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