must be a NumPy array or bytes.") # Allocate memory for the texture address = self.memory_manager.allocate_block(size_bytes) if address is not None: self.memory_manager.write_data(address, texture_data) # Write actual data self.textures[name] = texture_data # Store actual data for reference return name else: raise MemoryError("Failed to allocate memory for texture") def get_texture(self, name: str) -> Optional[np.ndarray]: """Get texture data by name.""" return self.textures.get(name) def cache_read(self, address: int, size: int) -> Optional[np.ndarray]: """Read data with caching support.""" cache_key = (address, size) # Check cache first if cache_key in self.cache: # Move to end (most recently used) data = self.cache.pop(cache_key) self.cache[cache_key] = data return data.copy() # Read from memory data = self.memory_manager.read_data(address, size) if data is not None: # Add to cache if len(self.cache) >= self.cache_size: # Remove least recently used item self.cache.popitem(last=False) self.cache[cache_key] = data.copy() return data def transfer_from_ram(self, name: str, data: Union[np.ndarray, bytes], delay_ms: float = 0.0) -> Optional[str]: """Transfer a block of data from RAM to VRAM.""" if isinstance(data, np.ndarray): size_bytes = data.nbytes data_to_store = data.flatten() elif isinstance(data, bytes): size_bytes = len(data) data_to_store = np.frombuffer(data, dtype=np.uint8) else: raise TypeError("Data must be a NumPy array or bytes.") # Simulate delay if delay_ms > 0: time.sleep(delay_ms / 1000.0) # Allocate memory in VRAM address = self.memory_manager.allocate_block(size_bytes) if address is not None: # Store data in VRAM self.memory_manager.write_data(address, data_to_store) # Register the transferred data as a texture/buffer in VRAM # For simplicity, we\"ll register it as a texture for now texture_id = f"ram_transfer_{self.texture_counter}" self.texture_counter += 1 self.textures[texture_id] = data # Store actual data for reference print(f"Transferred {size_bytes} bytes from RAM to VRAM at address {address} as {texture_id}") return texture_id else: print(f"Failed to transfer {size_bytes} bytes from RAM to VRAM: Out of VRAM memory.") return None def get_stats(self) -> Dict[str, Any]: """Get comprehensive VRAM statistics.""" memory_stats = self.memory_manager.get_memory_stats() framebuffer_memory = sum(fb.get_memory_usage() for fb in self.framebuffers.values()) texture_memory = sum(tex.nbytes for tex in self.textures.values()) return { **memory_stats, "framebuffers_count": len(self.framebuffers), "textures_count": len(self.textures), "framebuffer_memory_bytes": framebuffer_memory, "texture_memory_bytes": texture_memory, "cache_entries": len(self.cache), "cache_hit_ratio": 0.0 # TODO: Implement cache hit tracking } if __name__ == "__main__": # Test the VRAM module vram = VRAM(memory_size_gb=1) # Use 1GB for testing # Create a framebuffer fb_id = vram.create_framebuffer(1920, 1080, 3) print(f"Created framebuffer: {fb_id}") # Get the framebuffer and modify it fb = vram.get_framebuffer(fb_id) if fb: fb.clear((255, 0, 0)) # Clear to red fb.set_pixel(100, 100, (0, 255, 0)) # Set a green pixel print(f"Framebuffer size: {fb.width}x{fb.height}") print(f"Pixel at (100, 100): {fb.get_pixel(100, 100)}") # Load a test texture test_texture = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8) tex_id = vram.load_texture(test_texture) print(f"Loaded texture: {tex_id}") # Test transfer_from_ram ram_data = b"\x01\x02\x03\x04\x05\x06\x07\x08" transferred_id = vram.transfer_from_ram("test_ram_data", ram_data, delay_ms=10) print(f"Transferred RAM data ID: {transferred_id}") # Print statistics stats = vram.get_stats() print(f"VRAM Stats: {stats}")