| import os
|
| import pathlib
|
| from typing import Dict, Any, List, Optional, Tuple
|
| import numpy as np
|
|
|
| from gpu_chip import GPUChip, OpticalInterconnect
|
| from streaming_multiprocessor import StreamingMultiprocessor
|
| from virtual_vram import VirtualVRAM
|
| from tensor_core import TensorCore
|
| from core import AdvancedCore
|
| from multicore import MultiCoreSystem
|
|
|
| class HardwareAbstractionLayer:
|
| def __init__(self):
|
| self.chips: Dict[int, GPUChip] = {}
|
| self.optical_links: Dict[str, OpticalInterconnect] = {}
|
|
|
| def get_chip(self, chip_id: int) -> GPUChip:
|
| """Get or create a GPU chip"""
|
| if chip_id not in self.chips:
|
| self.chips[chip_id] = GPUChip(chip_id)
|
| return self.chips[chip_id]
|
|
|
| def connect_chips(self, chip_id_a: int, chip_id_b: int, bandwidth_tbps: float = 800, latency_ns: float = 1) -> None:
|
| """Connect two chips with an optical link"""
|
| chip_a = self.get_chip(chip_id_a)
|
| chip_b = self.get_chip(chip_id_b)
|
|
|
| link_id = f"link_{chip_id_a}_{chip_id_b}"
|
| if link_id not in self.optical_links:
|
| self.optical_links[link_id] = OpticalInterconnect(bandwidth_tbps, latency_ns)
|
|
|
| chip_a.connect_chip(chip_b, self.optical_links[link_id])
|
| chip_b.connect_chip(chip_a, self.optical_links[link_id])
|
|
|
| def execute_tensor_core_matmul(self, chip_id: int, sm_id: int, A: np.ndarray, B: np.ndarray) -> Optional[np.ndarray]:
|
| """Execute matrix multiplication on tensor core"""
|
| chip = self.get_chip(chip_id)
|
| if sm_id >= len(chip.sms):
|
| return None
|
| return chip.sms[sm_id].tensor_core_matmul(A, B)
|
|
|
| def v2_vertex_shader(self, chip_id: int, vertex_data: List[float], shader_program: Dict[str, Any]) -> List[float]:
|
| """
|
| Run vertex shader using provided instructions.
|
| Supports AI/ML ops: matmul, activation, softmax, etc.
|
| """
|
| chip = self.get_chip(chip_id)
|
| if not chip.sms:
|
| return vertex_data
|
|
|
| sm = chip.sms[0]
|
| registers = list(vertex_data)
|
|
|
| for instr in shader_program.get('instructions', []):
|
| op = instr.get('opcode')
|
| args = instr.get('args', [])
|
|
|
| if op == 'load_vertex_data':
|
| continue
|
| elif op == 'transform_vertex':
|
| registers = [v * 2 for v in registers]
|
| elif op == 'matmul':
|
| A = args[0] if args else [[v] for v in registers]
|
| B = args[1] if len(args) > 1 else [[1.0] * len(registers)]
|
| result = sm.tensor_core_matmul(np.array(A), np.array(B))
|
| if result is not None:
|
| registers = result.flatten().tolist()
|
| elif op == 'activation':
|
| registers = [max(0, v) for v in registers]
|
| elif op == 'softmax':
|
| import math
|
| exp_vals = [math.exp(v) for v in registers]
|
| s = sum(exp_vals)
|
| registers = [v / s for v in exp_vals]
|
|
|
| return registers
|
|
|
| def v2_fragment_shader(self, chip_id: int, fragment_data: Dict[str, Any],
|
| shader_program: Dict[str, Any]) -> Tuple[float, float, float, float]:
|
| """
|
| Run fragment shader using provided instructions.
|
| Supports AI/ML ops: matmul, activation, softmax, etc.
|
| """
|
| chip = self.get_chip(chip_id)
|
| if not chip.sms:
|
| return (1.0, 1.0, 1.0, 1.0)
|
|
|
| sm = chip.sms[0]
|
| color = [1.0, 1.0, 1.0, 1.0]
|
|
|
| for instr in shader_program.get('instructions', []):
|
| op = instr.get('opcode')
|
| args = instr.get('args', [])
|
|
|
| if op == 'load_fragment_data':
|
| continue
|
| elif op == 'compute_color':
|
| x = fragment_data.get('x', 0)
|
| y = fragment_data.get('y', 0)
|
| color = [x % 256 / 255.0, y % 256 / 255.0, 0.5, 1.0]
|
| elif op == 'matmul':
|
| A = args[0] if args else [[c] for c in color]
|
| B = args[1] if len(args) > 1 else [[1.0] * len(color)]
|
| result = sm.tensor_core_matmul(np.array(A), np.array(B))
|
| if result is not None:
|
| color = result.flatten().tolist()
|
| elif op == 'activation':
|
| color = [max(0, v) for v in color]
|
| elif op == 'softmax':
|
| import math
|
| exp_vals = [math.exp(v) for v in color]
|
| s = sum(exp_vals)
|
| color = [v / s for v in exp_vals]
|
|
|
| return tuple(color[:4])
|
|
|
| def allocate_vram(self, chip_id: int, size_bytes: int) -> Optional[str]:
|
| """Allocate VRAM on specified chip"""
|
| chip = self.get_chip(chip_id)
|
| return chip.allocate_memory(size_bytes)
|
|
|
| def transfer_data(self, src_chip_id: int, dst_chip_id: int, size_bytes: int) -> float:
|
| """Transfer data between chips, returns transfer time"""
|
| src_chip = self.get_chip(src_chip_id)
|
| dst_chip = self.get_chip(dst_chip_id)
|
| return src_chip.transfer_data(dst_chip, size_bytes)
|
|
|
