eml-cost-torch 0.1.0a0

PyTorch profiler plugin for Pfaffian depth/width per layer.

eml-cost-torch

Pre-release. Patent pending. Not for redistribution.

PyTorch profiler plugin: per-layer Pfaffian profile of a torch.nn.Module.

Status

Pre-release. Covered by Monogate Research patents #11 and #12.

Quick start

import torch.nn as nn
from eml_cost_torch import profile

model = nn.Sequential(
    nn.Linear(128, 64),
    nn.ReLU(),
    nn.Linear(64, 32),
    nn.GELU(),
    nn.Linear(32, 10),
)

p = profile(model)

print(p.total_layers)                  # 5
print(p.total_pfaffian_depth)          # 0 — all r=0 (ReLU=0, GELU=non-EML, Linear=0)
print(p.transcendental_layer_count)    # 0
print(p.non_eml_layer_count)           # 1 — GELU uses erf
print(p.estimated_pfaffian_width)      # 0 — no softmax/attention

for layer in p.layers:
    print(f"  {layer.name:8s}  {layer.activation:30s}  r={layer.pfaffian_r}")

What it does

Walks the module graph statically (does NOT execute the model), classifies each layer against an internal registry of activation/operator types, and returns a structured profile.

Registry covers ~50 standard torch.nn modules:

• Linear / Conv / Norm: r=0 (polynomial)
• ReLU family / Hard sigmoid / Hard swish: r=0
• Sigmoid, Tanh, Softplus, ELU, SiLU/Swish: r=1
• Mish: r=3
• GELU: flagged is_pfaffian_not_eml=True (uses erf, outside EML class)
• Softmax / MultiheadAttention: contributes to estimated_pfaffian_width

Why this matters for architecture search

Per the research validating patent #11: NN training cost correlates with Pfaffian width. This profile gives a static input you can use as a search heuristic before training.

Library API

from eml_cost_torch import profile, ModelProfile, LayerProfile

p: ModelProfile = profile(model)

# Aggregate fields
p.layers                       # list[LayerProfile]
p.total_layers                 # int
p.total_pfaffian_depth         # sum of r over all layers
p.total_eml_depth              # sum of depth
p.transcendental_layer_count   # count of layers with r >= 1
p.non_eml_layer_count          # count of layers using non-EML primitives (e.g., GELU)
p.estimated_pfaffian_width     # parallel-chain count (softmax + attention)
p.total_params                 # parameter count

# Per-layer fields
layer.name                  # named_modules path
layer.cls_name              # Python class name
layer.activation            # friendly description
layer.pfaffian_r            # chain order
layer.eml_depth             # routing depth
layer.is_pfaffian_not_eml   # True for GELU and similar
layer.n_params              # parameters at this layer

License

PROPRIETARY-PRE-RELEASE. See LICENSE.