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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] # Use first SM for shader execution
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] # ReLU
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] # Use first SM for shader execution
color = [1.0, 1.0, 1.0, 1.0] # Default white
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] # ReLU
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]) # Ensure RGBA output
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)
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