T1C-Bridge - PyTorch Bridge for T1C-IR
PyTorch helpers for the T1C-IR (Type 1 Compute Intermediate Representation). This package enables PyTorch-based libraries to translate models to and from T1C-IR, providing export, import, and execution capabilities for neuromorphic neural networks.
T1C-Bridge serves as the bridge between PyTorch models and T1C-IR graphs, enabling seamless model portability and deployment to Type 1 Compute hardware.
Read more about T1C-IR in our documentation
Installation
uv add t1c-bridge@git+ssh://git@github.com:type1compute/t1cir.git
Or with pip:
pip install t1c-bridge
Usage
T1C-Bridge provides two main functions to_ir and ir_to_torch that map PyTorch models to T1C-IR graphs and vice versa.
Translating from PyTorch to T1C-IR
T1C-Bridge automatically traces through your PyTorch model and converts each module to the corresponding T1C-IR primitive. You can provide custom converters for unsupported modules via the custom_map argument.
import torch
import torch.nn as nn
from t1c import bridge, ir
# Define your model
model = nn.Sequential(
nn.Linear(784, 128),
nn.Linear(128, 10)
)
# Export to T1C-IR
graph = bridge.to_ir(model, torch.randn(1, 784))
# Save to file
ir.write("model.t1c", graph)
For custom modules, provide a mapping dictionary:
# Define custom PyTorch -> T1C-IR mapping
from t1c import bridge, ir
custom_map = {}
def _extract_custom_module(module: MyCustomModule) -> ir.Node:
# Convert your custom module to a T1C-IR node
return ir.Affine(weight=module.weight, bias=module.bias, ...)
custom_map[MyCustomModule] = _extract_custom_module
# Export with custom mapping
graph = bridge.to_ir(model, sample, custom_map=custom_map)
Translating from T1C-IR to PyTorch
T1C-Bridge automatically creates PyTorch modules from T1C-IR nodes. The ir_to_torch function combines import with execution, returning an executable GraphExecutor module.
from t1c import bridge
import torch
# Load and create executable module
executor = bridge.ir_to_torch("model.t1c")
# Execute
output = executor(input_tensor)
For stateful models (SNNs), use return_state=True:
# Load with state management
executor = bridge.ir_to_torch("model.t1c", return_state=True)
# Execute with state
state = None
for t in range(100):
output, state = executor(input_tensor, state)
You can also provide custom node converters:
from t1c import bridge, ir
def custom_node_map(node: ir.Node) -> Optional[torch.nn.Module]:
if isinstance(node, MyCustomNode):
return MyCustomModule(...)
return None # Falls back to default converter
executor = bridge.ir_to_torch(graph, node_map=custom_node_map)
Quick Start
Basic Export and Execute
import torch
import torch.nn as nn
from t1c import bridge, ir
# Your model
model = nn.Sequential(nn.Linear(784, 128), nn.Linear(128, 10))
sample = torch.randn(1, 784)
# Export to T1C-IR
graph = bridge.to_ir(model, sample)
# Save to file
ir.write("model.t1c", graph)
# Load and execute
executor = bridge.ir_to_torch("model.t1c")
output = executor(sample)
Spiking Neural Network
import torch
import torch.nn as nn
import snntorch as snn
from t1c import bridge
# Define SNN
model = nn.Sequential(
nn.Linear(784, 128),
snn.Leaky(beta=0.9, init_hidden=True, output=True),
nn.Linear(128, 10),
snn.Leaky(beta=0.9, init_hidden=True, output=True),
)
# Export
graph = bridge.to_ir(model, torch.randn(1, 784))
# Execute with state
executor = bridge.ir_to_torch(graph, return_state=True)
state = None
for t in range(100):
output, state = executor(input_tensor, state)
Supported Modules
T1C-Bridge automatically converts the following PyTorch modules to T1C-IR:
| PyTorch Module | T1C-IR Node | Notes |
|---|---|---|
nn.Linear |
Affine or SpikingAffine |
Use spiking=True for hardware-optimized version |
nn.Conv2d |
Conv2d |
Full 2D convolution support |
nn.Flatten |
Flatten |
Tensor flattening |
nn.MaxPool2d |
MaxPool2d |
2D max pooling |
nn.Identity |
Skip |
Skip/residual connections |
snn.Leaky |
LIF |
snnTorch LIF neuron |
For Hardware Teams
T1C-Bridge is for validation only. The execution runtime is a PyTorch simulation of what the hardware will do. Use it to verify your IR graph produces correct outputs before deploying to actual hardware.
The GraphExecutor handles:
- Topological sorting of graph nodes
- State management for stateful modules (LIF neurons)
- Skip connections and residual paths
- Multiple input/output handling
Development
From the repo root, install in editable mode (requires t1c-ir installed first):
uv pip install -e .
# or: pip install -e .
pytest tests/ -v
If you see AttributeError: module 't1c.bridge' has no attribute 'to_ir', you are likely using an old install. Uninstall any legacy t1ctorch and reinstall this package in editable mode:
pip uninstall t1ctorch t1c-bridge 2>/dev/null; uv pip install -e .
Documentation
For more information, visit the T1C-IR documentation.
1 maintainer
Metadata
1 maintainer
Release files for t1c-bridge 0.0.1
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Attestation for t1c_bridge-0.0.1-cp313-cp313-win_amd64.whl
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Signed by GitHub Actions, verified by PyPI on Feb 5, 2026.
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