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import torch
import torch.nn as nn
import torch.fx as fx
import numpy as np
import os
from typing import List, Dict
class XOR_MLP(nn.Module):
def __init__(self, hidden_dim=4):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
def forward(self, x):
return self.layers(x)
class NameGen:
def __init__(self):
self.counter = 0
def next(self) -> str:
name = f"v{self.counter}"
self.counter += 1
return name
def get_rules() -> str:
rules_path = os.path.join(os.path.dirname(__file__), "rules.in")
if not os.path.exists(rules_path):
return "// Rules not found in rules.in\n"
rules_lines = []
with open(rules_path, "r") as f:
for line in f:
if "// Net testing" in line:
break
rules_lines.append(line)
return "".join(rules_lines)
def export_to_inpla_wiring(model: nn.Module, input_shape: tuple) -> str:
traced = fx.symbolic_trace(model)
name_gen = NameGen()
script: List[str] = []
wire_map: Dict[str, List[str]] = {}
for node in traced.graph.nodes:
if node.op == 'placeholder':
num_inputs = int(np.prod(input_shape))
wire_map[node.name] = [f"Linear(Symbolic(X_{i}), 1.0, 0.0)" for i in range(num_inputs)]
elif node.op == 'call_module':
target_str = str(node.target)
module = dict(model.named_modules())[target_str]
input_node = node.args[0]
if not isinstance(input_node, fx.Node):
continue
input_wires = wire_map[input_node.name]
if isinstance(module, nn.Flatten):
wire_map[node.name] = input_wires
elif isinstance(module, nn.Linear):
W = (module.weight.data.detach().cpu().numpy()).astype(float)
B = (module.bias.data.detach().cpu().numpy()).astype(float)
out_dim, in_dim = W.shape
neuron_wires = [f"Concrete({B[j]})" for j in range(out_dim)]
for i in range(in_dim):
in_term = input_wires[i]
if out_dim == 1:
weight = float(W[0, i])
if weight == 0:
script.append(f"Eraser ~ {in_term};")
elif weight == 1:
new_s = name_gen.next()
script.append(f"Add({new_s}, {in_term}) ~ {neuron_wires[0]};")
neuron_wires[0] = new_s
else:
mul_out = name_gen.next()
new_s = name_gen.next()
script.append(f"Mul({mul_out}, Concrete({weight})) ~ {in_term};")
script.append(f"Add({new_s}, {mul_out}) ~ {neuron_wires[0]};")
neuron_wires[0] = new_s
else:
branch_wires = [name_gen.next() for _ in range(out_dim)]
def nest_dups(names: List[str]) -> str:
if len(names) == 1: return names[0]
if len(names) == 2: return f"Dup({names[0]}, {names[1]})"
return f"Dup({names[0]}, {nest_dups(names[1:])})"
script.append(f"{nest_dups(branch_wires)} ~ {in_term};")
for j in range(out_dim):
weight = float(W[j, i])
if weight == 0:
script.append(f"Eraser ~ {branch_wires[j]};")
elif weight == 1:
new_s = name_gen.next()
script.append(f"Add({new_s}, {branch_wires[j]}) ~ {neuron_wires[j]};")
neuron_wires[j] = new_s
else:
mul_out = name_gen.next()
new_s = name_gen.next()
script.append(f"Mul({mul_out}, Concrete({weight})) ~ {branch_wires[j]};")
script.append(f"Add({new_s}, {mul_out}) ~ {neuron_wires[j]};")
neuron_wires[j] = new_s
wire_map[node.name] = neuron_wires
elif isinstance(module, nn.ReLU):
output_wires = []
for i, w in enumerate(input_wires):
r_out = name_gen.next()
script.append(f"ReLU({r_out}) ~ {w};")
output_wires.append(r_out)
wire_map[node.name] = output_wires
elif node.op == 'output':
output_node = node.args[0]
if isinstance(output_node, fx.Node):
final_wires = wire_map[output_node.name]
for i, w in enumerate(final_wires):
res_name = f"result{i}"
script.append(f"Materialize({res_name}) ~ {w};")
script.append(f"{res_name};")
return "\n".join(script)
def train_model(name: str):
X = torch.tensor([[0,0], [0,1], [1,0], [1,1]], dtype=torch.float32)
Y = torch.tensor([[0], [1], [1], [0]], dtype=torch.float32)
net = XOR_MLP()
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
print(f"Training {name}...")
for epoch in range(1000):
optimizer.zero_grad()
out = net(X)
loss = loss_fn(out, Y)
loss.backward()
optimizer.step()
if (epoch+1) % 100 == 0:
print(f" Epoch {epoch+1}, Loss: {loss.item():.4f}")
return net
if __name__ == "__main__":
# Train two different models
net_a = train_model("Network A")
net_b = train_model("Network B")
print("\nExporting both to xor.in...")
rules = get_rules()
wiring_a = export_to_inpla_wiring(net_a, (2,))
wiring_b = export_to_inpla_wiring(net_b, (2,))
with open("xor.in", "w") as f:
f.write(rules)
f.write("\n\n// Network A\n")
f.write(wiring_a)
f.write("\nfree ifce;\n")
f.write("\n\n// Network B\n")
f.write(wiring_b)
print("Done. Now run: inpla -f xor.in | python3 prover.py")
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