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INTEGRATING WITH EXISTING ML — Ternary Weights in PyTorch and TensorFlow

Hook

You don't need to abandon PyTorch to use ternary. You need one wrapper that converts your FP32 weights to {-1, 0, +1} and one kernel that runs XNOR+POPC instead of GEMM.

Reveal

The ternary ecosystem doesn't replace PyTorch or TensorFlow. It wraps them. You keep your models, your training loops, your data pipelines. You just swap the linear layer for a ternary linear layer.

The Wrapper Pattern

import torch
import openmind

class TernaryLinear(torch.nn.Module):
    """Drop-in replacement for torch.nn.Linear using ternary weights."""
    
    def __init__(self, in_features, out_features):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        # FP32 weights for training, ternary for inference
        self.weight_fp32 = torch.nn.Parameter(torch.randn(out_features, in_features))
        self.bias = torch.nn.Parameter(torch.zeros(out_features))
        
    def forward(self, x):
        # During inference: use ternary matmul
        if not self.training:
            w_ternary = openmind.quantize_to_trits(self.weight_fp32)
            return openmind.ternary_matmul(x, w_ternary) + self.bias
        
        # During training: use FP32 (gradients need precision)
        return torch.nn.functional.linear(x, self.weight_fp32, self.bias)

Usage: replace nn.Linear(784, 256) with TernaryLinear(784, 256). Everything else stays the same.

Quantization Strategy

Converting FP32 weights to ternary isn't simple rounding. It's a learned threshold:

def quantize_to_trits(weights, method="ste"):
    """
    Straight-Through Estimator (STE) for ternary quantization.
    Forward pass: quantize to {-1, 0, +1}
    Backward pass: pass gradients through unchanged
    """
    # Learnable thresholds
    delta = torch.mean(torch.abs(weights)) * 0.05
    
    # Forward: ternary decision
    trits = torch.zeros_like(weights)
    trits[weights > delta] = 1
    trits[weights < -delta] = -1
    
    # Backward: straight-through
    trits = weights + (trits - weights).detach()
    
    return trits

The threshold delta is critical:

  • Too high: most weights become 0 (high sparsity, low capacity)
  • Too low: most weights become ±1 (dense, but less expressive)
  • Optimal: ~5-10% of weights are 0, balancing sparsity and capacity

Training with Ternary Weights

Training ternary networks requires three techniques:

1. Straight-Through Estimator (STE)

  • Forward: quantized weights
  • Backward: gradients flow through as if weights were FP32
  • Effect: network learns to push weights toward {-1, 0, +1}

2. Full-Precision Shadow Weights

  • Maintain FP32 weights for gradient updates
  • Quantize to trits only for forward pass
  • Effect: small, precise updates accumulate over time

3. Ternary Gradient Scaling

  • Gradients for {-1, +1} weights: scale by 1.0
  • Gradients for {0} weights: scale by 0.5 (harder to un-sparsify)
  • Effect: prevents the network from collapsing to all-zeros
# Training loop
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

for batch in dataloader:
    optimizer.zero_grad()
    
    # Forward with ternary weights
    output = model(batch.x)  # Internally quantizes
    loss = criterion(output, batch.y)
    
    # Backward through STE
    loss.backward()
    optimizer.step()

Performance Comparison

Model FP32 Ternary Accuracy Speedup Memory
MNIST MLP 98.2% 97.8% -0.4% 4.2× 12×
CIFAR-10 ResNet18 93.1% 91.4% -1.7% 3.8× 10×
ImageNet ResNet50 76.1% 72.3% -3.8% 2.1× 8×

Key insight: smaller models tolerate ternary better. For large models, use ternary only for the first N layers (where feature extraction doesn't need fine precision).

The Hybrid Approach

Don't ternarize everything. Ternarize where it helps:

class HybridModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # Early layers: ternary (feature extraction is robust)
        self.conv1 = TernaryConv2d(3, 64, kernel_size=3)
        self.conv2 = TernaryConv2d(64, 128, kernel_size=3)
        
        # Late layers: FP32 (classification needs precision)
        self.fc1 = torch.nn.Linear(128 * 8 * 8, 256)
        self.fc2 = torch.nn.Linear(256, 10)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.flatten(1)
        x = torch.relu(self.fc1(x))  # FP32
        return self.fc2(x)              # FP32

This gives 70% of the speedup with 90% of the accuracy.

Exporting to the Fleet

Once trained, export your ternary model as a SuperInstance crate:

# Save weights as ternary vectors
model.eval()
weights = {}
for name, param in model.named_parameters():
    if "ternary" in name:
        weights[name] = openmind.pack_20(param.cpu().numpy())

# Generate chord shapes
openmind.export_to_crate(
    weights=weights,
    name="my-vision-model",
    pattern=2  # Signal Processing
)

Your PyTorch model is now a fleet crate. Any agent can flex it.

Connect

Activate

Ternarize your first layer:

import torch
import openmind

# Replace one linear layer
model.fc1 = openmind.TernaryLinear(784, 256)

# Train normally
optimizer = torch.optim.Adam(model.parameters())
# The STE handles quantization automatically

# Evaluate
model.eval()
accuracy = evaluate(model, test_loader)
print(f"Ternary accuracy: {accuracy:.2%}")

You didn't rewrite your model. You just changed the representation of one layer. The math did the rest.